Morphological Stasis and Genetic Divergence Without Reproductive Isolation in the Rhinichthys Cataractae Species Complex: Insigh

Morphological stasis and genetic divergence without reproductive isolation in the Rhinichthys cataractae species complex: insights from a zone of secondary contact in the lower Fraser Valley, British Columbia

Jennifer Anne Ruskey

B.Sc., Princeton University, 2007

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

The Faculty of Graduate and Postdoctoral Studies

(Zoology)

THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2014 c Jennifer Anne Ruskey 2014 Abstract

The Nooksack dace (Rhinichthys cataractae putative subspecies; NSD) and longnose dace (R. cataractae; LND) form a zone of post-glacial secondary contact in three streams in British Columbia’s lower Fraser River valley, providing a valuable opportunity to study contact between populations separated during the Pleistocene glaciations. They are morphologically cryptic, despite an estimated 2-3 million years of separation. The NSD is currently listed as Endangered under Canada’s Species at Risk Act (SARA) and my study clarifies its taxonomic and conservation status. NSD and LND have highly divergent mitochondrial DNA types, and dace carrying each mtDNA type have been found in roughly equal numbers in the zone of secondary contact. However, it was unknown whether this represented ongoing hybridization or reproductive isolation in sympatry. I conducted a morphological analysis using 11 morphometric measurements and two meristic characters (N = 582, 23 sampling locations) to uncover any subtle variation between the two dace, as well as to test for morphological intermediacy in the zone of sympatry. I then employed a 10–locus microsatellite DNA assay (N = 374, 12 sampling locations) to test for introgression between LND and NSD in the zone of secondary contact. I found that the two dace could not be reliably distinguished: there was overlap in all morphological characters measured, and both morphological and microsatellite analyses showed a greater effect of location than mtDNA clade, even when restricted to allopatric populations. There was no evidence of population structure within the sympatric populations, indicating complete admixture. The LND and NSD provide an example of “ephemeral speciation”—two lineages which, despite long separation, have developed no apparent barriers to reproduction and have collapsed into a single interbreeding population where they come into secondary contact. The zone of secondary contact should ideally be conserved for its evolutionary significance, and is a good illustration of the complicated patterns of diversification caused by the Pleistocene glaciations. However, while the NSD should be protected as a distinct designatable unit, it should not be considered a separate species, or even subspecies.

ii Preface

Seven of the microsatellite markers used in my study were developed specif- ically for this study and for further work on the Rhinichthys cataractae species complex. Work leading to the development of 25 microsatellite markers for R. cataractae that distinguished the Nooksack and longnose dace was conducted at Savannah River Ecology Laboratory by R. Beasley and Dr. S. L. Lance, with samples contributed by Dr. E. B. Taylor and myself. These markers have since been published as Beasley et al. (2014).

iii Table of Contents

Abstract ...... ii

Preface ...... iii

Table of Contents ...... iv

List of Tables ...... vii

List of Figures ...... ix

Acknowledgements ...... xi

Dedication ...... xiii

1 Introduction ...... 1 1.1 Background ...... 1 1.2 The Rhinichthys cataractae species complex ...... 3 1.3 Secondary contact between the Nooksack dace and longnose dace ...... 6 1.4 Cryptic species ...... 9 1.5 Conservation status of the Nooksack dace ...... 10 1.6 Research objectives ...... 11

2 Morphometric Analysis of the Nooksack and Longnose dace (Cyprinidae: Rhinichthys) ...... 12 2.1 Introduction ...... 12 2.2 Materials and methods ...... 14 2.2.1 Sampling ...... 14 2.2.2 Morphometric and meristic measurements ...... 15 2.2.3 Statistical analysis of morphological data ...... 18 2.3 Results ...... 22 2.3.1 Searching for new dace populations ...... 22 2.3.2 Variation in vertebral counts ...... 22

iv Table of Contents

2.3.3 Principal components analysis ...... 23 2.4 Discussion ...... 41 2.4.1 Subtle morphological variation ...... 42 2.4.2 Alternative explanations for morphological variation . 44 2.4.3 Implications ...... 46

3 Genetic Analysis of Nooksack and Longnose Dace ..... 48 3.1 Introduction ...... 48 3.1.1 Background ...... 48 3.1.2 Genetic techniques ...... 50 3.1.3 Objectives for this chapter ...... 51 3.2 Materials and methods ...... 52 3.2.1 Sampling ...... 52 3.2.2 DNA extraction and ampliﬁcation ...... 52 3.2.3 Genetic analyses—mitochondrial DNA ...... 53 3.2.4 Genetic analyses—microsatellite data ...... 54 3.2.5 Admixture analyses (Structure)...... 55 3.2.6 Analysis of molecular variance (AMOVA) ...... 56 3.3 Results ...... 58 3.3.1 Distribution of mitochondrial haplotypes ...... 58 3.3.2 Microsatellite analyses ...... 58 3.3.3 Admixture analysis ...... 63 3.3.4 AMOVA analysis ...... 71 3.4 Discussion ...... 73 3.4.1 Similarity of sympatric R. cataractae to allopatric parental lineages ...... 74 3.4.2 Admixture of LND and NSD in sympatric populations 75 3.4.3 Genetic diﬀerentiation throughout the range of R. cataractae ...... 76 3.4.4 Implications for the taxonomic and conservation status of the Nooksack dace ...... 78

4 Conclusion ...... 79 4.1 Summary of ﬁndings ...... 79 4.2 Conservation implications ...... 80 4.3 “Ephemeral” speciation ...... 82

Bibliography ...... 83

v Table of Contents

Appendices

A Chapter 2 ...... 99

B Chapter 3 ...... 108

vi List of Tables

2.1 Description of morphometric and meristic measurements . . 16 2.2 Sampling effort and results in streams previously un-surveyed for dace ...... 23 2.3 Eigenvalues and percentage of variance explained by each axis of a principal components analysis conducted on allopatric populations of dace ...... 26 2.4 Character loadings for the first five axes of the principal components analysis conducted on allopatric populations of dace 28 2.5 r2 values, F-ratios and P-values for nested ANOVAs conducted on the first five principal components of morphological samples from allopatric populations of Rhinichthys ...... 28 2.6 r2 values, F-ratios and P-values for nested ANOVAs conducted on the first five principal components of morphological samples from all populations of Rhinichthys ...... 29 2.7 F-ratios and P-values for ANOVAs conducted on the first five principal components of samples from Kanaka Creek . . . . . 30 2.8 Results for linear regressions comparing morphological variables and environmental variables ...... 39 2.9 F-ratios and P-values for ANOVAs comparing Rhinichthys samples from east and west of the Rocky Mountains . . . . . 41 2.10 F-ratios and P-values for ANOVAs comparing LND samples from east and west of the Rocky Mountains ...... 41

3.1 Populations used in Structure analysis ...... 56 3.2 Summary of genetic data for the 12 locations used in microsatellite analysis ...... 61 3.3 Evanno table output from Structure Harvester for Struc- ture analysis conducted on all 12 dace populations . . . . . 64 3.4 Evanno table outputs from Structure Harvester for Struc- ture analysis conducted on Kanaka Creek, Coquitlam River, and Alouette River ...... 70

vii List of Tables

3.5 AMOVA results comparing diﬀerent groupings of allopatric and sympatric populations ...... 72

A.1 List of sampling locations for Nooksack and longnose dace used in morphological analyses ...... 99 A.2 Raw data of vertebral counts from allopatric dace populations 103 A.3 Eigenvalues and percentage of variance explained by each axis of a principal components analysis conducted on all populations of dace ...... 104 A.4 Character loadings for the ﬁrst ﬁve axes of the principal components analysis conducted on all populations of dace . . . . 105 A.5 Bayesian information criterion (BIC) values for each model and number of clusters for the cluster analysis of principal component scores from morphological analysis of samples from all populations ...... 106 A.6 Bayesian information criterion (BIC) values for each model and number of clusters for the cluster analysis of principal component scores from morphological analysis of samples from allopatric populations ...... 107

B.1 List of sampling locations for Nooksack and longnose dace (Rhinichthys cataractae) used in genetic analyses ...... 108 B.2 Details for 10 polymorphic microsatellite loci developed for Rhinichthys cataractae ...... 110 B.3 Observed (HE) and expected (HO) heterozygosities for 10 microsatellite loci and 12 populations of Rhinichthys cataractae111 B.4 Allele frequencies for 10 microsatellite loci at 12 sampling locations ...... 113 B.5 Pairwise FST values for all sampling locations ...... 122 B.6 Pairwise FST for all sampling locations, with each sympatric population divided by mtDNA haplotype ...... 123

viii List of Figures

1.1 Maximum extent of Pleistocene ice sheets in northwestern North America ...... 4 1.2 Distribution of Nooksack dace (Rhinichthys cataractae putative subspecies) in Canada and North America ...... 8

2.1 Micro CT scan of longnose dace sample ...... 17 2.2 Histogram showing distribution of vertebral counts for longnose and Nooksack dace ...... 24 2.3 Variables factor map for the principal components analysis of size-transformed morphometric characters for all samples . . 27 2.4 Histogram showing lateral line scale count by mtDNA haplotype for allopatric Rhinichthys samples ...... 31 2.5 Histogram showing lateral line scale count by mtDNA haplotype for Kanaka Creek samples ...... 32 2.6 Scatter plot of dace morphological samples on the ﬁrst two principal components, with convex hulls enclosing each mclust- assigned cluster ...... 34 2.7 Stacked bar chart showing the composition of each cluster generated by the mclust analysis on all Rhinichthys samples . 35 2.8 Stacked bar chart showing the composition of each cluster generated by the mclust analysis on only allopatric Rhinichthys samples ...... 36 2.9 Stacked bar chart showing the composition of each cluster generated by the mclust analysis conducted on Rhinichthys from Kanaka Creek ...... 37

3.1 Map of Nooksack and longnose dace cytochrome b mtDNA haplotypes ...... 59 3.2 Factorial correspondence analysis plot based on 10 microsatellite loci for Rhinichthys cataractae ...... 62 3.3 Structure admixture analysis of Nooksack and longnose dace from all populations, K = 11 ...... 66

ix List of Figures

3.4 Structure admixture analysis of Nooksack and longnose dace from all populations, K = 3 ...... 67 3.5 Structure admixture analysis of samples from Coquitlam River, K = 3 ...... 68 3.6 Structure admixture analysis of samples from Alouette River, K=3 ...... 69

x Acknowledgements

I would first like to thank my supervisor, Dr. Eric Taylor, who has been a patient and inspiring source of guidance throughout my degree. I exited every meeting with you feeling like my work was more interesting and, cru- cially, more possible than I had going in! Thank you for helping me to figure out what doing science is all about. Your example as a rigorous researcher, conservation scientist and advocate, thoughtful (and occasionally enraged) Canadian citizen, gregarious community member, and full-on ichthyophile has been inspiring from start to finish. My committee members, Darren Irwin and Peter Arcese, offered invaluable insight and suggestions which broadened my perspective on my work and occasionally flipped my line of reasoning upside down. I only wish I had taken greater advantage of your advice. I owe a huge thank-you to Rebecca Seifert for her lab and field assistance. Thank you to Shannan McNally and Monica Yau for being my lab work teachers—I started out never having touched a pipette, and these two ladies made me a competent and enthusiastic lab rat. Thank you to Mike Pearson, Stephanie Avery–Gomm and Mike Champion for conversations about Nook- sack dace, and Don McPhail for laying the vast and invaluable groundwork that underlies this thesis. Thank you to John Schipilow and Nancy Ford at the UBC Center for High-Throughput Phenogenomics for helping with my micro-CT scanning. Thank you to the members of the Taylor and Rosenfeld labs for your helpful ideas, discussion and support: Shannan McNally, Amanda Moreira, Monica Yau, Mike Champion, Sean Naman, Rebecca Piercey, Jill Miners, Katie Haman, Sarah Davidson, Carla Crossman, Matthew Siegle. When I was accepted to the UBC Zoology department, I had no idea just how lucky I was to become a part of this incredible community. Aca- demically and socially, this is a wonderful place to be: rigorous, creative, collaborative, friendly, and with a commitment to science’s broader social responsibilities. The faculty, students, administrators, Beaty Museum staff, and incomparable IT department have all made these three years a great experience. Too many names to list, but I would like to especially thank

xi Acknowledgements those involved in the Huts Skit, as well as my cohort from ZOOL502. I would also like to thank Rick Taylor, again, for giving me the opportunity to serve as the teaching assistant for BIOL 465; both of my semesters as Deputy Ruskey were an absolute privilege and pleasure. It’s no secret that this thesis has been extremely difficult for me to complete, and I considered quitting many times. Without the tremendous amount of help and support I received over the past three years I would never have been able to finish. I am deeply indebted to my friends and family, as well as to two wise and compassionate counsellors who helped me to repair my mental health and learn an entirely new set of skills. I think that the psychological struggles of graduate work are often swept under the rug, despite their ubiquity, and I would like to acknowledge the people who were particularly supportive to me during difficult times: Caitlin Kopperson, Jay Morritt, Daniel Wood, Kiri Sawyer, Ashley Waring, Andrew MacDon- ald, Chris Luft, Leanne Fessler, Laurie Kohl, Saskia Wolsak, Andrew Gillis, Gwylim Blackburn, Joel Heath, Albert Ruskey, Susan Ruskey, and Frank Ruskey. Funding for this project was provided by NSERC Discovery and Equip- ment grants, Canadian Wildlife Federation Endangered Species Fund grant, and the Department of Zoology Teaching Assistantship.

xii Dedication

This thesis is dedicated to my family: Mom, Dad, and Albert.

xiii Chapter 1

Introduction

1.1 Background

The origin of new species, in its simplest formulation, involves clear dichoto- mous branching of a single lineage into two distinct lineages with separate evolutionary trajectories. However, in reality the process of speciation is complex and can take many different paths, with much study and debate currently dedicated to the roles of selection, adaptation, genetic drift, and geographic isolation, among others (see Coyne and Orr, 2004). The path to becoming a new species is rarely straightforward; incipient species may remain in “limbo” for millions of years (e.g. Avise et al., 1998), may undergo parallel, reverse, or de-speciation (Turner, 2002; Taylor et al., 2006), and may diverge and re-merge, potentially multiple times, before ultimately becoming two stable species or collapsing back into one (Rosenblum et al., 2012). Episodes of recurrent vicariance and reconnection, such as those caused by the glacial cycles of the Pleistocene, can create complex patterns of divergence and incomplete speciation (e.g. Bernatchez and Wilson, 1998). Allopatric speciation occurs when geographic isolation disrupts gene flow and is followed by independent evolution of the separated populations, ultimately leading to reproductive isolation (Mayr, 1963). Divergence may be driven by selection, or stochastic processes like genetic drift (Dobzhansky and Dobzhansky, 1937). Molecular divergence of different populations can easily be measured and mtDNA phylogeography has become a popular tool for uncovering deep historical divergence between populations even when it may be accompanied by little morphological differentiation (e.g. Bond et al., 2001; Krosch et al., 2009). However, although ecological and morphological differences and standard levels of genetic divergence have been suggested as methods to determine whether allopatric lineages should be considered separate species (Thorpe, 1982; April et al., 2013), it is often difficult to reliably describe two groups as different species until they have been observed in sympatry. When a population has been separated, barriers to reproductive isolation may arise, or they may not. Some clades that differ very little in neutral molecular genetic traits may be strongly reproductively isolated (e.g.

1 1.1. Background some African cichlid fishes, Moran and Kornfield, 1993), whereas others that are deeply genetically diverged may still freely interbreed upon secondary contact (e.g. ravens, Webb et al., 2011). When two lineages that have diverged in allopatry reconnect in a zone of secondary contact, the outcome is uncertain and provides an excellent opportunity to examine the strength and causes of reproductive isolation (Barton and Hewitt, 1985; Barton and Hewitt, 1989). The possible outcomes fall along a spectrum: the two species may be completely reproductively isolated, in which case speciation can be considered complete (e.g. Pfenninger and Posada, 2002), or they may form hybrid zones of varying types, ranging from a bimodal hybrid zone with minimal interbreeding (e.g. Phillips et al., 2013) to a unimodal, fully introgressive hybrid swarm with nonexistent reproductive isolation (e.g. Avise and Saunders, 1984; Wiens et al., 2006). Fishes are widely considered an excellent group in which to study evolutionary questions and North American freshwater fishes in particular provide many good opportunities in which to study hybridization and zones of secondary contact (e.g. Hubbs, 1955; Knowlton, 1993; Bernardi, 2013). Many such zones of secondary contact formed in the wake of the Pleistocene glaciations, which dramatically reformed the North American landscape. Ice covered most of Canada, and flora and fauna survived only in ice-free glacial refuge areas, with previously continuous species distributions often divided into two or more refugia. The ice sheets reached their furthest extent ∼ 24, 000 − 21, 000 years ago and began to substantially recede ∼ 14, 000 years ago (Dyke et al., 2002); however, throughout the Pleistocene they followed climatic cycles and receded and advanced multiple times (Imbrie, 1985; Hewitt, 2004). In eastern North America, the two major refugia were the Mississippian and Atlantic refugia—the Mississipian encompassing the upper Mississippi River basin south of the ice sheet, and the Atlantic along the east coast south of Long Island and east of the Appalachians (Bailey and Smith, 1981). In western North America, the three major refugia were the Pacific refugium, the Missourian refugium and the Beringian refugium, with the Pacific refuge existing along the west coast south of Vancouver Island and west of the Rocky Mountains, the Missourian located further east in the Missouri River basin, and the Beringian refuge encompassing the northwestern corner of North America (see fig. 1.1) (Shafer et al., 2010). These large refugia contained additional structure in the form of smaller refugia- within-refugia, and there also exist cryptic refugia outside of the boundaries of the major ones, which phylogeography can help identify (Stewart and Lis- ter, 2001; Provan and Bennett, 2008; Shafer et al., 2010). The distribution of refugia and the repeated cycles of glaciation dictated the movement and

2 1.2. The Rhinichthys cataractae species complex survival of biota within North America, with particularly strong eﬀects on freshwater ﬁshes, leading to complex phylogeography and many opportunities to study recent or incomplete speciation (Lindsey and McPhail, 1986; Bernatchez and Wilson, 1998).

1.2 The Rhinichthys cataractae species complex

Cyprinids are the largest family of vertebrates and the most diverse freshwater fish family in North America (Dowling et al., 2002) and have been frequently used as study systems for evolutionary questions (Dawley and God- dard, 1988; Dowling and DeMarais, 1993). The genus Rhinichthys (or “riffle daces”) consists of eight living species that are endemic to North America where they are broadly distributed in coolwater habitats (Matthews et al., 1982). Rhinichthys are relatively small bodied (maximum size of about 120 mm), omnivorous fishes morphologically well-adapted to live on or near the bottom (see below). Several studies have investigated the systematics and biogeography of blacknose dace (R. atratulus, Matthews et al., 1982; Fraser et al., 2005; Smith and Dowling, 2008; Tipton et al., 2011) and speckled dace (R. osculus, Oakey et al., 2004; Pfrender et al., 2004; Smith and Dowling, 2008; Billman et al., 2010; Kinziger et al., 2011; Hoekzema and Sidlauskas, 2014). R. cataractae is the most widely distributed species in the genus, and indeed the most widely distributed native North American minnow, ranging from the Atlantic to the Pacific coasts, and from the Arctic Circle to northern Mexico (McPhail and Taylor, 2009). It is likely that the longnose dace survived in at least three refugia (Mississippian, Atlantic, and Pacific) during the Pleistocene glaciations (Scott and Crossman, 1998). It occupies varied habitats with differing geological histories and is potentially an excellent system for studying North American biogeography, and ecological and environmental issues from an evolutionary perspective (e.g. Girard and Angers, 2006). R. cataractae, the longnose dace, is a small benthic cyprinid that lives in shallow stream riffles with a loose gravel, cobble or boulder substrate. They prefer swift current and are morphologically adapted in a variety of ways for life at the bottom of fast-flowing streams: their body shape is streamlined; they have large paddle-like pectoral fins which can be used as hydrofoils to maintain their position in swift current; and they have very reduced swim bladders (Gee and Northcote, 1963). They have a long snout which overhangs their mouth, and can reach 106 mm in standard length, averaging 94 mm as adults. They feed primarily upon aquatic insect larvae.

3 1.2. The Rhinichthys cataractae species complex

Figure 1.1: Extent of ice during the last glacial maxima. Ice layer is from Dyke et al. (2002). Major refugia and ice-sheets are labeled; inset map shows the states and provinces of northwestern North America: in Canada, YT–Yukon Territory, NWT–Northwest Territory, BC–British Columbia, AB–Alberta. In the United States, WA–Washington, MT–Montana, OR– Oregon, ID–Idaho, WY–Wyoming. Figure from Shafer et al. (2010).

4 1.2. The Rhinichthys cataractae species complex

Their most common food depends upon what is available in their location, and can include chironomid, midge, blackfly, and mayfly larvae (Scott and Crossman, 1998; McPhail and Taylor, 2009). R. cataractae spawn in the early summer, with estimates of actual spawning time ranging from May to early July depending on the location (Scott and Crossman, 1998). There is little detailed information available about their mating and spawning activities, but it is thought that they spawn in riffles over a gravelly bottom, and that while they do not dig a nest, they form a territory and one parent guards the eggs. Females lay 200–2000 eggs, depending on the size of the female, which hatch in 7–10 days. The young dace are pelagic and live in calm waters near the shore, transitioning at approximately four months to their adult lifestyle in fast-moving water. Hybrids between R. cataractae and R. osculus and between R. cataractae and Nocomis micropogon have been recorded (Smith, 1973; Scott and Cross- man, 1998). Although R. cataractae has a wide range and occupies diverse habitats throughout a continent with a complex biogeographic history, there has been little population structure documented. By contrast, the blacknose dace, which occupies a small geographic range in eastern North America, has been studied using both genetics and morphology and some have suggested that it consists of a complex of three distinct species: southern, R. obtusus; eastern, R. atratulus; and western, R. meleagris (Fraser et al., 2005; Tipton et al., 2011). These different forms likely were isolated in separate glacial refugia, as were lineages of R. cataractae, yet no continent-wide investigation of population structure has been done in R. cataractae. Rather, work to date in the R. cataractae complex has focused on diversity arising from “refugia- within-refugia” (Shafer et al., 2010) in the Pacific Northwest (McPhail and Taylor, 2009). The Umpqua and Millicoma dace have been identified as distinct species within the R. cataractae species group, forming a distinct Oregon coastal clade (McPhail and Taylor, 2009), and the Nooksack dace, endemic to southwest British Columbia and northwest Washington State, has been suggested as a fourth clade (McPhail, 1967, Taylor et al., unpubl. data). The Nooksack dace forms part of the Chehalis fauna, a group of animals isolated in the Chehalis glacial refugium—south of Puget Sound and north of the Columbia River drainage—during the Pleistocene glaciations (McPhail, 1997). It is currently found in several rivers in northwestern Washington State and adjacent portions of British Columbia in tributaries of the upper Nooksack River and in several streams that drain to the lower Fraser River (Fig. 1.2). In Canada, the Nooksack dace is listed as Endangered under the

5 1.3. Secondary contact between the Nooksack dace and longnose dace federal Species at Risk Act (SARA). The Nooksack dace is morphologically similar to the longnose dace; it can only be reliably distinguished genetically, although differing lateral line scale counts have been suggested to differentiate the two types (McPhail, 1967). Mitochondrial DNA sequence analysis has shown that Nooksack and longnose dace form reciprocally monophyletic clades that differ from each other by about 3% (cytochrome b) to 4% (ND2) mitochondrial DNA sequence divergence, implying a separation of 2-3 mya (Taylor et al., unpubl, data).

1.3 Secondary contact between the Nooksack dace and longnose dace

Our current understanding suggests that when the last glaciers receded, the Nooksack dace dispersed northwards to recolonize several tributaries of the lower Fraser River in British Columbia, following postglacial lakes at the edge of the receding ice (Fig 1.2) (McPhail, 1967). Streams with fish bearing only Nooksack dace mtDNA exist in four streams in British Columbia: Pepin Creek, Fishtrap Creek, Bertrand Creek (all tributaries of the upper Nooksack River), and the Brunette River (a tributary of the lower Fraser River). Additionally, fish that bear Nooksack dace mtDNA and fish that bear longnose dace mtDNA are found in sympatry in three streams that drain to the north side of the Fraser River: the Coquitlam River, the Alouette River, and Kanaka Creek. These streams with sympatric Nooksack and longnose mtDNA represent a zone of postglacial secondary contact and an opportunity to study the degree of reproductive isolation between the two lineages. If reproductive isolation exists, the Nooksack and longnose dace may represent a case of cryptic species within Rhinichthys (see below and McPhail and Taylor, 2009). Currently there has been no study of the streams with sympatric Nook- sack and longnose dace than a simple survey of mtDNA types, and there are several possibilities for the situation underlying the observed pattern. My thesis is focused on evaluating three possible scenarios for the current status of Nooksack and longnose dace

1. Nooksack and longnose dace co-occur without interbreeding and be- have as distinct biological species

2. Nooksack and longnose dace interbred completely upon secondary contact with extensive introgression such that there is no evidence of current reproductive isolation between types and the occurrence of two

6 1.3. Secondary contact between the Nooksack dace and longnose dace

mtDNA types represents a vestige of past isolation.

3. Nooksack and longnose dace are incompletely reproductively isolated such that there is ongoing hybridization and the genetic structure of each stream consists of a bimodal hybrid zone; primarily parental forms present with some hybrids suggesting some degree of reproductive isolation between Nooksack and longnose dace.

Although the two lineages are thought to represent isolation in different glacial refugia for two to three million years (Taylor et al. unpubl. data), the two fish remain morphologically very similar. Any ecological differentiation is unknown, and their degree of mtDNA differentiation puts them well within the range of cyprinid lineages that have undergone complete speciation (Broughton and Gold, 2000; Dowling et al., 2002), but also lineages that have not developed any significant reproductive barriers (Ward et al., 2005; Zemlak et al., 2009). Consequently, testing the degree of genetic differentiation in the nuclear genome in sympatry presents an excellent opportunity to assess the status of the Nooksack and longnose dace as distinct biological species (c.f. McPhail, 1984; Wilson and Bernatchez, 1998; Zamudio and Savage, 2003; Hey et al., 2004; Hyde et al., 2008).

7 1.3. Secondary contact between the Nooksack dace and longnose dace ) in Canada (left) and North America (right). Rhinichthys cataractae Figure 1.2: Distribution of Nooksack dace ( (Modiﬁed from Pearson et al.The (2008). black 1—Brunette dots River, 2—Bertrand inlocations Creek, the 3—Pepin of Brook, right major 4—Fishtrap panel cities. Creek. represent all known populations of Nooksack dace; white squares indicate the

8 1.4. Cryptic species

1.4 Cryptic species

Early studies of the Nooksack and longnose dace uncovered subtle morphological differences: a more slender caudal peduncle and fewer scales along the lateral line and around the caudal peduncle (Bisson and Reimers, 1977). However, it is nearly impossible to tell the two forms apart without re- sorting to scale counts or DNA analysis. Even scale counts themselves are unreliable, as there is overlap between Nooksack and longnose dace in all counts. Their degree of morphological similarity in the presence of extensive mtDNA divergence places them within the category of potential cryptic species. Cryptic species are defined as separate species that are difficult or impossible to distinguish by morphology, and that may have been incorrectly lumped as a single species (Sáezand Lozano, 2005). This does not mean that they are literally identical; these cryptic species may be morphologically distinguishable with the use of tools such as scanning electron microscopy and statistical morphometrics, and may rely upon non-visual cues to identify conspecifics (e.g. Feulner et al., 2006). The key point is that there has general morphological stasis: species diversification, decoupled from ecological and/or morphological change (Bond et al., 2001). The rate of identification of cryptic species has increased enormously since the advent of PCR and affordable genetic sequencing (Bickford et al., 2007) and they are an important component of undescribed biodiversity. The discovery of cryptic species can have major conservation implications: in many cases, a species thought to have a wide distribution has been shown to be a cryptic species complex, with each species having a very small distribution and therefore being at much greater risk of extinction (e.g. Ravaoa- rimanana et al., 2004). Detection of cryptic species can increase diversity estimates within a group by over 100% in taxa as diverse as, for example, frogs (Funk et al., 2012), wasps (Smith and Dowling, 2008), and bats (Mayer and Helversen, 2001). Cryptic species can therefore have a huge effect on biodiversity estimates, making estimation of cryptic species distribution important for identifying global biodiversity hotspots (Trontelj and Fiˇser,2008). Recently, a population of speckled dace (Rhinichthys osculus) in Oregon was proposed to consist of three cryptic species (Hoekzema and Sidlauskas, 2014). If the Nooksack dace and longnose dace are reproductively isolated from one another, the fact that they are morphologically cryptic implies that non-visual modes of mate selection are important for these fish. Limited dispersal abilities and fragmented landscapes have frequently been put forward as potential drivers of cryptic speciation (e.g. Knowlton,

9 1.5. Conservation status of the Nooksack dace

1993; Kelly et al., 2006; Leavitt et al., 2007; Bryson et al., 2011). A variety of studies suggest a similar basic pattern: taxa with low dispersal potential are separated in a fragmented landscape in which each fragment is ecologically equivalent, and undergo divergence without morphological change (Knowlton, 1993). As small freshwater ﬁsh, R. cataractae have limited dispersal ability, and their landscape is fragmented by watershed boundaries, as well as having been historically fragmented by repeated glaciation. In the Oregon coastal clade of R. cataractae, there are two lineages of dace: the Umpqua dace, which is located in the drainages of the Umpqua and Smith Rivers, and the Millicoma dace, which is located in the Coos River, just south of the range of the Umpqua dace (McPhail and Taylor, 2009). Both are derived from Columbia River longnose dace, which live in water- sheds north of the Smith River, but the relationships between the three were a puzzle: the Millicoma dace is morphologically extremely similar to the longnose dace, despite having the range of the morphologically divergent Umpqua dace located in between (McPhail and Taylor, 2009). McPhail and Taylor (2009) constructed a mtDNA phylogeny to resolve this question and found that the Umpqua and Millicoma dace are sister species, with their common ancestor having diverged from the Columbia longnose ancestor 3–4 mya, and the Millicoma dace splitting from the Umpqua dace 1.5–2.0 mya. They suggested that the morphological divergence of the Umpqua dace took place after the separation of the Millicoma dace to the Coos River system, and that dace in this smaller, depauperate environment retained ancestral characteristics. Though the range of the Nooksack dace covers a much larger and biologically complex area than the Coos River watershed, it may be that— similar to the Millicoma dace—the Nooksack dace has not been in an environment with opportunities for morphological divergence.

1.5 Conservation status of the Nooksack dace

Within Canada, the Nooksack dace is listed as Endangered under the Species at Risk Act (COSEWIC, 2007). Their population in Washington State appears to be stable, and they are not listed under the American Endangered Species Act (ESA); however, in Canada their extremely limited range is threatened by habitat destruction, and they have been extirpated from two streams: Howes Creek and Cave Creek (McPhail, 1997). While the Nook- sack dace certainly represents a distinct evolutionary lineage of R. cataractae and will likely continue to be assessed as a Designatable Unit (DU, a

10 1.6. Research objectives subspecies, variety, or genetically or geographically distinct population that may be assessed by COSEWIC, the Committee on the Status of Endan- gered Wildlife in Canada (COSEWIC, 2014)) regardless of how its species status is resolved, any further information about its level of reproductive isolation from the longnose dace and level of admixture in the streams with sympatric dace will help inform decisions about whether the three streams with sympatric dace should be considered part of the range of the NSD and protected as such, and whether interbreeding with LND constitutes a threat to the purity of the NSD gene pool.

1.6 Research objectives

The overarching goal of my thesis research is to clarify the species status of the Nooksack dace by examining its relationships to the longnose dace using genetic and morphological data. Speciﬁcally, my objectives are:

1. Determine whether the Nooksack and longnose dace can be reliably distinguished morphologically, and whether there is morphological intermediacy within the zone of secondary contact using robust morphometric and meristic analysis with samples in sympatry and from throughout the range.

2. Provide a more rigorous assessment of the geographic distribution of the two clades of mtDNA.

3. Test for genetic divergence between Nooksack and longnose dace in sympatry using ten microsatellite markers from the nuclear genome, and to assess the degree of introgression between the Nooksack dace and longnose dace and whether or not reproductive barriers exist between them.

4. In light of the above, assess the status of the Nooksack dace as a designatable unit within the R. cataractae complex.

11 Chapter 2

Morphometric Analysis of the Nooksack and Longnose dace (Cyprinidae: Rhinichthys)

2.1 Introduction

The longnose and Nooksack dace are divergent forms of Rhinichthys cataractae. The longnose dace (LND) is found in freshwaters across North America, while the Nooksack dace (NSD) is found in several streams in southwestern British Columbia (BC) and adjacent portions of Washington State south to the Columbia River (McPhail, 1967, Fig 1.2). Genetic analysis of LND and NSD indicates two deeply diverged clades, with 3-4% sequence divergence of mitochondrial cytochrome b and ND2 mitochondrial DNA (mtDNA), indicating a divergence time of 2-3 mya (Taylor et al., unpubl. data). Despite this long separation, initial morphological analysis showed little diﬀerence except in lateral line and caudal peduncle scale counts, and caudal peduncle depth (McPhail, 1967). A more detailed morphological analysis may uncover subtle diﬀerentiation, and if separate morphological forms are found to correspond with the mtDNA clades, it will provide further support for the two groups being designatable units (DUs) under Canada’s Species at Risk Act and perhaps distinct species. Additionally, there are three streams in southwestern BC in which the two mtDNA clades exist in sympatry (Taylor unpubl. data). If morphology still corresponds with mtDNA where the two clades exist in sympatry, it would provide strong evidence that the two are distinct species. No multivariate morphometric analysis has been carried out to date on the R. cataractae complex, and it is an important step to fully describe the putative Nooksack dace species. If there is no consistent morphological distinction between the two groups, they may be considered cryptic species:

12 2.1. Introduction separate species which are difficult or impossible to distinguish by morphology, and have been incorrectly lumped as a single species (Sáezand Lozano, 2005). A multivariate morphological analysis will establish whether the Nook- sack and longnose dace are truly morphologically cryptic, and if further genetic analysis (Chapter 3) establishes them as separate species, they may qualify as cryptic species. If so, it would add support to the theory that limited dispersal abilities and fragmented landscapes are drivers of cryptic speciation (Knowlton, 1993; Kelly et al., 2006; Leavitt et al., 2007; Bryson et al., 2011). If any consistent, fine scale morphological differentiation exists, it may provide clues to ecological differentiation or the sources of reproductive isolation between species (e.g. Feulner et al., 2007). There are instances of similar levels of morphological overlap between other dace species: for example, the eastern (Rhinichthys atratulus) and western (Rhinichthys obtusus) blacknose dace (Fraser et al., 2005). The classification of different forms in the R. atratulus complex has switched back and forth from multiple subspecies to a single species, and a rigorous morphological study was needed to see if the different groups could be reliably distinguished (Fraser et al., 2005). A univariate and multivariate morphometric analysis found that eastern and western blacknose dace could not be differentiated morphologically in allopatric populations, nor in sympatric populations, where it was hypothesized that character displacement would lead to greater morphological differentiation (Fraser et al., 2005). Perhaps the same scenario applies to the Nooksack and longnose dace. In this chapter, I report the results of a morphological analysis of 582 longnose dace (LND) and Nooksack dace (NSD) from 45 sampling sites in Washington State, Michigan, Oregon, British Columbia, Alberta, Manitoba, and Ontario, comprising 25 populations. Of the sampling locations, eight are allopatric for NSD; 14 are allopatric for LND; and three are sympatric LND and NSD. The analysis consisted of multivariate statistical analysis of 11 morphometric and three meristic characters to test for differentiation between the two putative species, and to see if the sympatric mtDNA populations were also morphologically distinct which would be consistent with the existence of two species of dace. Alternatively, if the sympatric populations showed any sign of morphological intermediacy this would be consistent with the idea that there is no reproductive isolation between LND and NSD and that they readily interbreed when sympatric. I also tested for other possible explanations of observed morphological differentiation. Morphological variation along environmental gradients such as salinity, or latitudinal gradients (e.g. McDowall, 2003; Floeter et al., 2004)

13 2.2. Materials and methods has been well documented, and intraspecific diversification arising from divergent selection has been widely studied in fishes. Differentiation arising from divergent selection can be due to genetic differentiation (e.g. Schluter and McPhail, 1992; Jonsson and Jonsson, 2001) or plasticity (e.g. Wood and Bain, 1995) and has been of use in defining and identifying fisheries stocks (Swain and Foote, 1999). Wood and Bain (1995) used regression analysis to demonstrate significant relationships between body morphology and microhabitat use in a variety of cyprinid and percomorph fishes in the Alabama River watershed; on a larger scale, Salini et al. (2004) showed a disjoint between morphological variation and genetic variation in Tenualosa ilisha in five countries, and concluded that the wide morphological variation was due to environmental differences. Many studies demonstrate that considerable intraspecific variation across the species’ range is due to environmental conditions. Although R. cataractae does not show considerable morphological variation across its range (Scott and Crossman, 1998), I wanted to investigate whether there might be environmental correlates to morphological variation. While it was impossible to gather environmental data first-hand from all sampling sites, Geographic Information System (GIS) and Remote Sensing (RS) databases provide a great resource for combining fish distribution data and surveyed or remotely sensed environmental data. These tools have been used exten- sively in conservation and in fisheries management and research to identify essential habitat over large spatial scales (Valavanis et al., 2004). I used pub- licly available GIS databases to obtain environmental data for each sampling location.

2.2 Materials and methods

2.2.1 Sampling The samples used in my study were drawn from the Beaty Biodiversity Mu- seum’s fish collections, as well as samples I collected myself in 2012. Samples from the Beaty Biodiversity Museum include 532 fish from 23 streams, with collection dates ranging from 1954 to 2011 (Appendix A, Table A.1). A minimum of 10 fish were measured from each stream. In August and September 2012, I collected 50 fish from seven sites in the three streams that had been previously identified as having both NSD and LND mtDNA: the Coquitlam and Alouette rivers, and Kanaka Creek. I collected fish using both electrofishing and kick-seining. All fish were sac- rificed using an overdose of MS-222 and stored in 95% ethanol. Altogether,

14 2.2. Materials and methods

582 fish from 23 locations were measured. Fish were only measured if their standard length was greater than 4 cm to ensure that all fish measured were adults. Fish were collected under Ministry of Environment fish collection permit #SU12-81471; because the Coquitlam River, Alouette River, and Kanaka Creek are not currently recognized as containing Nooksack dace, permits were for longnose dace only. During my sampling, I also tried to uncover any hitherto undiscovered populations of dace that could potentially be sympatric for NSD and LND mtDNA. I hoped to obtain finer-grained detail about the boundaries of the putative hybrid zone, and to obtain samples from streams that had not yet been searched for Nooksack or longnose dace. As such, I electrofished in every stream (N = 5) that met the following criteria:

• It had no previously documented dace population

• It provided appropriate dace habitat: clean, shallow, fast-moving water with a rocky substrate ranging in size from gravel to boulders (Scott and Crossman, 1998).

• It lay between the Brunette River in the east (the most easterly pure NSD river on the north side of the Fraser River) and Norrish Creek in the west (the most westerly pure LND river on the north side of the Fraser River.)

2.2.2 Morphometric and meristic measurements I measured 12 morphometric characters on the left side of each specimen, following Hubbs et al. (1958) and made three meristic counts: lateral line scale count, pectoral fin ray count, and vertebral count (Table 2.1). Mea- surements were made using Vernier dial calipers, and a dissecting microscope when necessary. To obtain vertebral counts, fish were scanned using at the UBC Cen- tre for High-Throughput Phenogenomics using a Micro-CT 100 scanner (Scanco Medical AG, Brttisellen, Switzerland). Scans were conducted with an isotropic voxel size of 49.2µm; energy of 90 kVp; intensity of 200µA; integration time of 150 ms; and filter of 0.5 mm. All raw scan data were converted into DCM (DICOM—Digital Imag- ing and Communications in Medicine) format and then imported into Mi- croView v. 2.5.0 (Parallax Innovations 2014). Scan data is stored as 2D cross-sectional slices, so Microview was used to convert these slices into a 3D image, from which vertebral counts were conducted by eye (Fig. 2.1).

15 2.2. Materials and methods

Table 2.1: Description of morphometric (M1-M12) and meristic (Count 1- 3) measurements examined in populations of Nooksack and longnose dace (Rhinichthys cataractae).

Measure View Description of Measurement / Count Number

M1 Dorsal Distance between the eyes M2 Dorsal Width at dorsal fin origin M3 Dorsal Width at origin of caudal fin M4 Left side Distance from dorsal fin insertion to back side of eye M5 Left side Distance from dorsal fin insertion to pectoral fin insertion M6 Left side Distance from dorsal fin insertion to pelvic fin insertion M7 Left side Distance from dorsal fin insertion to anal fin insertion M8 Left side Distance from dorsal fin insertion to bottom of caudal fin insertion M9 Left side Distance from snout to front side of eye M10 Left side Distance from pectoral fin insertion to anal fin insertion M11 Left side Width of caudal peduncle at caudal fin insertion M12 Left side Distance from snout to end of scales on caudal peduncle Count 1 Left side Lateral line scale count Count 2 Left side Pectoral fin ray count Count 3 CT scan Vertebral count

16 2.2. Materials and methods Figure 2.1: Microcount CT was scan conducted of by longnose eye. dace sample CL018 (Columbia River, BC, length = 87.72 mm). Vertebral

17 2.2. Materials and methods

2.2.3 Statistical analysis of morphological data The first priority of this chapter was to determine whether NSD and LND form different morphological groups, and whether populations from streams with sympatric dace contain individuals that are morphologically intermediate. Thus, analyses were first performed on allopatric populations, to ad- dress the question of differences between pure NSD and LND. Subsequently, analyses were performed on (1) all dace, and (2) the subset of samples from streams with sympatric dace in which mtDNA haplotype had been matched with morphology. Analysis of group (2) attempted to unravel whether there was an association between morphology and mtDNA haplotype.

Size-standardization In morphometric analyses, it is often observed that the size of body parts scale allometrically with body size; consequently, when carrying out a morphometric analysis of shape, it is critical to remove the eﬀect of size variation among samples (Reist, 1985; Albrecht et al., 1993; Lleonart et al., 2000). Reist (1985) recommended allometric scaling to a standard size, a technique which has been used in many subsequent studies (Elliott et al., 1995; Fraser et al., 2005; Østbye et al., 2005), and which I have also chosen to use in mine. As such, to remove the eﬀect of size, I standardized all morphometric measurements using the equation:

Ls b Ms = Mo( ) (2.1) Lo

Where Ms = standardized measurement, Mo = measured character length, Ls = overall mean standard length for all ﬁsh, Lo = standard length of specimen, and b was estimated for each character using the allometric growth equation M = aLb (2.2)

I estimated parameter b as the slope of the regression of log Mo on log Lo. All samples were used to estimate b, but the intercept was allowed to vary between groups (streams allopatric for NSD, allopatric for LND, and sympatric for both NSD and LND mtDNA haplotypes.) Unless otherwise stated, all subsequent analyses utilize size-standardized morphometric measurements. No meristic counts were size-transformed; I tested for relationships between counts and standard length but found none (r between 0.02 and 0.1, all P > 0.1) These calculations, as well as all further analyses, were conducted using the R statistical environment (R Core Team, 2013).

18 2.2. Materials and methods

The size-standardization was carried out using the average of M12 (standard length) as the size to standardize all ﬁsh to; therefore, following size- standardization, all ﬁsh had the same value for M12 and it was discarded from all further analyses.

Principal components analysis Multivariate statistical analysis can simplify a great number of measurements into more useful and informative summaries across a smaller number of composite variables (Dunteman, 1989). Most morphometric studies looking to distinguish two or more groups use principal components analysis (PCA) as a first step (Chapleau and Pageau, 1985; Edge et al., 1991; Østbye et al., 2005; Egge and Simons, 2006). I performed a principal components analysis on the 11 morphometric and two meristic characters (vertebral counts were uninformative and not used—see below), and used the principal components to conduct subsequent analyses. All principal components analyses were performed on the correlation matrix using the FactoMineR package for R, v.1.24 (Husson et al., 2013). I used the Jolliffe cut-off criterion as a guide to deciding how many principal components to retain for subsequent analyses, discarding any principal component whose eigenvalue was less than one (Jolliffe, 2002).

ANOVA I performed nested analysis of variance (ANOVA) on principal component (PC) scores with sampling location as a subgroup of putative species, to see whether differences in morphology between NSD and LND persisted after accounting for geographic variation within putative species. Putative species was a fixed factor and sampling location was a random factor. I grouped samples according to mtDNA clades present in the stream: NSD and LND for the allopatric populations, and “both” for streams with sympatric mtDNA types. I first performed nested ANOVAs on samples from only allopatric locations, to test for morphological differentiation between pure forms of NSD and LND. Next, I analyzed samples from all locations, to see whether sympatric dace fell into a group of their own. A subset of samples from Kanaka Creek had been analyzed for mtDNA haplotype as well as morphology (N = 44). In order to test whether there was a significant association between mtDNA type and morphotype in streams with sympatric dace, I performed single-factor ANOVAs on the PCs for these samples, grouped by mtDNA type. Lateral line scale count has been identi-

19 2.2. Materials and methods

ﬁed in the past as a divergent character between LND and NSD (McPhail, 1967) and I performed ANOVAs on this character separately. I performed a nested ANOVA on all samples, with population as a subgroup of putative species, and a single-factor ANOVA on samples from Kanaka Creek, grouped by mtDNA type.

Cluster analysis Clustering is a form of data analysis that places objects into “natural” groups without any predefined groups being set (Fielding, 2007). I performed a cluster analysis on the morphological data to determine the number of clusters present, using R package mclust, v.4 (Fraley et al., 2012). mclust fits a series of different Gaussian mixture models to the data and tests the fit of each model with different numbers of clusters. mclust then selects the model and number of clusters that maximizes the Bayesian information criterion (BIC), a model-selection criterion based on the likelihood function and accounting for overfitting by applying a penalty for the number of model parameters (Fraley et al., 2012). In addition to the analyses on the complete dataset and the set of allopatric populations only, I also performed analyses on a subset that contained only the streams sympatric for NSD and LND mtDNA, and individual analyses on each sympatric population, in order to test for morphological differentiation in sympatry. Again, for the Kanaka Creek samples, not only could I use mclust to test for the presence of two or more morphological groups of dace, but I could test for an association between morphological clusters and mtDNA type because these fish had mtDNA type and morphology matched by individual.

Discriminant analysis Discriminant analysis (DA) is a common classification method for multivariate data. It uses one or more predictor variables to predict a categorical dependent variable; unlike in cluster analysis, DA is used when groups are known or assumed a priori. Data for which the grouping is already known can be used to “train” the DA, by finding the combination of the predictor variables which maximizes the difference between predefined groups. This can then be used to classify samples for which the grouping is not known. Traditionally, discriminant analysis creates a linear combination of the predictor variables; however, the mclust package in R (Fraley et al., 2012) uses a model-based approach to recombine the predictor variables for maximum

20 2.2. Materials and methods discriminatory power. Because cluster analysis does not assume a particular number of clusters, and does not include a priori knowledge of grouping, it may uncover variation due to location, or other factors. Performing a discriminant analysis with a priori knowledge of species grouping constrains the analysis to maximize discrimination between species groups, even if other factors may be significant contributors to morphological variation. In this sense it is a better test than cluster analysis of whether Nooksack and longnose dace can be distinguished morphologically. I trained the discriminant analysis on half of the samples from allopatric locations and tested its discriminatory ability on the other half. I also applied the discriminant function obtained in the first step to sympatric samples matched with mtDNA, to see if the DA could correctly differentiate between LND and NSD mtDNA types in sympatry.

Analysis of environmental variables To investigate the possibility that any morphological diﬀerentiation was a function of environment rather than lineage, I ran linear regressions of PCs 1–5 for all dace populations against the following variables:

1. Watershed area

2. Annual mean temperature

3. Maximum temperature of the warmest month

4. Minimum temperature of the coolest month

5. Temperature range

6. Annual precipitation

7. Precipitation of Wettest Quarter

8. Precipitation of Driest Quarter

9. Date collected

Choice of variables to test was determined by data availability as well as demonstrated relevance in previous studies. Temperature (variables 2–5) is a factor in Baltic herring morphology (Clupea harengus, Jørgensen et al., 2008) as well as many other fishes; it may affect phenotype directly or in- directly (e.g. by affecting prey availability). Watershed area, temperature

21 2.3. Results and precipitation can all affect species composition in streams, potentially affecting both prey availability and predation pressure on Rhinichthys, both of which can affect morphology (Jackson et al., 2001). Collection date was included mainly to check whether there had been deterioration of older samples. To obtain this data, I used raster GIS data from WorldClim (Hijmans et al., 2005, http://www.worldclim.org) and shapefiles from the United States Geological Survey (United States Geological Survey, 2014) and DataBC (British Columbia Provincial Government, 2014). I joined this data to my sampling site locations using the program QGIS (QGIS Development Team, 2009), and ran linear regressions in R. I also performed ANOVAs comparing the means of PC 1–PC 5 and lateral line scale count for dace found east and west of the Rocky Mountains, as this is a geographical barrier often associated with morphological differentiation, and has been associated with differences in mating behaviour in subspecies of R. cataractae (Bartnik, 1970). To avoid confounding the effects of putative species and geography (as there are no NSD found east of the Rocky Mountains), I also compared solely allopatric LND populations from east and west of the Rocky Moun- tains.

2.3 Results

2.3.1 Searching for new dace populations After sampling in ﬁve streams that met my criteria across a total of four days, I did not ﬁnd any new populations of dace (Table 2.2).

2.3.2 Variation in vertebral counts I scanned an initial sample of 53 fish (17 from allopatric NSD populations, 36 from allopatric LND populations) in the micro-CT scanner and visualized them in the computer program MicroView, then counted vertebrae by eye. An ANOVA conducted on the vertebral counts, however, was non-significant (F = 0.1143, P > 0.7), indicating no difference between vertebral counts for NSD and LND, and there was almost complete overlap of vertebral counts for fish from the two mtDNA clades (see Fig. 2.2). Given this initial sample, vertebral counts did not seem to be an informative measurement and I did not scan any further samples. The average vertebral count (±SD) was 35.7 (±0.66) and 34.7 (±0.93) for LND and NSD, respectively (see Appendix A,

22 2.3. Results

Table A.2 for counts by location). Vertebral counts were not used in any further analyses.

2.3.3 Principal components analysis For the PCA performed on the set of all samples, the mean eigenvalue across all PC axes was ∼ 0.92 and served as the Jolliffe cut-off criterion, meaning that the first five principal components were retained. This selection was also largely in accordance with the rule-of-thumb of discarding PCs with eigenvalues less than 1; PCs 1-4 had eigenvalues greater than 1, and PC 5 had an eigenvalue of ∼ 0.95. Thus, the first five principal components were retained for analyses and explained a total of 58.5% of the morphological/meristic variance: PC 1, 18.3%; PC 2, 13.8%; PC 3, 10.2%; PC 4, 8.8%; PC 5, 7.4% (Appendix A, Table A.3). Size-standardization removed any effect of size, so the PCs represent shape differences. In PC 1, M2 (width at dorsal fin insertion) and M6 (dorsal insertion to pelvic insertion) had the highest positive loadings, with lateral line scale count and M9 (snout to eye distance) and M10 (pectoral insertion to anal insertion) had the highest negative loadings (see Fig. 2.3). In PC 2, M7, M8 (measures of distance posteriorly from dorsal insertion), M11 (caudal peduncle depth) and lateral line scale count had the highest positive loadings and M1 (interorbital width) and M5 (dorsal insertion to pectoral insertion) had the

Table 2.2: Sampling of previously un-surveyed streams to try and detect new Nooksack and/or longnose dace (Rhinichthys cataractae) populations in southwestern British Columbia. Location, survey date, latitude/longitude, sampling method, and length of stream reach sampled (m) are provided.

Location Survey Latitude Longitude Sampling Stream Date Method Reach Sampled (m) Cascade Creek 10/10/2012 49.41556 -122.45305 electrofisher 50 Gaudin Creek 9/9/2012 49.15681 -122.33160 electrofisher 50 Lagace Creek 10/10/2012 49.21656 -122.23520 kick-seine 30 Whonnock 10/11/2012 49.21964 -122.45782 electrofisher 50 Creek Scorey Creek 9/9/2012 49.24778 -122.25166 electrofisher 50 Scorey Creek 9/20/2012 49.20019 -122.24670 electrofisher 50

23 2.3. Results

Figure 2.2: Histogram showing distribution of vertebral counts for longnose and Nooksack dace (Rhinichthys cataractae) collected from six streams ranging from British Columbia to Ontario (N = 53.) Grey bars signify longnose dace, black bars signify Nooksack dace.

24 2.3. Results highest negative loadings (Fig. 2.3). In PC 3, M3 (body width at caudal peduncle) had the highest positive loading and M5 and M10 the lowest; PC 4 was dominated by high positive loadings on M9 and M10. Finally, PC5 was dominated by a high positive loading on pectoral fin ray count and a negative loading on M8 (dorsal insertion to bottom of caudal fin insertion (see Appendix A, Table A.4 for complete character loadings). For the PCA performed on allopatric populations only, the first five PCs were again retained for analysis, again according both to the Jolliffe cutoff and the convention of removing PCs with eigenvalues less than one. The first five PCs accounted for 59.3% of the variance: PC 1, 19.9%; PC 2, 12.7%; PC 3, 10.2%; PC 4, 8.5%; and PC 5, 7.9% (Table 2.3). In PC 1, M1 (interorbital width), M2 (width at dorsal fin origin) and M6 (dorsal fin insertion to pelvic fin insertion) had the highest loadings, and M10 (pectoral fin insertion to anal fin insertion) and lateral line scale count had the most negative loadings. In PC 2, M7 (dorsal fin insertion to anal fin insertion) and M11 (caudal peduncle depth) had high positive loadings; in PC 3, M3 (body width at caudal peduncle) has a high positive loading, and M5 (dorsal fin insertion to pectoral fin insertion) and M10 had very negative loadings. In PC 4, M9 (snout to eye) had a high loading and pectoral fin ray was most negative, and in PC 5, pectoral fin ray was highest and M8 (dorsal fin insertion to bottom of caudal fin insertion) was lowest (Table 2.4).

Variation in body morphology and meristics For both datasets—only allopatric populations, and all populations—nested ANOVAs were performed on the first five principal components, with the samples grouped by species and localities nested within species. The results of the nested ANOVAs conducted on allopatric populations showed that both putative species and sampling location had significant effects on all principal components, except for the effect of putative species on PC 4 (P = 0.103). Typically, location accounted for a greater percentage of the total morphological variation than putative species identity. For PC 1, r2 values show that species accounted for 11.6% of the total variation, and location accounted for 39.3% (Table 2.5). For PC 2, putative species accounted for 21.9% of the variation, and location accounted for 25.1%. For PC 3–PC 5, putative species accounted for 0.5–1.8% of the variation, and location accounted for 20.0–35.3% (Table 2.5). The results of the nested ANOVA conducted on PCs for all sampling locations showed that putative species and sampling location had significant effects on all principal components, again with the latter factor typically

25 2.3. Results

Table 2.3: Eigenvalues and percentage of variance explained by each axis of a principal components analysis conducted on 11 size-transformed morphological traits and two meristic traits values of morphological samples for allopatric populations of Nooksack dace and longnose dace (Rhinichthys cataractae). N = 414.

PC Eigenvalue Percentage of variance Cumulative percentage of variance

1 2.58 19.91 19.91 2 1.65 12.74 32.66 3 1.32 10.21 42.87 4 1.10 8.48 51.35 5 1.02 7.90 59.26 6 0.91 7.00 66.26 7 0.83 6.41 72.68 8 0.74 5.71 78.39 9 0.71 5.47 83.87 10 0.66 5.10 88.98 11 0.54 4.16 93.15 12 0.49 3.81 96.96 13 0.39 3.03 100.00

26 2.3. Results

Figure 2.3: Variables factor map for the principal components analysis of size-transformed characters for all samples of longnose dace and Nooksack dace (Rhinichthys cataractae), showing the variable loadings for the ﬁrst two PCs (“Dim1” and “Dim2”). The arrows represent the relative character loadings along each axis simultaneously.

27 2.3. Results

Table 2.4: Character loadings for the ﬁrst ﬁve principal components of the PCA conducted on 11 size-transformed morphological traits and two meristic traits values of morphological samples for allopatric populations of Nook- sack dace and longnose dace (Rhinichthys cataractae).

Measurement PC 1 PC 2 PC 3 PC 4 PC 5

M1 0.676748 −0.056530 0.356530 −0.122115 −0.002919 M2 0.654676 0.114546 0.390251 0.134502 −0.038168 M3 0.159775 −0.074513 0.581263 0.357333 0.167530 M4 0.577557 −0.074360 −0.389468 −0.168274 0.199190 M5 0.555256 −0.035977 −0.407105 0.080368 0.132872 M6 0.656964 0.414734 −0.143638 −0.047379 −0.240394 M7 0.149575 0.682831 −0.160072 0.052721 −0.225181 M8 −0.225344 0.280736 0.287599 −0.374290 −0.567322 M9 −0.187815 0.353676 0.158705 0.640619 0.121196 M10 −0.383003 0.432158 −0.409583 0.318238 0.008586 M11 0.270543 0.625259 0.134467 −0.097243 0.239235 Lateral line scale −0.504469 0.326214 0.206894 −0.276134 0.140174 count Pectoral ﬁn ray count −0.143405 0.320021 0.101861 −0.405594 0.647223

Table 2.5: r2 value, F-ratios and P-values for nested ANOVAs conducted on the first five principal components of morphological samples from allopatric populations of Rhinichthys cataractae: allopatric longnose dace (LND) and allopatric Nooksack dace (NSD). Sampling location was nested within putative species. For each PC, statistics are given for putative species (first line) and sampling location nested with putative species (second line).

PC r2 FP

1 Species 11.6% 92.84 < 0.0001 Location 39.3% 18.57 < 0.0001 2 Species 21.9% 163.15 < 0.0001 Location 25.1% 10.99 < 0.0001 3 Species 1.8% 11.07 0.000962 Location 35.3% 13.02 < 0.0001 4 Species 0.5% 2.675 0.103 Location 22.4% 6.737 < 0.0001 5 Species 1.3% 6.31 0.0124 Location 20.0% 5.918 < 0.0001

28 2.3. Results accounting for a greater percentage of the total variation. For PC 1, species accounted for 11.8% of the variation, and location accounted for 35.9% (Ta- ble 2.6). The only instance of species accounting for a greater proportion of the variance than location was for PC2: species accounted for 29.9%, location for 22.3%. For PC 3–PC 5, species accounted for 1.0–3.8% of the variance, and location for 17.5–28.6% (Table 2.6).

Table 2.6: r2 value, F-ratios and P-values for nested ANOVAs conducted on the first five principal components of morphological samples from all populations of Rhinichthys cataractae: allopatric longnose dace, allopatric Nooksack dace, and sympatric dace. Sampling location was nested within putative species (Nooksack dace, longnose dace, or “both” for streams with sympatric dace). For each PC, statistics are given for putative species (first line) and sampling location nested with putative species (second line).

PC r2 FP 1 Species 11.8% 62.92 < 0.0001 Location 35.9% 19.17 < 0.0001 2 Species 29.9% 175.23 < 0.0001 Location 22.3% 11.67 < 0.0001 3 Species 3.5% 14.53 < 0.0001 Location 28.6% 11.79 < 0.0001 4 Species 3.8% 14.645 < 0.0001 Location 22.7% 8.637 < 0.0001 5 Species 1.0% 3.59 0.0282 Location 17.5% 5.982 < 0.0001

By contrast, my analysis of sympatric dace in Kanaka Creek dace showed no difference between fish carrying NSD and LND mtDNA; ANOVAs were non-significant for all but PC 3 (F = 4.0672, P = 0.024) (Table 2.7).

Variation in lateral line scale counts When analyzed across all populations, there was a strong difference in lateral line scale counts between NSD and LND, and ANOVA results were highly significant (F = 186.5, P < 0.0001) (see Fig. 2.4). In a nested ANOVA with location nested within species, both species and location had significant effects (P < 0.0001), and species accounted for 28.9% of the variance while location accounted for 15.5%. By contrast, when the analysis was performed on samples from Kanaka creek and separated by mtDNA haplotype, there

29 2.3. Results

Table 2.7: F-ratios and P-values for ANOVAs conducted on the ﬁrst ﬁve principal components of samples of longnose dace and Nooksack dace (Rhinichthys cataractae) from Kanaka Creek which have been matched with mtDNA haplotype, testing for grouping by mtDNA type.

PC F P

1 0.5372 0.5884 2 0.3564 0.7023 3 4.0672 0.02447 4 0.6719 0.4321 5 0.6127 0.5468

was no pattern found; the distribution of lateral line scale counts was similar between the LND and NSD (ANOVA: F = 0.0183, P > 0.98) (Fig. 2.5). Overall, lateral line scale counts for sympatric populations were intermediate between LND and NSD: mean lateral line scale count for LND was 66.26, for NSD 59.99, and for sympatric dace 62.50 (ANOVA: F = 117.6, P < 0.0001).

30 2.3. Results ) sampling locations. Light grey bars signify longnose dace Rhinichthys cataractae Figure 2.4:Nooksack and Histogram longnose showing dace lateral ( line scale count by mtDNA haplotype for samples from 19 allopatric mtDNA, dark grey bars signify longnose dace mtDNA. N = 414.

31 2.3. Results

Figure 2.5: Histogram showing lateral line scale count by mtDNA haplotype for Kanaka Creek samples of longnose dace and Nooksack dace (Rhinichthys cataractae). Light grey bars signify Nooksack dace mtDNA, dark grey bars signify longnose dace mtDNA. N = 43.

Morphological cluster analysis The results for mclust analysis on all dace populations indicated that the top three models according to the BIC criterion were VEI, 5 clusters (BIC = −8732.108); VEI, 6 clusters (BIC = −8742.650); and VVI, 4 clusters (BIC = −8761.264, see Appendix A, Table A.5 for complete BIC values for each model and number of clusters). The model names indicate whether the volumes of the clusters may vary, whether the shapes of the clusters may vary, and what the orientation of the clusters is. For example, the VEI model indicates that the volumes of the different clusters may vary (V), the shapes of the clusters are the same (E), and the orientation of the clusters is the identity (I). The VVI model is the same except that the shapes of the clusters may vary as well. Using the five cluster model, a strong association was detected between morphocluster and dace type (LND, NSD or “both”), as tested with a contingency table (G = 228.7, P < 0.0001); however, each cluster contained a mixture of samples from allopatric LND sites, allopatric NSD sites, and sympatric sites (see Fig. 2.7 for a plot of cluster by putative species, and Fig. 2.6 for a representation of the clusters on a scatter plot of first two principal components). The only exception was cluster 5, which

32 2.3. Results was composed entirely of NSD samples from Wynoochee River. The results for mclust analysis on allopatric dace populations indicated that the top three models according to the BIC criterion were VEI, 6 clusters (BIC = −6199.875); VEI, 3 clusters (BIC = −6224.831); and VEI, 5 clusters (BIC = -6228.845, see Appendix A, Table A.6 for complete BIC values for all models and number of clusters). Using the six cluster model, a strong association was detected between morphocluster and dace type, as tested with a contingency table (G = 139.2, P < 0.0001). Each cluster was dominated by either NSD or LND, but all clusters contained a mixture of LND and NSD, aside from cluster 6, which consisted entirely of samples from the Wynoochee River in western Washington (Fig. 2.8). The results of mclust analysis on the subset of sympatric samples indicated that the top three models according to the BIC criterion were VII, 3 clusters (BIC = −2742.222), VII, 4 clusters (BIC = −2743.502), and VII, 2 clusters (−2747.559). I also analyzed each sympatric population individually: for Coquitlam River, the best model was EII, 2 clusters (BIC = −392.8082); for Alouette River, the best model was EEI, 2 clusters (−773.41116); and for Kanaka Creek, the best model was EII, 3 clusters (BIC = −1029.744). For Kanaka Creek samples which had mtDNA type matched with morphology, I performed a contingency test of the association between mtDNA type and cluster. The results were non-signiﬁcant: G = 2.5402, P = 0.2808 (see Fig. 2.9).

Predicting group membership with discriminant analysis The mclust discriminant analysis performed on allopatric populations correctly assigned 88.4% of dace to the correct group: 91.7% of LND were correctly assigned, and 80.6% of NSD. The model-based approach to discriminant analysis ﬁts a Gaussian mixture model as a density estimate for each category of the a priori grouping (Fraley et al., 2012), using BIC to select the best model for each category. In my analysis, the LND group was best modeled by a VVI model with three components (i.e., morphological subgroups), and the NSD group was best modeled by a EEI model with four components.

Environmental correlates of morphological variation The analyses performed on principal components vs. environmental variables did not detect any strong associations. Though some linear models were signiﬁcant (Table 2.8), r2 values were all < 0.10, with the exception

33 2.3. Results

Figure 2.6: Convex hulls enclose each mclust-assigned cluster of individual dace longnose dace and Nooksack dace (Rhinichthys cataractae) based on variation in 11 morphometric and two meristic traits, measured on all samples from all populations (N = 582). Putative species is indicated by symbol: open triangles indicate allopatric longnose dace, plus signs indicate allopatric Nooksack dace, and open circles indicate sympatric samples.

34 2.3. Results

Figure 2.7: Stacked bar chart showing the composition of each cluster generated by the mclust analysis conducted on samples of Rhinichthys cataractae from all sampling locations (allopatric LND, allopatric NSD, and sympatric, N = 582). Each column represents one cluster (cluster number indicated at the bottom). The colours indicate the proportions of each species that make up the clusters: Nooksack dace (light grey), longnose dace (black), and sympatric dace (dark grey). All clusters aside from cluster 5, which is composed entirely of Nooksack dace from Wynoochee River, WA, contain a mixture of longnose, Nooksack, and sympatric dace.

35 2.3. Results

Figure 2.8: Stacked bar chart showing the composition of each cluster generated by the mclust analysis conducted on longnose dace and Nooksack dace (Rhinichthys cataractae) from only those locations that were allopatric for longnose or Nooksack dace (N = 414). Each column represents one cluster (cluster number indicated at the bottom). The colours indicate the proportions of each species that make up the clusters: Nooksack dace (black) and longnose dace (grey).

36 2.3. Results

Figure 2.9: Stacked bar chart showing the composition of each cluster generated by the mclust analysis conducted on longnose dace and Nooksack dace (Rhinichthys cataractae) from Kanaka Creek (N = 44). Each column represents one cluster (cluster number indicated at the bottom). The colours indicate the proportions of each mtDNA haplotype that makes up the clusters: ﬁsh with Nooksack dace mtDNA (“A”, grey) and longnose dace mtDNA (“B”, black).

37 2.3. Results of PC 2’s association with annual precipitation (r2 = 0.14), PC 2’s association with highest precipitation quarter (r2 = 0.14), PC 2’s association with date collected (r2 = 0.16), and PC 2’s association with watershed area (r2 = 0.25). However, for all four of these, the slope given by the model was < 0.003 (Table 2.8.) (P significant at < 0.011.) Comparisons of morphology east and west of the Rocky Mountains for all dace were significant for PC 2–PC 5, as well as lateral line scale count (Table 2.9). Fish from west of the Rocky Mountains had an average lateral line scale count of 63.1, whereas fish from the east had on average 66.8 (a difference of 3.7 scales; LND have on average 6.3 more scales than NSD). They showed the greatest differences in (transformed) values for M3 and M4, with dace east of the Rockies being larger for both. Comparisons of morphology for allopatric LND east and west of the Rocky Mountains were significant for PC 2–PC4, with the comparison of lateral line scale counts being almost significant (P = 0.09) (Table 2.10). LND from west of the Rocky Mountains had on average 65.9 lateral line scales, whereas fish from the east had on average 66.8 (a difference of 0.9 scales).

38 2.3. Results 4 − 10 × 0001 * 02907 01485 11 0004114 . . . . . 0 0 0 2 0 − 0.005392 0.04403 . − 0.01106 0.00695 * − − < 4 − 10 × 0001 * 0.000548 * ). Morphological variation was 08163 0.138 022625 0.017737 0002586 0.0001862 . . . . 0 0 0 0 12 . − − − 0.05566 0.02016 0.000404 * 0.02266 0.02319 0.000159 * 2 < 4 − 10 × 0001 * 0001 * 0001 * 08763 031099 94 ...... 0 0 0 0 0 2 − 0.04501 < − 0.03869 < − < Rhinichthys cataractae 4 − 10 × 0001 * 0001 * 0.0008 *0001 * 0.007 * 0001 * 0.01790 0.261 0.369 0001 * 11678 047561 42 ...... 0 0 0 0 0 0 0 8 − 0.05935 < < − 0.06785 < − < < 01019 001411 13241 0.2800300173 0.10925 005656 0.051235 0.030955 0002651 0.0032378 0.0005409 0009039 0.09064 0.04409 0.02669 0.01917 0008472 0.2457 0.009893 0.0005729 ...... 0 0 0 0 0 0 0 0 − − 0.654 − 0.001248 − 0.885 − 0.0002942 − 0.014540.002319 * 0.0934 0.01792− 0.01101 0.04193 0.485 − 0.437 0111, using the method of Benjamini and Yekutieli (2001)) tests are indicated by (*). . 0 Maximum temperature of hottest month Temperature range Variable testedWatershed area PC 1Annual mean temperature PC 2Minimum temperature PC 3of coldest month PC 4 PC 5 P < Table 2.8: Results for linearacross regressions conducted 21 on populations between of morphologicalsummarized variables Nooksack using and dace principal environmental variables component and analysis longnose acrossprovided dace 11 are morphological ( variables slope and two of meristicof traits. the linear Statistics model, adjusted r-squared value, and P-value. Signiﬁcant (at the corrected value

39 2.3. Results 11 − 10 × 0001 * 91 0003731 003967 . . . . 0 8 0 0 − 0.01474 0.00218 * − − 0.0353 < 10 − 10 × 0001 * . 0 49 . 1 0.01213 0.00494 * 0.0054478 0.05664 < 10 − 10 × 0006103 0.0003416 50 003094 . . . 0 1 0 − − 0.02126 0.000288 * − 0.01445 0.00238 * 10 − 10 × 0001 * 0001 * 0001 * 0.0197 0.0127 0.104 0018418 006214 . . 15 . . . . 0 0 0 0 2e-16 *0 0.000485 * 0.0359 * 0.0121 * 7 0.1373 < − < − 0.04635 < < − 10 − 10 × 0001 * . 001981 30 . . 0 0 4 0.0419< 0.157 0.007731 0.009077 0.002883 0.01123 0.00657 ** 0.0007504 − 0.001978 0.146 0.01640.00129 ** 0.1411 0.01957 0.005998 0.009319 − Table 2.8 – continued from previous page Precipitation of wettest quarter Variable testedAnnual precipitation PC 1Precipitation of PC driest 2quarter PC 3 PC 4 PC 5 Date collected

40 2.4. Discussion

Table 2.9: F-ratios and P-values for ANOVAs comparing longnose dace and Nooksack dace (Rhinichthys cataractae) samples from east and west of the Rocky Mountains for the ﬁrst ﬁve principal components as well as lateral line scale count. All populations (allopatric and sympatric) were included in this analysis (N = 582).

PC 1 1.143 0.2855 PC 2 4.153 0.04201 PC 3 27.59 < 0.0001 PC 4 36.891 < 0.0001 PC 5 5.3246 0.02138 Lateral line scale count 58.966 < 0.0001

Table 2.10: F-ratios and P-values for ANOVAs comparing longnose dace (Rhinichthys cataractae) samples from east and west of the Rocky Mountains for the ﬁrst ﬁve principal components as well as lateral line scale count. Only allopatric longnose dace populations were included in this analysis (N = 290.)

PC 1 1.2895 0.2395 PC 2 42.714 < 0.0001 PC 3 10.239 0.001529 PC 4 30.536 < 0.0001 PC 5 1.4026 0.2373 Lateral line scale count 2.8404 0.09301

2.4 Discussion

The genus Rhinichthys has a broad North American distribution (Matthews et al., 1982), and several studies have been dedicated to the systematics and biogeography of blacknose dace (R. atratulus, Matthews et al., 1982; Tipton et al., 2011) and speckled dace (R. osculus, Smith and Dowling, 2008; Billman et al., 2010). Similar work has also been done on Rhinichthys

41 2.4. Discussion cataractae, the longnose dace, which is the most widely distributed species in the genus, and indeed the most widely distributed native North American minnow (McPhail and Taylor, 2009); it occupies varied habitats with differing geological histories and is potentially an excellent study system with significant undiscovered diversity (Girard and Angers, 2006). The Umpqua (R. evermanni) and Millicoma dace (R. cataractae spp.) have already been identified as distinct species within the R. cataractae species group (McPhail and Taylor, 2009), and the Nooksack dace, endemic to southwest British Columbia and northwest Washington State, has been identified as a fourth clade with mtDNA distinct from the longnose dace (McPhail and Taylor, 2009). The NSD and LND mitochondrial clades co-exist in three streams in the lower Fraser Valley, presumably from postglacial secondary contact, and the confirmation of two divergent morphological groups in sympatry would lend support to the idea that the Nooksack dace and longnose dace are separate species; however, my results show minimal differences in morphology, which suggests a lack of reproductive isolation between two lineages which have been separated for potentially millions of years.

2.4.1 Subtle morphological variation My morphometric analysis showed that there are very subtle morphological differences between the Nooksack and longnose dace. Nested ANOVAs conducted on the first five principal components of allopatric populations showed that both putative species and sampling location had significant effects on all PCs; however, in each case, location accounted for a higher proportion of the total variance than putative species did. The principal components themselves were not highly informative, with PC 1 and PC 2 accounting for only 18.3% and 12.8% of the variance, respectively. Attempts to interpret the character loadings of the PCs did not yield any insight; PC 1 was dominated by two body-width measurements (M1, M2) and two diagonal body measurements (M5, M7), but only one of the measurements (M6) has a high enough loading to be considered “high association” (loadings > 0.71 or < −0.71), i.e., those characters which make large contributions to the PC (see Tabachnick, Fidell, et al., 2001). Indeed, among the first five principal components, only two measurements can be considered ”high association” traits. This suggests that morphological variation between NSD and LND is subtle and involves the overall shape of the fish rather than being dominated by a few dimensions. The one morphological character identified by McPhail (1967) to differ

42 2.4. Discussion consistently between NSD and LND was lateral line scale count. My analysis did indeed show that lateral line scale counts fell into two groups for NSD and LND; however, the scale counts overlap significantly and cannot be used as a diagnostic character. Sympatric populations have intermediate values for this important meristic, suggesting that they are introgressed. The cluster analysis was designed to identify any clustering within the data. If the LND and NSD were strongly differentiated morphologically, the cluster analysis should have identified two groups. However, in the cluster analysis of all dace populations, five clusters were identified, each composed of a broad mixture of samples from allopatric NSD and LND sites as well as sympatric sites. The exception was cluster 5, which was composed solely of Nooksack dace from the Wynoochee River. Cluster analysis of the allopatric populations revealed six clusters, with each being composed of both NSD and LND, with the exception of cluster 6, which was composed solely of Nooksack dace from Wynoochee River. However, each cluster in this analysis was clearly dominated by either NSD or LND, whereas clusters from the analysis of all populations were ambiguous in composition, and likely do not represent meaningful morphological clusters. For both analyses, when the association between cluster and putative species was tested with a contingency table, it was found to be highly significant. These results, as well as the results of the nested ANOVAs, suggest that while putative species does have an effect on morphology, it is not strong, and sampling location may have a larger effect—for example, Wynoochee River samples were very distinctive in all morphological analyses, possibly due to unique environmental conditions, genetic drift, or a small and distinctive founding population. R. cataractae is a small, benthic freshwater fish with very specific habitat requirements. It is a lithophilic, relatively sedentary fish, and like all freshwater fish, it is restricted to its drainage. Thus, different populations may remain isolated for long periods of time, and adaptations to local conditions may develop. Differences in stream conditions, prey availability, and preda- tor presence may all cause slight morphological adaptations; certain streams, such as Wynoochee River, may be more distinctive and cause greater divergence in their population. Alternately, as relatively small populations may remain isolated for some time, genetic drift and/or founder effects may play a role, and streams such as Wynoochee River may have been isolated for a longer period of time. The cluster analysis is sensitive to all sources of variation and will form clusters “blindly.” Discriminant analysis, on the other hand, uses a priori information about the grouping of interest to create a function that max-

43 2.4. Discussion imizes the difference between these groups—in this case, putative species. Indeed, discriminant analysis was much more successful than cluster analysis, successfully identifying 91.7% of allopatric LND and 80.6% of allopatric NSD. This also suggests morphological differentiation between the two fish, which becomes more evident with an analysis that is able to focus on variation due to putative species as opposed to location or other factors, and falls within the commonly-suggested 75% rule for subspecies. Again, however, given that the discriminant analysis had between 8.3% and 19.4% error, even multivariate analysis does not provide a truly diagnostic tool. Analysis of sympatric samples from Kanaka Creek with matched morphology and mtDNA revealed no association between mtDNA type and morphology. Lateral line scale counts for sympatric samples had a unimodal distribution with complete overlap between NSD and LND mtDNA types. The analyses of variance on each PC for the Kanaka Creek samples were also non-significant. Cluster analysis assigned three clusters, two with both NSD and LND, and one with only NSD, but only two fish were contained in this NSD exclusive grouping. A contingency test revealed no association between mtDNA type and cluster, unlike the analysis on allopatric populations (see above). Finally, when the discriminant function trained on allopatric populations was applied to Kanaka Creek samples, it assigned only 38.7% of samples with NSD mtDNA to the NSD morphogroup, and 69% of samples with LND mtDNA to the LND morphogroup, with a total of 47.7% of fish assigned to the morphogroup that matched their mtDNA—approximately the same as would be achieved by chance. Compared to the overall 88.4% success rate of the discriminant function on allopatric samples, this strongly suggests that there is no association between mtDNA and morphotype in streams with sympatric dace. Bailey et al. (1954) suggested that subspecies criteria should be that at least 93% of the individuals from each allopatric population differ from individuals from the other allopatric population; LND and NSD do not meet this standard. The intermediacy of Kanaka Creek samples in both lateral line scale count, and the lack of association of mtDNA with morphotype, supports the idea that they are an introgressed population, with NSD and LND interbreeding readily when they come into contact.

2.4.2 Alternative explanations for morphological variation Though contingency tests of the cluster analysis did indicate that there was an association between cluster and putative species, there was clearly variation that could not be explained by putative species. Morphological

44 2.4. Discussion variation is affected by both genetic and environmental factors, and the relatively small range of morphological variation observed in the Nooksack and longnose dace could be due to environmental variation across the wide sampling range. Indeed, sampling location had significant effect on all PCs. However, there was no meaningful association between the principal component scores and any of the variables that I tested. Though there were several significant linear models, the proportion of variance explained by environmental variables was low, and the slopes of the linear models were generally small, indicating relationships that are probably not biologically relevant. These slight correlations with environmental variables do not appear to be informative, particularly after taking into consideration the relatively low contributions of each principal component to the total morphological variability. Although my analysis showed no clear effect of environment upon morphology, it may be premature to draw the conclusion that dace morphology does not vary by environment. The environmental data I was able to use was limited in their number and were only coarse-grained measures of environmental variation (i.e., measured at the watershed level whereas dace were collected at the site level). R. cataractae is a habitat specialist, occupying only those portions of streams which suit their needs (fast-flowing shallow riffles), and it is likely that microhabitat variation plays a much greater role than large-scale environmental differences. Further study in this area should make finer-scale measurements of dace habitat. For instance, relevant habitat parameters to measure at the site of capture include water depth, rate of flow, average size of substrate, as well as community composition: preda- tors and competitors, and abundance of different prey. Baltz et al. (1982) showed that speckled dace were found in numbers inversely proportionate to the number of a competitor, the riffle sculpin (Cottus gulosus), in a given riffle, and Wood and Bain (1995) demonstrated morphological variation by microhabitat use in several stream fish. Comparison of Rhinichthys from east and west of the Rocky Mountains did yield significant results, even when restricted to allopatric LND populations. R. cataractae from east of the Rocky Mountains may have been isolated in a separate glacial refugium, and have different mating behaviour, mating during the day, while western dace mate at night (Bartnik, 1972). The morphological differentiation, different mating behaviour, and divergent mtDNA (3-8%, Taylor et al. unpubl. data) suggest that dace east of the Rocky Mountains are quite divergent from those west of the Rocky Moun- tains, and merit further study particularly because these mountains have been shown to separate divergent lineages in a variety of taxa (e.g. wood

45 2.4. Discussion frogs (Rana sylvatica), Lee-Yaw et al., 2008).

2.4.3 Implications The Nooksack dace was isolated in the Chehalis refugium, probably for millions of years during the Pleistocene; however, only subtle morphological divergence from the longnose dace occurred. There has also been little morphological divergence between other dace within the R. cataractae group, or across the geographical range of R. cataractae generally (Bisson and Reimers, 1977). In contrast, the speckled dace (R. osculus) exhibits considerable morphological variation across its range (Oakey et al., 2004), yet has still revealed cryptic diversity within Oregon’s Great Basin (Hoekzema and Sidlauskas, 2014). Like R. cataractae, R. osculus has experienced repeated range fragmentation and remerging during the Pleistocene glaciations. This type of phylogeographic history, with species of limited dispersal ability being divided by a fragmented landscape, has been suggested as one mechanism for generating genotypic differentiation without accompanying morphological differentiation. That R. osculus displays greater phenotypic variation across its range may be reflective of its more generalist nature: though the two fish have similar diets, R. cataractae is adapted for specialization in fast-flowing stream riffles, whereas R. osculus is found in a wide range of habitats, from small streams to deep lakes. Specialization is thought to con- strain phenotypic variation, and though R. cataractae is not so extreme a specialist as fish like the African butterflyfish (Pantodon buchholzi), which provides one of the strongest examples of morphological stasis in extant vertebrates (Lavouéet al., 2011), it may still explain why its morphology appears less geographically variable than that of other dace. With regard to the taxonomic status of Nooksack and longnose dace, there does appear to be some subtle morphological differentiation between the two lineages, best displayed by the ability of the discriminant function analysis to separate the two groups at an average 88.7% success rate. How- ever, the magnitude of difference is small and there is considerable overlap between all characters. Nor was any differentiation discovered that hinted at ecological separation. More importantly, sympatric dace were morphologically intermediate, and there was no association between mtDNA haplotype and morphology in streams with sympatric dace. This suggests that the two lineages are interbreeding freely where they come into contact, which argues against assigning species status to the Nooksack dace. Subspecies definitions are contentious and often vague. Subspecies of birds have been described with discriminant function analysis success rate

46 2.4. Discussion of less than 75% (Patten et al., 2002), but different stocks of a single fish species have been discriminated with up to 100% success using discriminant function analysis (Turan et al., 2006). Although the morphological differences between the NSD and LND do put them within the range of being separate subspecies, I think that their status should be evaluated in light of the larger picture; is this morphological differentiation indicative of incipient speciation, and/or adaptation to environmental conditions? In the case of the NSD and LND, I believe the answer to both questions is no, and they should not be classified as subspecies.

47 Chapter 3

Genetic Analysis of Nooksack and Longnose Dace

3.1 Introduction

3.1.1 Background Vicariant events such as glaciation or mountain orogeny commonly separate previously continuous populations, leading to allopatric populations that evolve independently. These different lineages may later come back into secondary contact, as when glaciers recede and previously glaciated areas are recolonized by species from separate refugia (e.g. Tipton et al., 2011). Depending upon factors such as the length of time of separation and the differences in environment and selective pressures acting on the different populations, as well as stochastic processes such as genetic drift, the degree of divergence will vary and may or may not permit interbreeding upon secondary contact. Even if considerable divergence has occurred, when species evolve in allopatry reproductive isolation may be incomplete and hybridization may occur when the species come into secondary recontact (Allendorf and Leary, 1988). The Nooksack dace (NSD) and longnose dace (LND) are representative of two lineages within the Rhinichthys cataractae complex that were separated during the Pleistocene glaciations; LND in the Columbia refugium (among others), and NSD exclusively in the much smaller Chehalis refugium (McPhail and Taylor, 2009). It is not uncommon for lineages isolated in this way to evolve into separate species; indeed, these cycles of glaciation have been proposed as an “allopatric speciation pump” (April et al., 2013). How- ever, this is not always the case, and some lineages may interbreed freely upon secondary contact (e.g. Coregonus artedi, Turgeon and Bernatchez, 2001), but it is difficult to predict whether interbreeding will occur, and to what degree, without observing the lineages in sympatry. There are other lineages within the R. cataractae complex which have been classified as full species (the Umpqua and Millicoma dace, McPhail and Taylor, 2009) but

48 3.1. Introduction no cases of secondary contact have been documented and it is unknown whether barriers to reproduction exist. NSD and LND mitochondrial types are sympatric in three streams in the lower Fraser valley: the Coquitlam River, the Alouette River, and Kanaka Creek. This zone of secondary contact was first investigated using a diagnostic restriction fragment length polymorphism (RFLP) in the mitochondrial cytochrome b gene (Taylor et al., unpubl. data), finding that LND and NSD mtDNA both were present in all three streams. Though both mtDNA types are present, an analysis of nuclear DNA is necessary to eval- uate whether the two types have been interbreeding because mtDNA is haploid and maternally-inherited such that distinct maternal lineages can persist within randomly breeding population (see below). This zone of secondary contact provides a valuable opportunity to see whether reproductive isolation is present between these putative species. Though many definitions of species have been proposed (Hausdorf, 2011), the biological species concept (Mayr, 1942, 1963) is widely accepted as the best option for sexually reproducing organisms. The BSC defines species as “groups of actually or potentially interbreeding natural populations, which are reproductively isolated from other such groups” (Mayr, 1942, 1963). This definition is difficult to test, however, unless the two putative species are sympatric in some part of their range: completely allopatric species can only be tested for reproductive isolation in an experimental setting, which has the potential to influence behaviour. Thus, zones of sympatry between otherwise allopatric species provide important opportunities to test for reproductive isolation in a natural setting and apply the BSC. In the case of lineages which have diverged in allopatry (as opposed to species which have diverged in sympatry and only subsequently separated their ranges), zones of secondary contact provide opportunities to study the mechanisms of allopatric speciation. For example, Redenbach and Taylor (2002, 2003) investigated a zone of secondary contact between two species of Arctic Char, Dolly Varden (Salvelinus malma) and Bull Trout (Salvelinus confluentus), using mitochondrial DNA as well as nuclear DNA markers. They uncovered bimodal hybrid zones with isolation likely being maintained by size assortative mating; however, hybrid genotypes constituted 9% of the population of juvenile char, providing the opportunity for further study of the mechanisms leading to speciation (Redenbach and Taylor, 2003). Because these species each survived in multiple glacial refugia, there was a unique opportunity to study zones of sympatry of different ages: Dolly Varden and bull trout both survived in the Chehalis refugium, and have thus overlapped for potentially

49 3.1. Introduction more than 100,000 years. The two species also have zones of secondary contact between allopatric refugial populations that are much more recent, perhaps 14,000 years old (Redenbach and Taylor, 2002). Diﬀering levels of hybridization in these two zones might suggest a role for reinforcement in driving the development of reproductive isolation (Redenbach and Taylor, 2002). Generally, studying a range of zones of secondary contact across geographical region and taxa, can help understand the phylogeographical and evolutionary processes underlying patterns of diversity.

3.1.2 Genetic techniques Mitochondrial DNA is often the first tool used by evolutionary biologists to study variation between different populations; it is non-recombinant, has a relatively high rate of mutation, an effective population size one-quarter than of biparentally-inherited loci and is easy to isolate and characterize (Birky et al., 1983; Moritz et al., 1987). In fact, mtDNA has been proposed as the sole basis for species description (Avise and Walker, 1999; Hebert et al., 2003) and has been used for > 80% of phylogeographic studies (Avise, 2000). Despite its usefulness, mtDNA surveys have their limitations; for some of the very reasons that make it an appropriate choice for broad population surveys (haploid, maternal inheritance) mitochondrial DNA alone is not sufficient to detect hybridization (Scribner et al., 2000). However, a cytonuclear approach using both mtDNA and nuclear DNA can be a powerful tool for investigating the direction, extent, and evolutionary history of hybridization (Scribner et al., 2000; Avise, 2001; Toews and Brelsford, 2012). Lu et al. (2001) used mitochondrial DNA and nuclear microsatellites (short tandem repeats) to analyze a hybrid zone between lineages of lake whitefish (Coregonus clupeaformis). While mtDNA data indicated little introgressive hybridization between glacial lineages among populations in the St. John River basin zone of secondary contact, microsatellite data revealed extensive hybridization between lineages, and identified some populations as admixed which had been presumed unmixed from mtDNA analysis (Lu et al., 2001). In addition to being less sensitive to admixture than microsatellites, analysis of mtDNA cannot distinguish a past hybridization event between currently reproductively isolated species from ongoing hybridization between incompletely reproductively isolated species. For these reasons, further investigation using nuclear DNA is necessary to clarify the status of the NSD and LND. Microsatellites, or short tandem repeats, are rapidly evolving nuclear

50 3.1. Introduction markers that are commonly used in population genetic studies (Balloux and Lugon-Moulin, 2002). They consist of a short (typically < 5 bp) repeated sequence of DNA; the number of repeats is highly variable, with a given locus typically having from 1–50 alleles (Jarne and Lagoda, 1996). A set of several microsatellites can be used to genotype individuals and distinguish populations, and can be analyzed in a variety of ways (Jarne and Lagoda, 1996; Hedrick, 1999; Balloux and Lugon-Moulin, 2002). Assays of multilocus allele frequencies can provide a powerful indirect test of reproductive isolation between sympatric populations and their status as distinct biological species. For instance, McPhail (1984) use variation at nuclear-encoded allozyme loci to demonstrate reproductive isolation between species pairs of threespine stickleback, Gasterosteus aculeatus. More recently, many studies of sympatric populations of char (Salvelinus), whiteﬁshes, (Coregonus), and killiﬁsh (Fundulus) have used microsatellite assays to test phylogeographic theories, population divergence, and degrees of hybridization and introgression (Turgeon and Bernatchez, 2001; Redenbach and Taylor, 2002; Adams et al., 2006).

3.1.3 Objectives for this chapter In this chapter, I first expanded upon the mtDNA work of Taylor et al. (unpubl. data), who surveyed the mtDNA types of 120 NSD and LND, including 10 each from two of the streams with sympatric dace (Coquitlam River and Alouette River). I wanted to add samples from Kanaka Creek, and make a more extensive survey of mtDNA types in the Coquitlam River and Alouette River, both to have a larger set of samples matched with morphology and to see whether any pattern emerged in the ratio of LND to NSD haplotypes in each stream. I also wanted to confirm that allopatric populations have only one mtDNA type present, and expand the geographic range and number of allopatric populations surveyed. Next, I analyzed populations of Nooksack and longnose dace from three streams with sympatric dace to determine the degree of genetic differentiation between fish characterized morphologically and with respect to mtDNA. For this objective, I assayed allelic variation at 10 microsatellite loci (three from Girard and Angers (2006); seven from Beasley et al. (2014)) to test the hypothesis that the two putative species were genetically distinct and to assess what level of hybridization, if any, occurs between them.

51 3.2. Materials and methods

3.2 Materials and methods

3.2.1 Sampling Fish samples were collected as detailed in Chapter 2. Additional samples represented collections made by others (J.D. McPhail and E.B. Taylor, Uni- versity of BC, Dept of Zoology) and stored in the Beaty Biodiversity Mu- seum tissue collection (see Appendix B, Table B.1 for details of all genetic samples). Samples for DNA analysis consisted of a small fin clip (typically a portion of the caudal fin or one of the pelvic fins) and were stored in 95% non-denatured ethanol.

3.2.2 DNA extraction and amplification Genomic DNA for mtDNA and nuclear DNA analysis was extracted from fin samples. Extractions were performed using Qiagen DNeasy Blood & Tissue Kit and Qiagen QIAamp DNA Investigator Kit spin columns, and DNA was stored at −20 ◦C. For mtDNA analysis, a 600 base pair portion of the cytochrome b gene was amplified using the polymerase chain reaction (PCR) under the following conditions: amplification was carried out in a total volume of 50µL, consisting of 5µL buffer solution, 4µL of dNTP mix, 1µL of each primer, 0.3µL of Taq DNA polymerase, 1µL of DNA, and 37.7µL of sterile distilled water. The primers used for amplification were:

GluDG (5’-TGACTTGAAGAACCACCGTTG-3’; Palumbi et al., 1996) HD (5’-GGGTTGTTTGATCCTGTTTCGT-3’; Dowling et al., 2002)

Amplifications were performed with an initial denaturing at 95 ◦C for 3 minutes, two cycles of denaturation at 95 ◦C for 30 seconds, annealing at 55 ◦C for 30 seconds, and extension at 72 ◦C for 30 seconds, followed by 32 cycles of denaturation at 92 ◦C for 30 seconds, annealing at 54 ◦C for 30 seconds, and extension at 72 ◦C for thirty seconds, followed by a final extension at 72 ◦C for 10 minutes. After PCR, the 600 base pair product was subject to restriction fragment length polymorphism (RFLP) analysis using the restriction enzyme AvaII which produced RFLP profiles diagnostic of the Nooksack dace and the longnose dace and which were identified from fixed differences in the DNA sequences (Taylor et al., unpubl. data). Under these conditions, RFLP

52 3.2. Materials and methods analysis of the ∼ 600 bp fragment produced two fragments of ∼ 500 and ∼ 100 bp Nooksack dace and a single (uncut) fragment of ∼ 600 bp in longnose dace. Some DNAs were of poor quality and resulted in inadequate amplification of the larger 600 bp fragment. Consequently, a smaller 250 bp fragment was amplified using the following primers: forward [5’—TGCCCCGTTAGCATGTATATT—3’] reverse [5’—ACGAAAAACCCACCCACTAA—3’] An annealing temperature of 54 ◦C was used, and all other conditions were the same as for the 600 bp fragment. The RFLP analysis of the ∼ 250 bp product produced fragments of 157 and 93 bp in Nooksack dace, and an (uncut) 250 bp fragment in longnose dace. The resulting DNA fragments were separated on 2.5% agarose gels, stained CyberGreen (Molecular Probes, Inc.) and visualized under ultra- violet light. Nuclear DNA was analyzed using 10 microsatellite markers: Rhca15b, Rhca16, Rhca23 developed by Girard and Angers (2006), and Rhca4, Rhca5, Rhca7, Rhca36, Rhca42, Rhca43, and Rhca45 developed by Beasley et al. (2014) (see Appendix B, Table B.2 for complete primer information). Poly- merase chain reactions were performed in 20uL total volumes using the Qiagen Multiplex PCR Kit using the PCR conditions recommended by the manufacturer, with three multiplexes—MP 1 (Rhca5, Rhca36, Rhca42), MP 2 (Rhca7, Rhca16, Rhca43), and MP 3 (Rhca4, Rhca15b, Rhca45). Rhca23 was run in a separate PCR, with annealing temperature of 55 ◦C and conditions otherwise as above for 250 bp cytochrome b fragment. For the multiplex PCRs, the following modifications were made to manufacturer’s instruc- tions: in MP1, initial activation was followed by two “touchdown” cycles, in which annealing temperature was 58 ◦C. This was followed by 35 cycles with an annealing temperature of 55 ◦C. MP2 was the same as MP1. MP3 had two “touchdown cycles at 60 ◦C followed by 35 cycles at 58 ◦C. All multiplexes used a final extension step of 30 minutes at 60 ◦C. The forward primer of each locus pair was fluorescently labeled to facilitate detection and allele identification using the Beckman-Coulter CEQ 8000 automated genotyper.

3.2.3 Genetic analyses—mitochondrial DNA I compiled mtDNA results to see if there were any populations that had been independently classiﬁed as either Nooksack dace or longnose dace (using morphological data, previously-collected mtDNA data, or geography)

53 3.2. Materials and methods that contained longnose dace or Nooksack dace mtDNA, respectively (i.e., if previously characterized allopatric populations contained only one kind of mtDNA). I also used the mtDNA data to better characterize each sympatric population in terms of the relative proportions of Nooksack and longnose dace mtDNA.

3.2.4 Genetic analyses—microsatellite data Microsatellites were amplified and scored for 374 samples from 12 populations. I used MICRO-CHECKER (Oosterhout et al., 2004) to check for the presence of null alleles and PCR or scoring artifacts that could affect further analyses. I used FSTAT v2.9.3.2 (Goudet, 1995) to compile descriptive population genetic statistics: number of alleles, allelic richness, and observed and expected heterozygosity. Using Genepop v4.2.2 (Rousset, 2012), I tested for deviations from Hardy-Weinberg Equilibrium for each combination of locus and population using an exact test in which probability values were estimated using a Markov chain method. I tested for linkage disequilibrium for all combinations of locus pairs within each population with a Markov chain method using Genepop default values. I used the factorial correspondence analysis in GENETIX v4.05 (Belkhir et al., 2004) as a visual assessment of the clustering of different populations. Factorial correspondence analysis is a type of ordination analysis that uses variation in multilocus genotypes to ordinate individuals in “multiallelic space.” I calculated pairwise FST , as estimated by Weir and Cockerham’s (1968) θ, for each pair of populations to get a general sense of the level of genetic divergence amongst populations of R. cataractae using GENETIX v4.05 (Belkhir et al., 2004). I estimated average pairwise FST for different groupings to see whether sympatric populations were more similar to either allopatric NSD or LND populations, which could indicate bias in introgression, or even that the streams with sympatric dace consist entirely of either LND or NSD in terms of the nuclear genome, which would suggest that the mixed mtDNA represents an event of historical introgression. To do this, I estimated average pairwise FST between one sympatric population and all allopatric LND populations, and average pairwise FST between that same sympatric population and all allopatric NSD populations. I performed this estimation for each sympatric population independently, in case the pattern of introgression was different in different streams. I compared average pairwise FST among allopatric NSD with average pairwise FST among allopatric LND, to see if the overall level of differenti-

54 3.2. Materials and methods ation was different between NSD and LND and tested for a difference using the permutation utility in FSTAT (Goudet, 1995). The second step was to test for association of mtDNA type with nuclear DNA in sympatric samples, which would indicate some level of reproductive isolation. For this, I separated the fish from each sympatric population into two groups, those with LND mtDNA and those with NSD mtDNA. I tested whether mtDNA subgroups in each stream differed from each other by looking at the pairwise FST between LND and NSD subsets for each sympatric population.

3.2.5 Admixture analyses (Structure) I used the model-based Bayesian clustering program Structure v2.3.4 (Pritchard et al., 2000) to estimate the number of genetic populations (K). Structure uses a Bayesian algorithm to identify clusters of individuals based on their genotypes at multiple loci, by finding the arrangement which minimizes Hardy-Weinberg and linkage disequilibria within clusters (Pritchard et al., 2000). I used the admixture model, i.e., where individual fish can represent a composite or admixture of different genetic groups either from ancestral polymorphism or hybridization or both. I ran Structure five times for each model of K = 1-12, for 250,000 MCMC replications after a burn-in period of 50,000. I then used Structure Harvester v0.6.93 (Earl et al., 2012) to determine the most likely K following the method of Evanno et al. (2005). Structure calculates the log probability of the observed data for each value of K and run of the program that is conducted (P r(X|K)); however, this probability is meant only to be used as an ad hoc guide to the most accurate number of clusters (Pritchard et al., 2000). (Evanno et al., 2005) generated genetic data, analyzed it with Structure, and tested the efficacy of different methods in extracting the most accurate number of clusters from the Structure output. Their study found that using ∆K, an ad hoc measurement consisting of the second order rate of change of ln[P r(X|k)] with respect to K, was a better predictor than P r(X|k) for all of the scenarios they tested (Evanno et al., 2005). I first analyzed the full dataset of 12 populations (see Table 3.1, and then I examined each sympatric population independently to further test whether there was any population structure within each stream.

55 3.2. Materials and methods

Table 3.1: Populations of longnose dace (LND) and Nooksack dace (NSD), Rhinichthys cataractae, used in Structure analysis, showing mtDNA haplotypes present in each population and N for each. Populations 5–7 are sympatric, the rest are allopatric. Total N = 374.

Sample No. MtDNA Population N haplotypes present 1 NSD Porter Creek, WA 5 2 NSD Satsop River, WA 25 3 NSD Bertrand Creek, BC 22 4 NSD Brunette River, BC 11 5 NSD, LND Coquitlam River, BC 66 6 NSD, LND Alouette River, BC 99 7 NSD, LND Kanaka Creek, BC 92 8 LND Norrish Creek, BC 11 9 LND Fraser River, BC 6 10 LND Coquihalla River, BC 13 11 LND Beaver Creek, BC 16 12 LND Beaver River, ON 8

3.2.6 Analysis of molecular variance (AMOVA) I used Arlequin v.3.5.1.3 (Excoﬃer and Lischer, 2010) to perform Analysis of Molecular Variance (AMOVA). AMOVA takes into account pre-assigned population structure and partitions variance in allele frequencies into the variance due to (1) pre-assigned group (e.g. putative species group), (2) population within that group, and (3) individual. It can be used to assess the importance of grouping vs. population/sampling location, as well as to compare diﬀerent possible population groupings and see which grouping explains the largest proportion of the variance (e.g. Vonlanthen et al., 2007). I used AMOVA to test the hypothesis that mtDNA types within sympatric samples were more similar to the same mtDNA type in allopatry; i.e. that dace from streams with sympatric dace with NSD mtDNA were more similar to allopatric NSD than to allopatric LND. To do this, I performed AMOVAs under the following grouping hypotheses and compared the proportion of variance explained by each grouping (note: “sympatric LND” refers to sympatric dace that have LND mtDNA; similar for NSD):

56 3.2. Materials and methods

Grouping 1 Hypothesis (Putative species):

1. Sympatric LND + allopatric LND 2. Sympatric NSD + allopatric NSD

Under this hypothesis, if pooling samples under the groups indicated explains most of the variance, this would be consistent with longnose and Nooksack dace being distinct genetic groups (and perhaps distinct species) in both allopatry and sympatry.

Grouping 2 Hypothesis (Freely interbreeding):

1. Sympatric LND + allopatric NSD 2. Sympatric NSD + allopatric LND

If the groups indicated in this hypothesis explains an equal or greater amount of the variance as Grouping 1, it would indicate that dace with LND or NSD mtDNA in sympatry are not similar to those with the same mtDNA in allopatry. I also used AMOVA to test whether sympatric samples bore a greater overall similarity to either allopatric NSD or allopatric LND. To do this, I compared the following two grouping hypotheses:

Grouping Hypothesis 3 (Sympatric samples similar to LND):

1. sympatric samples + allopatric LND 2. Allopatric NSD

Under this hypothesis, if pooling the samples as per the groups above explained more variance than Grouping 4, it would indicate that the dace in streams sympatric for NSD and LND mtDNA are more similar to allopatric LND, which could indicate historical introgression of NSD mtDNA into a primarily LND population.

Grouping Hypothesis 4 (Sympatric samples similar to NSD):

1. sympatric samples + allopatric NSD 2. Allopatric LND

If the groups created under this hypothesis explain a greater proportion of the variance than Grouping 3, it would indicate that dace in streams sympatric for NSD and LND mtDNA are more similar to allopatric NSD.

57 3.3. Results

Lastly, I tested a subset of only allopatric populations, to see how much of the variance was explained by species diﬀerentiation when the potentially complicating sympatric samples were not present.

Grouping Hypothesis 5 (Allopatric populations more highly differentiated):

1. Allopatric LND 2. Allopatric NSD

Under this hypothesis, if the groups indicated above explain the largest proportion of the variance, it would indicate that without the confounding eﬀect of the streams with sympatric dace, species can explain more molecular variance than sampling location.

3.3 Results

3.3.1 Distribution of mitochondrial haplotypes I combined my data with previous mtDNA-based assays of Rhinichthys from Washington State and BC (Taylor et al. unpubl. data). The results show LND and NSD mtDNA are both present in the Coquitlam River, Alouette River, and Kanaka Creek, and that all other streams contain mtDNA from only one of the two putative species (Fig. 3.1). Taylor et al. (unpubl. data) have shown that all samples of R. cataractae east to Qu´ebec (including samples from Alberta, Manitoba, Ontario, and Qu´ebec) and north to my sampling area bear only longnose dace mtDNA (N = 56 from 30 localities, analyzed for cytochrome b and ND2 regions).

3.3.2 Microsatellite analyses Hardy-Weinberg equilibrium In total, I performed 120 tests of Hardy-Weinberg equilibrium (10 loci * 12 populations), and nine were signiﬁcant after a Benjamini and Yekutieli (2001) correction for multiple comparisons (adjusted critical P-value was 0.017). One population had more than one locus out of equilibrium: Alou- ette River (3). Six more populations had one locus out of equilibrium: Sat- sop River, Coquitlam River, Norrish Creek, Coquihalla River, and Beaver Creek (BC). Two loci were out of equilibrium in more than one population: Rhca4 (4) and Rhca7 (3). Two other loci were out of equilibrium in

58 3.3. Results

Figure 3.1: Map of Nooksack dace (black) and longnose dace (white) (Rhinichthys cataractae) cytochrome b mtDNA haplotypes. 1 = Willipa River (N = 5), 2 = Satsop River (N = 25), 3 = Porter Creek (N = 5), 4 = Stilliguamish River (N = 5), 5 = Nooksack River (N = 5), 6 = Brunette River (N = 25), 7 = Coquitlam River (N = 41), 8 = Alouette River (N = 86), 9 = Bertrand Creek (N = 32), 10 = Kanaka Creek (N = 44), 11 = Norrish Creek (N = 40), 12 = Coquihalla River (N = 19).

59 3.3. Results one population: Rhca42 and Rhca5 (see Appendix B, Table B.3 for tests of Hardy-Weinberg equilibrium, and observed and expected heterozygosity, for all locus-population combinations. See also Appendix B, Table B.4 for complete allele frequencies).

Linkage disequilibrium In total, there were 540 tests of linkage disequilibrium (12 populations with 10 loci each; 45 different combinations of loci). I used the method of Ben- jamini and Yekutieli (2001) to adjust for multiple comparisons, and assigned significance at the adjusted P-value of 0.011. Forty tests of linkage disequilibrium were significant. Four populations had more than one locus combination out of equilibrium: Satsop River (2), Bertrand Creek (2), Alouette River (18), and Kanaka Creek (18). Sympatric samples (with the exception of samples from the Coquitlam River) accounted for the vast majority of linkage disequilibrium.

Measures of genetic diversity The number of alleles resolved in a given locus ranged from 18 (Rhca5) to 60 (Rhca7). The number of alleles resolved in a given population ranged from 36 (Brunette River) to 193 (Alouette River); however, the sample sizes per population varied greatly in size (N from 5 to 118) so allelic richness was used as a more appropriate measure of diversity. The highest allelic richness (averaged over loci) was 4.45 in the Fraser River, and the lowest was 2.41 in Brunette River. Allelic richness was 4.05 among allopatric LND populations, 3.60 among allopatric NSD populations, and 4.05 among sympatric populations (Table 3.2). The highest average heterozygosity (averaged over populations) was found at locus Rhca43 (0.92) with the lowest at Rhca5 (0.55). Averaged across loci, the observed and expected heterozygosity were highest in the Coquihalla River (0.86, 0.88) and lowest in Brunette River (0.47, 0.45, see Table 3.2 and Appendix B, Table B.3).

Factorial correspondence analysis The factorial correspondence analysis provided an exploratory ﬁrst look at the variation in microsatellite data. There were three major outlying groups: Beaver Creek (BC), Beaver River (ON), and Satsop River (WA) (Fig. 3.2). All other samples were roughly clustered in one group.

60 3.3. Results , N : observed Allelic richness O H ; A IS N F E H O H : total number of alleles across 10 loci. A N : inbreeding coeﬃcient; 95% CI: 95% conﬁdence interval of IS F : expected heterozygosity; E H Stream N FIS Latitude Longitude Pop No. 123 Porter Creek, WA4 West Fork Satsop River,5 WA Bertrand Creek, BC 256 Brunette River, BC7 -0.03 Coquitlam River, BC8 Alouette 5 River, 47.0598 BC9 Kanaka Creek, BC 22 123.5410 0.14310 Norrish Creek, BC 11 -0.00411 0.8068 46.9465 Fraser 66 River, BC Coquihalla 49.0043 0.8299 River,12 0.034 BC 123.2954 0.013 Beaver 135 118 Creek, 122.5322 BC 49.2414 49.0416 4.2709 Beaver 0.7825 0.023 River, 92 ON 0.7744 122.8959 0.6809 122.7711 49.2392 11 0.7773 0.013 52 13 0.4697 122.5793 90 0.8282 0.05 49.2022 6 0.4545 3.9361 -0.016 0.8180 3.7824 0.8192 36 122.5413 49.3885 16 49.2349 177 0.041 0.8012 2.4093 4.2129 8 121.4334 0.7472 -0.003 122.1336 193 49.3831 0.7373 49.1009 4.1581 0.8610 0.7171 0.182 122.4522 159 117.5572 0.8819 0.6792 3.7626 44.3089 0.8286 99 67 0.7726 0.7966 79.0405 4.3858 0.7750 3.5469 71 90 0.8057 4.4498 0.7143 3.8832 65 3.9765 number of individuals sampled; heterozygosity; Table 3.2: Summary of genetic data for the 12 locations used in microsatellite analysis. Abbreviations:

61 3.3. Results from 12 locations. Circle 1 indicates samples from Beaver Creek Rhinichthys cataractae Figure 3.2:Nooksack Factorial correspondence dace analysis (NSD) plot(LND, based BC) on and 10Satsop circle microsatellite River loci 2 (NSD, for indicates WA).River longnose All samples (NSD, dace others from BC), (LND) represent Beaver Coquitlam and Porter(NSD River River Creek and (LND, (NSD LND, (NSD, BC), ON). and WA), Bertrand Norrish LND, Red Creek Creek BC), squares LND, (NSD, Alouette (BC), indicate BC), River Fraser samples Brunette River (NSD from (LND, and BC), LND, and BC), Coquihalla Kanaka River Creek (LND, BC).

62 3.3. Results

Genetic divergence among samples

Comparisons of pairwise FST demonstrated that sympatric dace were equally differentiated from allopatric NSD and LND, and that allopatric populations of LND and NSD were more similar to sympatric populations than they were to each other. Across all pairwise comparisons, FST averaged 0.130 (all P < 0.05), and ranged from 0.023 to 0.346. The average pairwise FST of allopatric LND populations compared to allopatric NSD populations was 0.169. The average pairwise FST of Coquitlam River (sympatric site) compared to allopatric NSD populations was 0.0972, and compared to LND populations was 0.0958. For Alouette River (sympatric site), average pairwise FST was 0.1018 compared to allopatric NSD, and 0.0889 compared to LND. For Kanaka Creek (sympatric site), average pairwise FST was 0.1064 compared to allopatric NSD, and 0.1276 compared to LND. Average pairwise FST among sympatric populations was 0.041 (see Appendix B, Table B.5 for all pairwise FST values.) I also compared the level of divergence among allopatric NSD populations and allopatric LND populations using the permutation utility in FSTAT. With this, the average FST among allopatric NSD populations was found to be 0.159, and among allopatric LND populations, 0.129, with P = 0.669. This result is not significant, and may be confounded by differences in sampling locations; however, as the LND populations cover a much larger area (ranging from British Columbia to Ontario) than the NSD populations, it seems worth noting that despite this, FST is higher among NSD populations. There was no suggestion of population structure within sympatric populations; mtDNA clades within sympatric populations were not differentiated from each other with respect to microsatellite allele frequencies. For calculations involving the mtDNA-divided subsets within each sympatric population, FST between dace with LND mtDNA and those with NSD mtDNA in each sympatric population was −0.001 for Coquitlam River (P = 0.509), 0.003 for Alouette River (P = 0.152), and 0.001 for Kanaka Creek (P = 0.421) (see Appendix B, Table B.6 for all pairwise FST values).

3.3.3 Admixture analysis Structure analysis on samples from all 12 populations revealed structur- ing by location, and no clear pattern of admixture in the sympatric populations. Geographically distant populations tended to form independent genetic clusters. Analysis of streams with sympatric dace individually indicated strongly that Coquitlam River and Kanaka Creek were each comprised

63 3.3. Results of a single population, while analysis of Alouette River revealed a random (according to mtDNA type) admixture of three groups in each sample. The analysis by Structure (Pritchard et al., 2000) across all samples found K = 11 to have the highest likelihood score and K = 3 to be the most likely, according to the Evanno method (Evanno et al., 2005, Table 3.3).

Table 3.3: Evanno table output from Structure Harvester (Earl et al., 2012) for Structure analysis conducted on all 12 longnose and Nooksack dace (Rhinichthys cataractae) populations, testing assumed number of populations K = 1–12, with 5 repetitions, and a total N = 374. K = number of populations assumed; Reps = number of times the simulation was run for a given K; Mean LnP (K) = mean log likelihood of K over all reps for that K; Stdev LnP (K) = standard deviation for LnP (K) over all reps for that K; Ln0(K) = first order rate of change of mean LnP (K), defined as LnP (K) − LnP (K − 1); |Ln00(K)| = second order rate of change, defined as Ln0(K + 1) − Ln0(K); ∆K = |L00(K)| divided by Stdev LnP (K). The highest log likelihood is indicated in bold (K = 11) and the highest ∆K is highlighted in grey.

K Reps Mean Stdev Ln0(K) |Ln00(K)| ∆K LnP (K) LnP (K) 1 5 −18619.7 0.648074 — — — 2 5 −17943.4 4.368066 676.3 201.02 46.020368 3 5 −17468.12 4.275746 475.28 324.28 75.841744 4 5 −17317.12 173.814174 151 280.14 1.611721 5 5 −16885.98 62.246663 431.14 278.76 4.478312 6 5 −16733.6 70.331536 152.38 20.92 0.297448 7 5 −16560.3 28.745956 173.3 23.82 0.828638 8 5 −16410.82 16.087013 149.48 59.36 3.689933 9 5 −16320.7 16.993675 90.12 52.8 3.107038 10 5 −16283.38 29.826532 37.32 25.38 0.85092 11 5 −16220.68 18.157147 62.7 169.66 9.343979 12 5 −16327.64 213.393025 -106.96 — —

For the K = 3 model, allopatric Nooksack dace from Washington State (localities 1 - 2 in Fig. 3.3) and all allopatric longnose dace (localities 8– 12) tended to form one group (dominated by grey in Fig. 3.3. Two of

64 3.3. Results the three sympatric localities (Alouette and Coquitlam), and two allopatric NSD localities (Bertrand Creek and Brunette River) tended to be dominated by another genetic group (largely white in Fig. 3.3), and the remaining sympatric locality (Kanaka Creek) was dominated by the third genetic group (black in Fig. 3.3). The model with the highest log likelihood, K = 11, also grouped the two Washington populations together (blue in Fig. 3.4). It assigned samples from Bertrand Creek and the Brunette River two largely exclusive genetic clusters (pink and red in Fig. 3.4). Norrish Creek, Fraser River, and Coquihalla were grouped together, whereas Beaver Creek and Beaver River each formed largely exclusive clusters of their own (mauve, tan, and yellow, respectively, in Fig. 3.4). The three sympatric populations had much more admixture between clusters than the allopatric populations, but roughly speaking, Co- quitlam River samples were mostly one cluster (light blue); Alouette River was a mixture of two (green and orange); and Kanaka Creek was a mixture of two (green and purple, see Fig. 3.4).

65 3.3. Results ) to the genome of each ﬁsh by one of three genetic groups Q ), K = 3. Each ﬁsh is represented by a thin vertical line and run showing admixture analysis from 10 microsatellite DNA loci in 374 Rhinichthys cataractae Structure Figure 3.3: Output of Nooksack and longnose dace ( each colour represents the proportional(white, contribution black ( and grey).graph, and Populations putative species are is arranged indicated from above. west to east. Population name is indicated below the

66 3.3. Results ) to the genome of each ﬁsh by one of eleven genetic Q ), K = 11. Each ﬁsh is represented by a thin vertical line run showing admixture analysis from 10 microsatellite DNA loci in 374 Rhinichthys cataractae Structure Figure 3.4: Output of groups. Population name is indicated below the graph, and putative species is indicated above. Nooksack and longnose daceand each ( colour represents the proportional contribution (

67 3.3. Results

The Structure analysis conducted within each sympatric population separately revealed no evidence for more than one genetic group. For Kanaka Creek the highest mean log likelihood was at K = 1 (−3221.98), with K = 2 at −3564.36 as the next most likely (Table 3.4. For the Coquitlam River, the highest mean log likelihood was also K = 1 (−2747.7), with K = 2 at −2749.9 being the next most likely (Table 3.4). Bar plots for each of these two localities for any K > 1 showed equal contributions of each genetic cluster to each individual in the population; i.e. each ﬁsh showed the same proportional contribution of each hypothetical genetic group (see Fig. 3.5). For these populations, the ∆K-based method did not necessarily agree for most likely K, but this method is not appropriate in situations where a model of K = 1 has the highest likelihood, as ∆K can only be calculated for K > 1 (Evanno et al., 2005).

Figure 3.5: Output of Structure run showing admixture analysis with K = 3 from 10 microsatellite DNA loci in 66 R. cataractae from the Coquitlam River, a site sympatric for longnose and Nooksack mtDNA haplotypes. Each ﬁsh is represented by a thin vertical line and each colour represents the proportional contribution (Q) to the genome of each ﬁsh by one of three genetic groups (white, black and grey).

Analysis of Alouette River also had K=1 with the highest mean log likelihood (−4048.22), with K=2 as the next most likely (−4289.52) as the most likely (Table 3.4). Unlike Coquitlam River and Kanaka Creek, however, at K > 1, Alouette River samples showed variation in admixture between genetic groups. However, in the bar plot for K = 3, considered the best model using the Evanno method (Evanno et al., 2005)), there was no clear population structure; diﬀerences in admixture levels seem randomly distributed according to mtDNA type (Fig. 3.6).

68 3.3. Results

Figure 3.6: Output of Structure run showing admixture analysis with K = 3 from 10 microsatellite DNA loci in 99 R. cataractae from the Alouette River, a site sympatric for longnose and Nooksack mtDNA haplotypes. Each ﬁsh is represented by a thin vertical line and each colour represents the proportional contribution (Q) to the genome of each ﬁsh by one of three genetic groups (white, black and grey). Samples are arranged by mtDNA type, with NSD on the left (N = 25) and LND on the right (N = 74).

69 3.3. Results

Table 3.4: Evanno table outputs from Structure Harvester (Earl et al. 2012) for Kanaka Creek (A, N = 118), Coquitlam River (B, N = 66), and Alouette River (C, N = 99). Each analysis was conducted for assumed number of populations K = 1–6, with 5 repetitions. K = number of populations assumed; Reps = number of times the simulation was run for a given K; Mean LnP(K) = mean log likelihood of K over all reps for that K; Stdev LnP(K) = standard deviation for LnP(K) over all reps for that K; Ln’(K) = first order rate of change of mean LnP(K), defined as LnP(K)-LnP(K-1); —Ln”(K)— = second order rate of change, defined as Ln’(K+1)- Ln’(K); Delta K = —L”(K)— divided by Stdev LnP(K). For each stream, the highest log likelihood is indicated in bold (K = 11) and the highest Delta K is highlighted in grey.

A K Reps Mean Stdev Ln0(K) |Ln00(K)| ∆K LnP (K) LnP (K) 1 5 −3221.98 0.898332 — — — 2 5 −3564.36 148.174671 −342.38 73.56 0.496441 3 5 −3833.18 112.173134 −268.82 368 3.280643 4 5 −3734 119.309828 99.18 50.02 0.419245 5 5 −3584.8 63.632853 149.2 241.48 3.794895 6 5 −3677.08 61.369756 −92.28 — —

B K Reps Mean Stdev Ln0(K) |Ln00(K)| ∆K LnP (K) LnP (K) 1 5 -2747.72 0.576194 — — — 2 5 −2749.92 3.541469 −2.2 3.22 0.909227 3 5 −2755.34 2.050122 −5.42 24.98 12.184641 4 5 −2785.74 29.898043 −30.4 30.62 1.024147 5 5 −2785.52 45.731302 0.22 65.3 1.427906 6 5 −2850.6 111.621414 −65.08 — —

C K Reps Mean Stdev Ln0(K) |Ln00(K)| ∆K LnP (K) LnP (K) 1 5 −4048.22 0.870057 − − − − − − − − − 2 5 −4289.52 111.020885 −241.3 50.96 0.459013 3 5 −4581.78 162.750075 −292.26 166.48 1.022918 4 5 −4707.56 328.296403 −125.78 195.22 0.594646 5 5 −4638.12 82.209866 69.44 60.28 0.733245 6 5 −4628.96 33.67704 9.16 − − − − − −

70 3.3. Results

3.3.4 AMOVA analysis In general, all of the AMOVAs suggested that sampling location accounted for a much greater proportion of the variance than did grouping by putative species (Table 3.5.) None of the AMOVA analyses suggested that NSD and LND were broadly distinct in microsatellite DNA allele frequencies after accounting for diﬀerences among populations within putative taxa, though the comparison of groupings 3 and 4 suggested that sympatric populations may be more genetically similar to NSD than LND. Groupings 1 and 2 compared “matching” and “mismatched” combinations of mtDNA clades from sympatric populations, and allopatric populations (e.g. grouping 1 consisted of sympatric LND and allopatric LND grouped together, and sympatric and NSD and allopatric NSD grouped together, where as grouping 2 combined sympatric LND with allopatric NSD.) Both arrangements explained essentially zero percent of the variation (Ta- ble 3.5), indicating that the mtDNA clades within sympatric populations are not more closely associated with either parental species in allopatry. Groupings 3 and 4 lumped the sympatric dace with either allopatric LND (grouping 3) or allopatric NSD (grouping 4), to test whether the sympatric dace were more similar to either LND or NSD allopatric populations. Surprisingly, this comparison showed that when sympatric dace were combined with allopatric NSD, group explained ten times the variation that was explained by combining them with LND (Table 3.5). Grouping 5, which was conducted on allopatric populations alone, demon- strates that even in this situation, which would be expected to maximize the amount of variation explained by grouping because the allopatric NSD and LND are presumably more diﬀerentiated from each other than the sympatric dace, population still explains six times as much of the variance as putative species group. Interestingly, combining the sympatric dace with allopatric NSD explains a greater proportion of the variance than does Grouping 5, but these arrangements are not directly comparable because they do not use the same number of populations.

71 3.3. Results Within individuals Among individuals within populations Among populations within groups groups P-valueP-value 0.21577P-value 0 0.69544P-value 0 0.17039P-value 0 0.00322 0.43973 0 0.01687 0.44816 0 0 0.4933 0 0.51116 0 0.55531 0 0 Grouping12 Among % of variation3 -0.01 % of variation4 -0.76 9.79 % of variation5 0.29 10.23 % of variation 3.17 % 9.17 of 0.24 variation 2.44 0.24 8.14 14.69 0.05 89.97 90.28 0.05 0 90.49 88.65 82.86 Table 3.5: AMOVA resultsvalue are for presented groupings for all 1–5individuals four (see within levels: populations, pages among and 59–60 pre-assigned within groups, for individuals. among deﬁnitions). populations within Percentage those of groups, among variation and P-

72 3.4. Discussion

3.4 Discussion

Allopatric speciation, while widely considered to be the main form of speciation (Mayr, 1963; Coyne and Orr, 2004), is difficult to study in nature. When different species are completely allopatric, it is impossible to put the biological species concept to the test, and thus allopatric species are assessed using a variety of different criteria: morphological divergence, ecological divergence, and increasingly, genetic divergence. An overall increase in the ease, power, and affordability of genetic data has led to initiatives such as DNA barcoding, which uses percentage sequence divergence at a standardized section of the cytochrome c oxidase gene (COI) to draw preliminary species boundaries based upon genetic data alone (Hebert et al., 2003; Zem- lak et al., 2009). Particularly when direct tests of reproductive isolation are not available, assessing species status should be done using as many forms of evidence as possible (Sites and Marshall, 2004; De Queiroz, 2007), and in- terpreted with knowledge specific to the taxa, environment, and geological history. In North America, the Pleistocene glacial cycles repeatedly fragmented the landscape, particularly for freshwater fish (April et al., 2013), splitting previously contiguous lineages into many allopatric and parapatric populations which are beginning to be uncovered by phylogeographic studies. Studies of zones of secondary between such separated lineages give the opportunity to examine levels of reproductive isolation and can shed light on the effects of similar isolation upon taxonomically or geographically related species. Nooksack (NSD) and longnose (LND) dace have a level of genetic divergence that establishes them well within the range of many “good” species (2– 3% sequence divergence of mitochondrial cytochrome b and ND2 mtDNA, indicating divergence 2–3 mya—Taylor et al., unpubl. data; % divergence range for fish species, April et al., 2011), but my results indicate that there is no reproductive isolation between the two where they come into secondary contact. There is also no evidence to support the hypothesis that the sympatric populations are the result of an event of historical introgression of one mtDNA type into a largely pure population of either LND or NSD; LND and NSD are thoroughly admixed within streams throughout the zone of secondary contact, and there is no indication that Coquitlam River, Alouette River or Kanaka Creek are more similar to either putative parental species. This represents the only study of secondary contact in the Rhinichthys cataractae species group, and one of the only genetic studies of secondary contact involving lineages preserved in the Chehalis glacial refugium (see also Redenbach and Taylor, 2002). As such, it can provide

73 3.4. Discussion valuable perspective on the process of diversiﬁcation and development of reproductive isolation among taxa whose range was fragmented during the Pleistocene.

3.4.1 Similarity of sympatric R. cataractae to allopatric parental lineages Partial or complete introgression of mtDNA from one taxon into another without accompanying introgression of nuclear DNA is seen frequently in nature and can be caused by selection, sex-biased dispersal, or chance (Toews and Brelsford, 2012). Studies of the Carpathian newt (Lissotriton montandoni, Zielińskiet al., 2013) and lake trout (Salvelinus namaycush, Wilson and Bernatchez, 1998) suggest that in both cases, a historical in situ hybridization event led to introgression of a second mtDNA type, which then became fixed by selection or by chance. However, though a similar situation was a possibility for R. cataractae, all of my results reject this hypothesis and instead point to admixture between NSD and LND in the sympatric populations. Comparisons of pairwise FST showed that sympatric populations are equally isolated from allopatric LND and NSD populations, and that allopatric LND and NSD are each more similar to sympatric dace than they are to each other. Both of these results strongly suggest that the sympatric populations are not composed of solely LND or NSD, but rather a mixture of the two. Structure analysis, although it did not show a clear picture of admixture between LND and NSD, did not cluster the sympatric populations with either putative parental species; instead, it tended to isolate each sympatric population into a separate group. The AMOVA analysis did indicate, contrary to analyses of pairwise FST , that sympatric populations were more similar to allopatric NSD than LND. The AMOVA grouping of sympatric dace + allopatric NSD explained a much larger proportion of the variance than did grouping sympatric dace with allopatric LND (3.44% vs. 0.29% of the total variance). Comparisons of average pairwise FST showed no difference for comparisons of sympatric populations with allopatric LND or NSD (0.1045 for LND, 0.1018 for NSD). On the other hand, Structure analysis of all populations at K = 3 grouped the Coquitlam River and Alouette River with two allopatric NSD streams: Bertrand Creek and Brunette River. These results may indicate that NSD made a greater overall contribution to the sympatric populations’ gene pool; however, the difference appears to be slight. Any imbalance may be due to selection, drift, imbalanced initial colonization, bias in ongoing migration

74 3.4. Discussion from pure streams into streams with sympatric dace, or sampling eﬀects, but the limitations of this study do not allow further conclusions to be drawn.

3.4.2 Admixture of LND and NSD in sympatric populations There appears to be no reproductive isolation between NSD and LND where they come into secondary contact. My results indicated that there was no population structure within any of the streams with sympatric dace, and no association of mtDNA type with nuclear DNA. Comparisons of pairwise FST involving subsets of the sympatric samples divided by mtDNA type indicated that the two types were completely intermixed within each stream and that mtDNA types of sympatric samples are not more closely associated with the nuclear genotype of either NSD or LND. Structure analysis of each sympatric population corroborated these results: there was no evidence of population structure. Kanaka Creek and Coquitlam River each consist of one population, with each individual showing completely uniform admixture levels when a higher K is forced. In the Alouette River, K = 1 is supported by log likelihood, while K = 3 is supported by the Evanno method, and there is some diﬀerentiation among the individuals; however, there is no pattern to the levels of admixture, and analysis of the Alouette population by mtDNA type did not reveal any association of microsatellite DNA admixture levels with mtDNA. That the individuals in the Alouette River are less uniform, according to Structure analysis, than those in Kanaka Creek and Coquitlam River (high variability in Q, whereas Q is nearly equal for all samples in Kanaka Creek and Coquitlam River) may indicate that the Alouette River population is more recently introgressed; the Alouette River is located between the Coquitlam River and Kanaka Creek, but its outlet is located approximately 4 km up the Pitt River, while the Coquitlam River and Kanaka empty directly into the Fraser. It may have taken dace longer to colonize the Alouette and thus introgression may be more recent and the population not as homogenous. Overall it would appear that the LND and NSD have not acquired any level of reproductive isolation. Given that their degree of mtDNA divergence (2–3%) puts them within range of many well-isolated separate species (e.g. April et al., 2013), this raises questions regarding why they have not developed any barriers to reproduction, and whether the length of isolation inferred from mtDNA % divergence (2–3 million years) is accurate.

75 3.4. Discussion

Several studies in birds have uncovered similar situations of populations with deeply divergent mtDNA lineages breeding without any reproductive isolation (Webb et al., 2011; Hogner et al., 2012). Webb et al. (2011) investigated cryptic lineages of the Common Raven (Corvus corax). These birds are widespread across the Northern Hemisphere and consist of two deep mitochondrial clades, Holarctic and Californian, which are over 4% divergent in mtDNA coding genes. In the western United States, the two clades form a large zone of secondary contact, and in Washington State Webb et al. (2011) conducted a comprehensive study of ecology, mate preference, mating success, and offspring survival. They showed that there was no isolation whatsoever between the two lineages, and no difference in offspring survival, concluding that the ravens were experiencing “despeciation,” in which two long-separated lineages, possibly once-separate species or possibly mitochondrial clades differentiated by genetic drift, remerged into one. Webb et al. (2011) have no definitive answer for why the ravens did not become reproductively isolated, but speculate that it may be related to their wide range of ecological tolerance, and/or the conservatism of signal traits in the genus Corvus. These explanations do not seem applicable to the NSD and LND; signal traits and mating behaviours may not be conserved across the range of R. cataractae, with populations east of the Rocky Mountains displaying mating colouration and mating at a different time of day (Bartnik, 1970; Bartnik, 1972). While R. cataractae consume a wide range of prey, they are habitat specialists, adapted in both internal and external morphology for life at the bottom of swift-moving water. While ecological generalism is suggested as a reason why long-separated species might experience neutral sequence divergence without functional divergence or reproductive isolation, ecological specialization has been suggested as a cause of morphological conservatism/stasis (Trontelj and Fiˇser,2008).

3.4.3 Genetic diﬀerentiation throughout the range of R. cataractae The FCA showed that the two most geographically distant LND populations (Beaver Creek, BC, and Beaver River, ON) were by far the most genetically distinct populations, with the other LND populations in BC clustering closely with the sympatric populations as well as the NSD populations. The AMOVA analysis also indicated that sampling location explained a much larger proportion of the variance than did species grouping; this is similar to the results of the nested ANOVA on morphological data (Chapter 2),

76 3.4. Discussion which also showed that location had a greater effect than putative species, even when sympatric populations were not included in the analysis. This is somewhat surprising given the deep divergence of mtDNA between the two lineages, and suggests that isolation by distance has also played an important role. This adds further support to the idea that there is undiscovered diversity in the R. cataractae group (Girard and Angers, 2011), and populations east of the Rocky Mountains may harbour levels of divergence equal to those western clades, particularly as their mating behaviour is diurnal instead of nocturnal (Bartnik, 1972). Another possible explanation is that mitochondrial divergence has been driven by selection along an ecological gradient. Though phylogeographic studies have typically assumed that all markers used are neutral, simulations by Irwin (2012) demonstrate that even weak selection can generate phylogeographic breaks in uniparentally inherited markers, or that such breaks can be generated entirely by neutral processes, particularly if dispersal dis- tances are low and population sizes are small (Irwin, 2002). It seems unlikely in the case of the NSD and LND that mitochondrial divergence has been caused solely by selection, as the break in mitochondrial clades lines up with a known glacial refugium (the Chehalis refugium); additionally, my broad analysis of environmental variables (Chapter 2) did not demonstrate any gradients or breaks corresponding with the ranges of NSD and LND, and in the zone of contact both mitochondrial types are found in the same riffles. However, some combination of weak selection, geographic barriers, and neutral processes may be at work in maintaining the mtDNA break, and any further investigation of ecological factors should consider whether they may be causing selection on mitochondrial genes. Part of the basis for inferring a 2–3 million year separation between the LND and NSD is the congruence of the NSD’s range with the Chehalis glacial refugium. Another fish that survived in the Chehalis refugium is the Salish sucker, an endangered catostomid which forms a designatable unit within the longnose sucker (Catostomus catostomus, COSEWIC, 2013). In many regards the Salish and longnose suckers present an identical case to the Nooksack and longnose dace: a small, specialized freshwater fish, isolated in separate refugia, widespread throughout North America. The Salish and longnose suckers are more morphologically divergent than the NSD and LND, but they are not known to be ecologically differentiated, and differ much less at cytochrome b and ND2 (between 0.8 and 1.2%, McPhail and Taylor, 1999). The two suckers are not known to be sympatric, but are both found in the lower Fraser River and could theoretically intermix; however, as there have been no hybrids detected, it is presumed to be reproductively

77 3.4. Discussion isolated from the longnose sucker (McPhail and Taylor, 1999). The dispar- ity in mtDNA % divergence between the Catostomus pair and Rhinichthys pair points to the complexity of unravelling times of divergence between sister lineages. These two ﬁsh were almost certainly isolated in the same refugia during the Pleistocene, yet one pair’s mitochondrial divergence is much greater. To add another twist, the more deeply diverged pair appears to be less reproductively isolated. Among other possibilities, there may have been intermittent connections between the Chehalis refugium and the Columbia River drainage during the Pleistocene that allowed for diﬀering amounts of contact between the Catostomus pair and Rhinichthys pair; or, as stated above, selection or neutral processes causing the mtDNA break in Rhinichthys to be deeper than predicted by conventional molecular clocks.

3.4.4 Implications for the taxonomic and conservation status of the Nooksack dace Despite their long separation, all of the analyses conducted indicate that there is no population structure in the streams with sympatric dace, and that LND and NSD are freely interbreeding in the zone of secondary contact. The NSD represents a deeply divergent mitochondrial clade and a separate evolutionary lineage of R. cataractae, but they are not reproductively isolated and cannot be considered a separate species under the biological species concept. Under the Canadian Species at Risk Act (SARA), the NSD is listed as an Endangered un-named subspecies of the LND, with a recovery strategy focusing on habitat protection and restoration (Pearson et al., 2008). My study does not necessarily impact the NSD’s status as a designatable unit under SARA, but does argue against full species status for the NSD. My results also suggests that the three streams that are sympatric for NSD and LND mtDNA types should ideally be conserved as a part of the NSD’s range, and a unique evolutionary area.

78 Chapter 4

Conclusion

4.1 Summary of ﬁndings

My study shows that despite subtle morphological differentiation between the Nooksack dace (NSD) and longnose dace (LND) within the Rhinichthys cataractae group, they showed no signs of reproductive isolation in sympatry. Nooksack dace and LND show subtle morphological differentiation, but cannot be reliably discriminated, and the effect of sampling location is stronger than the effect of mtDNA clade. There is overlap between the NSD and LND for all morphometric and meristic characters measured, including the scale counts that first identified differences between NSD and LND (McPhail, 1967). Cluster analysis revealed that some allopatric NSD and LND populations are morphological outliers, but failed to separate NSD and LND from each other, either in sympatry or allopatry. In contrast, discriminant function analysis—which is “trained” on pre-assigned groups— was 88.4% successful in assigning fish to the correct clade (allopatric populations only; 74.9% successful for all populations), indicating that some morphological differentiation exists between LND and NSD. My data suggest that NSD and LND freely interbred in the streams in which they are sympatric; there are no reproductive barriers. In all three streams with sympatric dace there was no evidence for population structure that was associated with mtDNA type. None of the streams with sympatric dace appear to be more similar genetically either to allopatric NSD or LND, and in a Structure analysis the streams with sympatric dace clustered together. For both morphological and genetic variation, geographic location accounted for a greater proportion of the variation than putative species. In general, my study shows that despite potentially millions of years of isolation in separate glacial refugia, and a 2–3% divergence in mtDNA, there has been minimal morphological differentiation between the NSD and LND, and there are no reproductive barriers evident between the kinds of dace after they came into secondary contact.

79 4.2. Conservation implications

4.2 Conservation implications

The Nooksack dace is currently listed as Endangered under Canada’s Species at Risk Act (SARA), but its taxonomic status remains unclear. One of the major outcomes of my study was to determine whether the Nooksack dace should be considered a species distinct from the longnose dace, a subspecies of the longnose dace, or simply a geographically cohesive variant of the longnose dace. There has long been debate among conservation biologists, policy-makers, and taxonomists (to name a few), over what the appropriate unit for conservation is. As the urgency to counteract anthropogenic environmental change becomes greater, it becomes ever more important to make wise conservation decisions; it is clear that not everything can be conserved, so where shall we draw the line? “Evolutionarily significant units” or ESUs, loosely defined as populations that have a distinct evolutionary potential, are considered worth conserving (Ryder, 1986; Moritz, 1994), and are distinguished using some combination of genetics, natural history, morphometrics, and distribution. Under Canada’s Species at Risk Act, the ESU concept is employed in the recognition of “designatable units” (DUs, e.g., Taylor et al., 2013). DUs are identified according to one (or more) of four criteria: being a named subspecies or variety; being a genetically distinct unit based on morphology, genetics, life history, or behavioural traits; being a geographically disjunct population; being a biogeographically distinct unit in a different ecogeo- graphic region. The DU must also represent a subdivision within the species which has a differing risk of extinction / conservation status. Under these criteria, the Nooksack dace does represent a DU regardless of its apparent lack of reproductive isolation with the longnose dace. The NSD is morphologically differentiated (though differences are slight), and forms a distinct mtDNA clade, meeting the ”genetically distinct unit” criterion. Its conservation status, at least within Canada, is also demonstrably different from the longnose dace. In a broader sense, the Nooksack dace represents a separate evolutionary lineage, as part of the distinctive Chehalis fauna. However, the DU criteria are in large part designed to allow the listing of populations that are likely to be genetically distinct, when no genetic survey has yet taken place. In the case of the NSD, its largely distinct geographic range, slight morphological differences, and distinct mtDNA all suggest that it would be genetically distinct, yet the analysis of nuclear DNA does not uphold this. In light of this, the case for continuing to list the NSD as DU is weak, and with extinction rates as high as they are right now, a triage approach should place the NSD at a very low priority level.

80 4.2. Conservation implications

Regarding the taxonomic status of the Nooksack dace, its lack of reproductive isolation from the longnose dace argues that it should not receive species status. Indeed, cases like the Nooksack dace make an argument against relying upon percentage divergence thresholds for declaring new species and conservation priorities. Despite 2–3% divergence in mtDNA, which puts it just within the estimated “species cutoff” for cyprinid fishes (April et al., 2011), the Nooksack dace exhibits no reproductive isolation from its sister lineage, nor does it display any apparent functional divergence. Particularly in sympatry, it is difficult to conceive of different adaptations in fish bearing distinct NSD and LND mtDNA when those fish can be caught in the same riffles (i.e., areas with 1 m2) and are morphologically nearly identical to one another. If, as some have argued (e.g. Crandall et al., 2000), conservation efforts should prioritize the maintenance of adaptive diversity, then the Nooksack dace may not be a high priority. Future study may uncover such functional differences, but the apparent free interbreeding between NSD and LND in sympatry, and the apparent health and longevity of the mtDNA–introgressed populations, does not suggest that any such differences would be significant, and that the NSD and LND are not distinct species. As mentioned in Chapter 2, the question of whether the NSD should be classified as a subspecies of the LND is more contentious, in large part because subspecies criteria is generally vague. The NSD certainly lies in a grey are where, by virtue of slight morphological differentiation or genetic differentiation, it could be classified as a subspecies, and a great many subspecies have been defined on lesser evidence (Mayr, 1982). However, if subspecies differences are taken to indicate incipient speciation and/or adaptation to differing environmental conditions, I do not think that the NSD should receive subspecies status. Rather than heading towards increased reproductive isolation and complete speciation, the NSD and LND appear to be gradu- ally progressing towards greater intermixing and merging of their gene pools. Morphological differences do not appear to be driven by large-scale environmental variation, though they may be due to microhabitat differences; this point requires further study, but for the time being, I recommend that the NSD be classified solely as a DU and not as a subspecies. If the preservation of evolutionary processes and the network of connections between populations are goals of conservation, then the three streams that are sympatric for NSD and LND mtDNA types should be considered part of the range of the Nooksack dace and protected as such. It is unlikely that human activities caused the overlap between Nooksack and longnose dace, and thus they provide a valuable example of a natural zone of post-

81 4.3. “Ephemeral” speciation glacial secondary contact and genetic exchange between the Nooksack and longnose dace. However, from a more pragmatic point of view and with limited resources available to conservation eﬀorts, the NSD is only marginally distinct from the LND, maintains a healthy population in the United States, and therefore should not be given high conservation priority.

4.3 “Ephemeral” speciation

The path to speciation is rarely straightforward; for every neat dichoto- mous branching, there are many other cases of incomplete speciation, or “ephemeral speciation” in which new lineages form but do not persist (Rosen- blum et al., 2012). Dynesius and Jansson (2013) suggested that the rate of speciation should be considered as having three separate components: rate of lineage splitting, level of persistence of within-species lineages, and length of “speciation duration,” the time required to complete speciation. They suggested that the level of persistence as a factor of speciation rate has been understudied, and that low persistence (as has been suggested by Rosen- blum et al. (2012)) is widespread and an important factor in determining overall rates of speciation. Additionally, while there has been a considerable amount of work on incomplete speciation and remerging of lineages which have arisen through ecological speciation (Taylor et al., 2006; Nosil et al., 2009; Behm et al., 2010), there has been comparatively little study of incomplete speciation in lineages which have diverged allopatrically. Studies in zones of secondary contact are an important component of investigating these questions and further understanding the rate of allopatric speciation. Situations such as that of the NSD and LND are infrequently studied, in part because they are difficult to identify: the two fish are, practically speaking, not morphologically differentiated, so there was no obvious marker of the zone of secondary contact until their mtDNA was studied. Discovery of such cryptic hybrid zones is becoming more common with widespread genetic sampling, however, and will hopefully lead to a greater number of studies investigating the stages of allopatric speciation, in particular the persistence of within-species lineages. Guidelines for determining what percent divergence indicates separate species is generally done by comparing the percent divergence within and among well-studied taxa whose species status is not in doubt. Studies such as my own suggest that percent divergence should not be used as a reliable indicator, but deep divergences can and should be used as a pointer towards new areas of research. Further work on the R. cataractae species complex could focus on dace

82 4.3. “Ephemeral” speciation east of the Rocky Mountains; my morphological and genetic analyses both indicated that populations in eastern British Columbia and in Ontario are far outliers from northwestern American dace, and previous work by Bart- nik (1972) found that at least some of the R. cataractae east of the Rocky Mountains have diﬀerent mating behaviour from those west of the continental divide.

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98 Appendix A

Chapter 2

Table A.1: List of sampling locations for Nooksack and longnose dace (Rhinichthys cataractae) used in morphological analyses. Catalogue no. refers to the catalogue of the Beaty Biodiversity Museum (University of British Columbia), where tissue samples which have not yet been entered into the museum catalogue are indicated by “—-”.N denotes number of ﬁsh from that collection that were measured.

Catalogue Allopatric / Species Location Location Site Latitude Longitude Collection N No. Sympatric Present Code No. Date BC80-0084 A NSD Wynoochee River, WA, WY 1 46.98069 123.64640 25/03/1975 13 USA BC80-0093 A NSD Wynoochee River, WA, WY 2 46.98069 123.64640 25/04/1975 15 USA BC66-0096 A NSD Willapa River, WA, USA WP 1 46.56001 123.56020 31/08/1961 13 BC06-0236 A NSD Willapa River, WA, USA WP 2 46.53591 123.46143 27/07/1990 4 11-0539 A NSD West Fork Satsop River, SR 1 47.05983 123.54097 12/08/2007 26 WA, USA BC11-0544 A NSD Porter Creek, WA, USA PC 1 46.94645 123.29543 13/08/2007 5 11-0018 A NSD Willamette River, WA, WR 1 43.80578 123.05827 21/09/1994 7 USA BC76-0038 A NSD Cowlitz Rver, WA, USA CW 2 46.35624 122.93204 18/02/1961 4 99 Continued on next page Appendix A. Chapter 2 N 28 30 Date 03/06/2002 13/06/1953 Continued on next page 122.89594 122.68121 Latitude Longitude Collection 49.24145 54.09764 1 1 Site No. BR WC Code CRCR 3CR 4 49.28031 122.77632CR 4 49.32602 08/06/2004 122.77153CR 10 5 49.32602 08/08/2004 122.77153CR 1 3 49.32602 31/08/2008 122.77153 2 2A 49.04155AN 122.77107 49.33616 09/07/2004 122.76821AN 3 6 07/06/2004AR 5 1 49.24242 8 122.60108AR 4 49.24285 15/08/2004 122.57974AR 7 3 49.24159 27/09/2012 122.57978AR 19 4 49.23439 18/05/1994 122.60097 27 4 49.23918 18/07/2004 122.57932 5 49.23918 21/05/1976 122.57932 1 09/07/2004 5 Table A.1 – continued from previous page LocationBrunette River, BC, Canada Canada Location Canada Canada Canada Canada Canada Wright Creek, BC, Canada Canada Canada Canada Canada Canada Canada Species Present NSD LND Allopatric / Sympatric A A Catalogue No. BC11-0418 A11-0019 BC11-0004 SBC11-0006 NSD S— Cowlitz Rver, WA, USA— BOTH Coquitlam CW River,BC11-0007 BC, S BOTH S Coquitlam 1 River,BC11-0010 BC, S SBC57-0269 46.30808BC11-0005 122.92053 BOTH BOTH 30/08/1958 S Coquitlam River,— BC, Coquitlam 15 BOTH River, BC, BOTH Coquitlam River,BC06-0128 BC, Coquitlam River, BC, SBC11-0008 S BOTH S Alouette RiverBC82-0012 North, BC, SBC11-0009 BOTH S Alouette BOTH River, BC, BOTH Alouette River North, BC, Alouette River, BC, BOTH Alouette River, BC, BOTH Alouette River, BC,

100 Appendix A. Chapter 2 N 33 12 2 11 6 8 7 18 6 20 1 9 Date 17/08/2007 17/08/2007 2008/01/08 31/08/2008 31/08/2008 31/08/2008 11/06/1992 18/05/1994 27/08/1955 15/08/1994 04/08/2007 04/08/2007 Continued on next page 122.54129 122.53630 122.53630 122.52399 122.50855 122.46117 122.13359 122.13359 122.13261 121.03088 117.60099 117.55722 Latitude Longitude49.20216 Collection 49.20727 49.20727 49.21558 49.21210 49.22612 49.23485 49.23485 49.23581 50.15396 49.06946 49.10086 1 2 2 4 3 5 2 3 1 1 1 2 Site No. KC KC KC KC KC KC NC NC NC PT BC BC Code BT 1 49.00432 122.53217CQ 19/07/2007 20 1CL 49.38851 121.43344 1 02/07/1952HW 32 51.55186 118.53917 1 07/08/1952 30 50.80708 113.78779 15/06/1953 16 Table A.1 – continued from previous page LocationKanaka Creek, BC, Canada Kanaka Creek, BC, Canada Kanaka Creek, BC, Canada Location Kanaka Creek, BC, Canada Kanaka Creek, BC, Canada Kanaka Creek, BC, Canada Canada Norrish Creek, BC, Canada Norrish Creek, BC, Canada Norrish Creek, BC, Canada BC, Canada Petit Creek, BC, Canada region, BC, Canada Beaver Creek, BC, Canada Beaver Creek, BC, Canada Canada Species Present BOTH BOTH BOTH BOTH BOTH BOTH LND LND LND LND LND LND Allopatric / Sympatric S S S S S S A A A A A A Catalogue No. 11-0541 11-0540 — — — — BC11-0542 A11-0020 BC06-0215 59-0600 BC59-0002 NSD ABC11-0003 Bertrand Creek, BC, BC56-0558 A11-0517 LND11-0516 BC57-0361 Hope Coquihalla River, A LND Columbia River, Big Bend LND High Wood River, AB,

101 Appendix A. Chapter 2 N 30 30 Date 16/06/1953 16/10/1950 113.75697 100.03532 Latitude Longitude51.35493 Collection 52.47925 1 1 Site No. RD PR Code BN 1 46.07371 86.19049 06/10/1957 10 Table A.1 – continued from previous page LocationJunction of Blindman & Red Deer Rivers, AB, Canada Pine River, Lake Winnipegosis, MB, Canada Location USA Species Present LND LND Allopatric / Sympatric A A Catalogue No. BC57-0362 BC57-0357 ABC59-0563 BC58-0047 A LND Milk River, AB, Canada BND MR Schoolcraft Country, MI, 1 49.07399 110.77100 14/06/1953 19

102 Appendix A. Chapter 2 ) sampled from British Columbia (BC), Ontario (ON), and Washington Rhinichthys cataractae Vertebral Count LocationBeaver Creek, BCBeaver River, ONColumbia River, BCNorrish LND Creek, BCSatsop LND Species LND River, WA 0Wynoochee 34 River, WA 0 NSD 0 35 1 NSD 36 NSD 5 4 4 0 37 2 3 6 3 0 4 4 0 0 1 5 3 4 1 1 2 State (WA) . N = 53. Table A.2: Rawlongnose data dace (LND, of vertebral counts made from CT scans for allopatric samples of Nooksack (NSD) and

103 Appendix A. Chapter 2 percentage variance ). Rhinichthys cataractae PC Eigenvalue Percentage variance1 Cumulative 2 2.383 1.804 1.325 1.14 18.286 0.95 13.847 0.90 10.218 0.82 8.809 0.72 7.3610 0.69 6.9911 0.65 6.3812 0.62 5.55 18.28 13 0.53 5.35 32.12 14 0.42 5.00 42.33 0.00 4.80 51.14 4.14 58.50 3.23 65.50 0.00 71.89 77.45 82.80 87.81 92.61 96.76 100.00 100.00 Table A.3: Eigenvalues and percentageconducted of on variance explained 11 by eachlongnose size-transformed axis dace morphological of ( a values principal and components two (PC) meristic analysis traits in all samples of Nooksack and

104 Appendix A. Chapter 2 2361 2344 4586 1842 0250 . . . . . 0 0 0 0 0 − − − − − 175 0.0647 . 0320 0.2461 2699 0.4987 2424 . 0 . . 0 0 0 − − − − 4387 0.4145 0.0871 41852647 -0.02942208 0.1237 0.3064 0.2264 3152 -0.2379 0.2689 . . . . . 0 0 0 0 0 − − − − − ). 1583 06381216 0.6246 0.2065 0.3013 27750322 0.3319 0.3298 0.0785 0.2085 . . . . . 0 0 0 0 0 − − − − − 3023 0.5296 0.2150 19812819 0.1125 0.3631 0.0852 0.7703 0.1549 . . . 0 0 0 − − − Rhinichthys cataractae M11Lateral line scale count Pectoral fin ray count 0.0154 0.5136 0.0750 0.4005 0.5723 0.1834 M6M7M8M9 M10 0.7130 0.3562 0.2040 0.0647 0.5658 0.5473 0.1996 M5 0.5242 MeasurementM1M2 PCM4 1 PC 2 PC 3 0.5439 0.6771 PC 4 0.5440 PC 5 M3 0.2003 Table A.4: Character loadingsmorphological for traits the and two firstand meristic five Nooksack traits. axes dace of Principal ( components the analysis principal was components conducted analysis on of 582 11 longnose size-transformed dace

105 Appendix A. Chapter 2 9 0 9 0 9 8 5 ...... 9449 8915 8957 8891 8932 9018 9102 − − − − − − − 6 8 1 4 3 19 NA NA 9 4 ...... 9449 8895 8846 8828 8861 8939 8988 9071 9130 − − − − − − − − − 5 5 8 5 6 4 9 6 5 ...... 9449 9186 9122 9128 9009 9168 9087 9142 9224 − − − − − − − − − 6 8 7 8 9 8 8 9 6 ...... 9449 9321 9125 9162 8981 9137 8955 9065 9057 − − − − − − − − − 0 1 3 2 4 3 1 4 ...... 8862 8787 8949 8914 8784 8818 8857 9386 − − − − -8761.3 − − − − 3 4 2 7 6 2 3 0 4 ...... 9042 8935 9033 8930 8932 8960 9386 9168 9088 − − − − − − − − − 3 0 0 6 4 3 7 ...... 8770 8768 8764 9386 8863 8871 8792 − − − − − − -8732.1 -8742.7 − 3 2 1 9 2 5 8 8 1 ...... 9007 8952 9386 9284 9131 9104 9096 8913 8808 − − − − − − − − − 1 6 7 2 0 6 2 1 3 ...... ). The top three models based on the BIC criterion are in boldface type. 9517 9001 8941 8915 8801 8768 8787 8781 8792 − − − − − − − − − 7 5 0 1 9 3 3 4 0 ...... 9517 9320 9213 9182 9060 9181 9110 9031 9127 − − − − − EII− VII EEI VEI EVI VVI EEE EEV VEV VVV − − − No. of clusters 1 2 3 4 9 5 6 7 8 Rhinichthys cataractae Table A.5: Bayesiananalysis information of criterion principal component (BIC)( scores values from for morphological each analysis of model all and samples number of of Nooksack and clusters longnose for dace the cluster

106 Appendix A. Chapter 2 0 1 0 9 . . . . 6767 6418 6494 6390 − − − − 2 7 86 NA 3 NA 1 NA 9 NA NA 0 0 ...... 6767 6403 6453 6344 6454 6447 6499 6556 9130 − − − − − − − − − 2 8 0 4 9 4 0 9 3 ...... 6767 6537 6575 6564 6530 6565 6571 6627 9224 − − − − − − − − − 4 7 8 6 0 2 4 5 7 ...... 6767 6699 6665 6478 6474 6509 6507 8981 6464 − − − − − − − − − 0 8 8 1 1 4 0 0 9 ...... 6291 6368 8949 6273 6276 6330 6362 6283 6706 − − − − − − − − − 8 9 2 7 0 5 2 5 2 ...... 9042 6460 6442 6477 6521 6366 6706 6442 6570 − − − − − − − − − 8 7 6 9 1 9 ...... ) from allopatric populations. The top three models based on the BIC 8770 6233 6256 6706 6375 6319 − − -6224.8 − − -6199.9 − − -6228.8 8 9 7 5 2 5 4 6 3 ...... 9007 6706 6627 6500 6426 6450 6477 6514 6455 − − − − − − − − − 2 2 1 2 4 6 2 1 5 ...... 6805 6449 6429 6419 6276 6273 6294 8801 6281 − − − − − − − − − Rhinichthys cataractae 2 9 9 7 5 4 5 7 3 ...... 6805 6634 6650 6524 6543 6492 6428 9060 6521 − − − − − EII− VII EEI VEI EVI VVI EEE EEV VEV VVV − − − No. of clusters 1 2 3 4 6 7 8 9 5 Table A.6: Bayesiananalysis information for criterion the cluster (BIC) analysisand values of longnose principal for dace component each ( scorescriterion from model are morphological and in analysis boldface of number samples type. of of clusters Nooksack for the cluster

107 Appendix B

Chapter 3

Table B.1: List of sampling locations for Nooksack and longnose dace (Rhinichthys cataractae) used in genetic analyses. N denotes number of ﬁsh from that collection that were measured.

Allopatric / Species Location Location Site Latitude Longitude Collection Date N Sympatric Present Code No. A NSD Wynoochee River, WA, USA WY 1 46.98069 123.64640 25/03/1975 4 A NSD Wynoochee River, WA, USA WY 2 46.98069 123.64640 25/04/1975 4 A NSD West Fork Satsop River, WA, USA SR 1 47.05983 123.54097 12/08/2007 25 A NSD Porter Creek, WA, USA PC 1 46.94645 123.29543 13/08/2007 5 A NSD Brunette River, BC, Canada BR 1 49.24145 122.89594 03/06/2002 30 S BOTH Coquitlam River, BC, Canada CR 3 49.28031 122.77632 08/06/2004 27 S BOTH Coquitlam River, BC, Canada CR 4 49.32602 122.77153 08/08/2004 12 S BOTH Coquitlam River, BC, Canada CR 3 49.04155 122.77107 09/07/2004 10 S BOTH Coquitlam River, BC, Canada CR 2A 49.33616 122.76821 07/06/2004 5 S BOTH Coquitlam River, BC, Canada CR X 49.23417 122.75389 2004 14 S BOTH Alouette River, BC, Canada AR 3 49.23439 122.60097 18/07/2004 22 S BOTH Alouette River, BC, Canada AR 1 49.22278 122.60028 2004 32 S BOTH Alouette River, BC, Canada AR 4 49.24159 122.57978 18/05/1994 11 S BOTH Alouette River North, BC, Canada AN 1 49.24285 122.57974 27/09/2012 19 S BOTH Alouette River, BC, Canada AR 2 49.23918 122.57932 09/07/2004 24 108 S BOTH Alouette River, BC, Canada AR 5 49.23503 122.56014 27/09/2012 2 Continued on next page Appendix B. Chapter 3 1 49 14 11 6 8 34 28 27/09/2012 17/08/2007 17/08/2007 31/08/2008 31/08/2008 31/08/2008 27/08/1955 04/08/2007 122.53481 122.54129 122.53630 122.52399 122.50855 122.46117 122.13261 117.60099 49.24443 49.20216 49.20727 49.21558 49.21210 49.22612 49.23581 49.06946 Latitude Longitude Collection Date N Site No. 6 1 2 4 3 5 1 1 Code AR KC KC KC KC KC NC BC Table B.1 – continued from previous page Alouette River, BC, Canada Kanaka Creek, BC, Canada Kanaka Creek, BC, Canada Kanaka Creek, BC, Canada Kanaka Creek, BC, Canada Kanaka Creek, BC, Canada Norrish Creek, BC, Canada Beaver Creek, BC, Canada Location Location BOTH BOTH BOTH BOTH BOTH BOTH LND LND Species Present A NSDA Bertrand Creek, BC, CanadaAA LND BT Fraser LND River, BC, Canada 1 Hope LND Coquihalla River, BC, Canada 49.00432 Beaver CQ River, ON, 122.53217 Canada FR 19/07/2007 1 1 22 49.38851 BV 121.43344 49.38306 02/07/1952 122.45222 1 27/09/2012 19 52.47925 100.03532 7 16/10/1950 10 Allopatric / Sympatric S S S S S S A A

109 Appendix B. Chapter 3 Girard and Angers 2006 Girard and Angers 2006 Girard and Angers 2006 Beasley et al. 2014 Beasley et al. 2014 Beasley et al. 2014 Beasley et al. 2014 Beasley et al. 2014 Beasley et al. 2014 Beasley et al. 2014 . The size indicates TYE705 WellRed D2 TYE705 WellRed D2 WellRed D2 TYE665 TYE705 TYE705 TYE705 TYE665 125-341 16 110-128 10 220-262 8 Rhinichthys cataractae Repeat Motif(CTAT)12 (CTATCATAT)8 (CTAT)3 Size (bp) K Dye Source (GA)12 GCGT(GT)12 (CA)7(CT) 4CACT (CA)8(CT)3 ATGGATATTAAAG 147-163AAAG 5 180-210ATCT 7 217-313AAAG 16 215-275 13 ATCT 196-240 9 244-384 19 167-307 19 Sequence Reverse, 5’—-3’ CAGAGGT- CAAACAGTAG- TAGG AGTGAGTG- GTTGAGTAGG TCATGAAT- GCAGTACTGG AAGCTTTCAAG- TAATCAGAT- GAGGC AATGTTGAAAT- GTTGCACAAACC ATAATAGCCAT- GAGGGAGTCCG TGAGATGCTGCT- GTGAATGC TTACGTTACTCAT- GCGTTTACCC TTACTCTAGACCT- GATCTGTGAGGC TTCATGC- TAGTTGTTCAACC- TATGG —3’ CTGCCC GACATCC TAGAGG CAAACCC TACAGCCC CGTTGC GCGCTTGC TAAATTATGTCC GAAACAAACC TACTTTCATC- CACCAGG 15b CTCACAGACTAC- 16 GAGAACGAGTG- 23 TTCGTCCATATC- 4 ATGCATCCACC- 57 TGTTTCCTTGCC- 36 ATCCTGCTGGATC- GATACACCTTCT- 42 GCTGCCTGGTG- 43 CAAATCCCTTTG- 45 TGCTTTAT- LocusRhca Sequence Forward, 5’ Rhca Rhca Rhca Rhca Rhca Rhca Rhca Rhca Rhca Table B.2: Details forthe 10 range polymorphic of microsatellite observed loci allelesdye in developed indicates base for the pairs ﬂuorescent and tag includes the used length to of label the the CAG locus. tag; K is number of alleles observed;

110 Appendix B. Chapter 3 23 0.8898 0.96 1 0 0 NA 0.0429 0.7577 0.6970 0.1125 Rhca 7 0.9429 1 1 0.7619 0.7273 0.415 0.9194 0.6768 0* Rhca 0* Continued on next page 16 Rhca 0.7698 0.76 0.0476 0 0 NA 0.6429 0.7576 0.9741 45 0.9600 1 1 0.3680 0.4545 1 0.9168 0.9394 0.9555 Rhca 5 indicates the P-value for a test of Hardy- Rhca 0.4669 0.4 1 0.6104 0.4545 0.16 0.6873 0.6869 0* P . 42 0.5657 0.52 0.619 0.5671 0.7273 0.9173 0.7849 0.7576 0.0852 Rhca 36 Rhca 0.9396 0.96 0.3627 0.6190 0.5455 0.4645 0.8396 0.8283 0.469 ) heterozygosities for each locus/population combination, for 10 O 15b H Rhinichthys cataractae Rhca 0.8290 0.76 0.204 0.7446 0.6364 0.3324 0.8930 0.9091 0.4348 4 0.8065 1 1 0.2597 0.2727 1 0.6645 0.6667 0* Rhca ) and expected ( 44 E H 0.71430.8 0.6 0.2 0.95560.9144 0.9111 0.80.8636 0.7833 0.5333 0.7273 0.9165 0.3778 0.8 0.8636 0.9556 0.7992 0.7727 0.40.8950 0.6444 0.70510.8939 0.8182 0.7865 0.9778 0.5877 0.8788 0.4 0.5909 0.8624 0.6857 0.8118 0.7879 0.8636 0.8455 0.6089 1 0.8636 0.5909 0.7363 0.9144 0.7273 0.8182 0.7267 0.7030 0.2 0.7424 0.8636 0.9021 0.8788 0.7289 1 0.8182 0.9438 0.8182 0.6415 0.8 0.5606 Rhca 0.6077 0.64 1 0.7662 0.7273 0.345 0.9286 0.9394 0.6382 E O E O E O E O E O E O H H PH 1H P H H 0.0857P 0.204H 0.2098H 0.3627P 0.3025H 0.619 0.2447H P 1 0.4184H 0.5673H 0.1195P 0.608 1 0.7755 0.0527 0.7414 0.0476 0.5119 0.5199 1 0.0911 0.0242 0.7563 0.3741 1 0.3227 0.9927 Locus Population Porter Creek, WA Satsop River, WA Bertrand Creek, BC Brunette River, BC Coquitlam River, BC Alouette River, BC Weinberg equilibrium performed inequilibrium Genepop are v4.2.2 indicated (Rousset in 2008). bold Signiﬁcant and departures with from (*). Hardy-Weinberg Table B.3: Observedmicrosatellite ( loci and 12 populations of

111 Appendix B. Chapter 3 23 0.6986 0.8182 0.9669 0.8646 0.8462 0.4886 0.7917 1 1 Rhca 7 0.8571 0.4545 0* 0.9662 1 1 0.8417 0.625 0.0826 Rhca 16 Rhca 0.8009 0.8182 0.3823 0.7415 0.8462 0.9078 0.7583 0.5 0.0488 45 0.9177 0.9091 0.2847 0.9077 0.7692 0.035 0.6111 0.625 1 Rhca 5 Rhca 0.1775 0.1818 1 0.7908 1 1 0.4904 0.625 1 42 0.7359 0.8182 0.8639 0.8800 0.8462 0.3704 0.7083 0.625 0.3497 Rhca 36 Rhca 0.6450 0.6364 0.4333 0.9108 1 1 0.9417 0.875 0.421 15b 0.8506 0.136 0.9502 0.0223 0.2769 0.6128 0.2512 0.0893 1 0.5653 0.7301 0.4535 1 0.5465 0.1035 0.0315 Rhca 0.5152 0.5455 0.7533 0.8215 0.9231 0.9674 0.6731 0.625 0.5257 4 0.0161* 0.6061 0.3636 0.0817 0.5992 0.3846 0.0024* 0.0164* 0.9333 0.75 0.1087 Rhca Table B.3 – continued from previous page 44 0.90730.9022 0.7246 0.7717 0.8144 0.8261 0.7642 0.6848 0.57090.9546 0.60871 0.4437 0.7424 0.4239 0.9529 0.9546 0.9348 0.6667 0.6781 0.9243 0.6413 0.8333 0.8858 0.84850.7762 0.8804 0.6165 0.8125 1 0.3182 0.7964 0.5870 0.75 0.9697 0.8629 0.8333 0.7424 1 0.7077 0.3333 0.8148 0.8629 0.8333 0.8788 0.8206 0.75 0.6667 0.9496 0.8333 0.875 0.6351 0.8333 0.8125 0.8972 1 0.4173 0.625 0.875 0.25 Rhca 0.9567 1 1 0.9292 1 1 0.3 0 0.0667 E O E O E O E O E O E O H H PH 0.1228 H P H H PH 1H P H 0.4717H P 0.3172H 0.7786 1H P 0.6718 1 0.1581 0.1706 1 0.5322 Norrish Locus Population Kanaka Creek, BC Creek, BC Fraser River, BC Coquihalla River, BC Beaver Creek, BC Beaver River, ON

112 Appendix B. Chapter 3 Beaver River, BC Beaver Creek, BC Coqui- halla River, BC Fraser River, BC Norrish Creek, BC Kanaka Creek, BC Alou- ette River, BC Coquit- lam River, BC Brunette River, BC Bertrand Creek, BC Satsop River, WA Location Creek, WA 136148164232 0236 0240 0244 0252 0256 0 0264 0 0268 0 0272 0 0276 0 0280 0 0 0284 0 0 0.375288 0 0 0292 0 0 0296 0 0 0.063 0.125300 0 0 0.031 0.125304 0 0 0.125308 0 0 0.125 0 0.031 0312 0 0.045 0.091 0.063 0.031 0.125316 0 0 0.063 0320 0.008 0 0 0 0324 0.008 0 0 0 0 0.136 0.094 0328 0 0 0 0.031 0.114 0332 0 0 0336 0 0 0 0.094 0 0.159 0.125340 0 0.045 0 0.136 0.125 0.091 0344 0 0 0 0 0.094 0348 0 0 0 0.068 0.031 0.182 0 0352 0 0 0 0 0.068 0.063 0.095 0356 0 0 0 0.079 0.068 0360 0 0.008 0 0 0 0.087 0364 0 0 0.005 0.016 0.045 0 0 0.082 0 0368 0 0 0.077 0.091 0 0.031 0 0 0372 0.015 0.036 0.056 0.031 0.107 0 0 0380 0 0 0 0.032 0 0.056 0 0384 0.095 0 0.068 0.107 0.056 0 0 0 0 0 0.011 0.051 0.071 0 0.045 0 0 0.101 0 0 0 0.017 0.455 0.071 0 0 0 0 0 0.077 0.091 0 0.107 0 0 0 0 0.006 0.045 0.022 0 0 0.067 0 0 0 0.045 0 0.045 0.127 0.04 0 0 0.062 0 0 0 0.024 0.056 0.048 0 0 0.112 0 0 0.091 0 0 0 0.091 0 0 0 0 0 0.083 0 0.026 0 0 0.091 0 0 0 0.051 0 0.083 0.066 0.024 0 0.136 0.046 0 0.045 0.083 0 0.016 0 0.167 0 0.167 0.038 0 0.067 0 0 0 0 0 0 0.034 0.115 0.083 0.038 0.039 0.015 0 0.038 0.083 0.028 0 0.038 0 0 0 0 0.192 0.005 0 0.115 0 0.115 0 0.031 0 0.115 0 0.045 0 0 0.045 0.031 0 0.016 0.063 0.136 0.017 0 0.063 0 0.045 0 0 0.015 0.008 0 0.006 0 0 0.063 0 0 0 0 0.031 0.048 0.083 0 0 0 0 0 0 0 0 0 0.333 0.167 0 0 0 0 0 0 0 0 0 0.071 0 0 0 0.031 0 0.041 0.032 0.094 0 0 0 0.333 0.333 0 0.031 0.077 0 0.031 0.005 0 0.031 0 0 0.038 0 0.011 0.026 0 0 0.02 0.008 0 0.038 0 0.011 0 0 0 0 0.031 0 0 0.469 0.051 0 0 0 0.045 0 0 0.02 0 0.062 0 0.015 0 0.038 0 0 0 0 0 0 0 0 0 0 0 0 0.006 0 0 0 0 0 0 0 0 0 0.091 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Continued on next page 43 N 4 16 22 11 63 98 89 11 6 13 16 3 Locus AlleleRhca Porter Table B.4: Allele frequencies for 10 microsatellite loci for Nooksack and longnose dace at 12 sampling locations.

113 Appendix B. Chapter 3 Beaver River, BC Beaver Creek, BC Coqui- halla River, BC Fraser River, BC Norrish Creek, BC Kanaka Creek, BC Alou- ette River, BC Coquit- lam River, BC Brunette River, BC Bertrand Creek, BC Table B.4 – continued from previous page Satsop River, WA Location Creek, WA 99111115119123 0 0127 0131 0135 0139 0143 0 0 0147 0 0.25151 0.06 0155 0.28 0.375163 0.02 0.375167 0 0.02 0.045 0.32 0171 0.091 0 0.08 0175 0.068 0.02 0 0179 0.136 0 0 0.023183 0 0.091 0 0187 0 0.432 0.04 0195 0 0.068 0 0.12 0 0199 0 0 0.04 0211 0 0.008 0 0235 0 0.864 0 0 0 0239 0.1 0 0 0247 0.123 0 0 0 0 0.092 0251 0.046 0.185 0 0 0255 0.369 0.023 0 0 0 0.005259 0 0 0 0 0.038 0 0 0271 0 0.065 0 0 0275 0 0.005 0.489 0 0 0 0 0 0.18279 0.008 0 0.039 0 0 0.084 0 0 0.011 0 0.008 0.062 0.006 0.455 0.045 0.011 0 0 0 0 0 0.194 0 0 0 0.5 0 0 0 0 0 0 0 0 0 0 0 0.045 0 0.045 0 0 0 0 0 0.022 0 0 0 0 0 0.167 0 0 0 0 0.5 0.031 0.083 0 0 0 0 0 0.083 0 0.008 0.409 0 0 0 0.017 0 0 0.008 0 0 0.023 0.15 0.005 0 0 0 0 0.1 0.25 0.005 0 0.167 0.045 0 0 0.011 0 0.05 0 0 0 0 0 0 0 0 0.156 0 0 0.045 0 0 0 0 0.017 0.008 0.4 0 0 0.25 0 0.005 0.344 0.063 0 0 0.005 0.188 0 0 0.05 0 0.011 0 0 0.011 0 0 0 0 0 0 0 0 0 0 0 0 0 0.125 0 0 0 0.006 0 0 0 0.063 0 0 0.028 0 0 0.008 0.063 0 0.006 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.108 0 0 0 0 0.027 0 0 0 0 0 0 0.038 0 0.028 0 0 0 0 0 0.006 0 0 0 0 0 0 0 0 0 0 0.063 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.125 0 0 0 0.125 0.188 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.063 0 0 0 0 0 0 0 0 0 0 0 0.063 0 0.063 0 0 0 0 0.063 0 0 N109117119 5123 0125 0127 0129 0.2 25 0.1 0 0 0.02 0 0 0.38 22 0.06 0.114 0.091 0 0.068 0 0.159 0 11 0 0 0 0 0 0 0 63 0 0 0.095 0.135 0 0 99 0.005 0 0 0.136 0 0.077 0 91 0.357 0 0.005 0.011 0 0 0 0.005 0 8 0 0.005 0 0 0 0 0 0 6 0 0 0 0 0 0 0.063 13 0 0 0 0 0 0 0 16 0 0 0 0 0.077 0.429 0 7 0 0.125 0 0 0 0 0 Continued on next page 4 N 4 25 22 11 65 93 89 11 6 10 16 8 15b Locus AlleleRhca Porter Rhca

114 Appendix B. Chapter 3 Beaver River, BC Beaver Creek, BC Coqui- halla River, BC Fraser River, BC Norrish Creek, BC Kanaka Creek, BC Alou- ette River, BC Coquit- lam River, BC Brunette River, BC Bertrand Creek, BC Table B.4 – continued from previous page Satsop River, WA Location Creek, WA 131133135137 0139 0141 0145 0149 0151 0.02 0153 0.02 0155 0 0.1157 0 0.1161 0 0.04 0.1165 0.023 0 0167 0 0.1 0.04169 0 0.2 0.02173 0 0.091 0 0.1 0.14175 0 0177 0 0 0.02 0 0.02179 0 0 0 0 0.1183 0 0 0 0.02185 0 0 0 0211 0 0 0 0217 0 0 0 0 0225 0.159 0 0.045 0 0 0231 0 0 0 0 0233 0.008 0 0.015 0 0.187237 0 0 0 0 0 0239 0 0 0 0 0 0241 0 0.01 0 0 0 0 0 0.06243 0 0 0 0 0 0245 0 0 0 0247 0 0 0 0 0 0.005 0 0253 0 0 0.5 0 0 0 0 0255 0 0.136 0 0 0 0 0 0259 0 0 0 0 0 0261 0 0 0 0 0 0263 0 0 0 0 0.083 0 0 0 0 0.005267 0 0 0 0 0 0 0 0269 0 0 0 0 0 0 0275 0.048 0 0 0.385 0 0.083 0 0 0281 0.032 0 0.023 0 0 0 0 0303 0 0 0 0 0 0 0.06 0 0 0.025 0 0 0 0 0 0.038 0 0 0 0 0 0 0 0 0 0 0 0 0.02 0 0 0.038 0 0.023 0 0 0 0.045 0.005 0 0 0.136 0 0 0 0 0 0 0 0 0 0 0.02 0 0 0 0.136 0.016 0 0.071 0 0 0.227 0 0 0 0 0.005 0 0.455 0 0.008 0.016 0.045 0 0.045 0 0 0 0 0.045 0 0 0 0.091 0 0.005 0 0 0 0.095 0 0.083 0.125 0.25 0 0 0.01 0.135 0 0 0.076 0 0.04 0.087 0 0 0 0 0 0 0 0 0 0 0 0 0.083 0.103 0 0 0.125 0.101 0 0 0 0.005 0 0.096 0.137 0.083 0.152 0 0 0.005 0.015 0 0 0 0 0 0 0 0.154 0 0.005 0.086 0 0 0.038 0 0 0 0 0 0 0 0 0 0.008 0 0 0.038 0.11 0 0 0.071 0 0 0 0 0 0 0 0 0 0 0 0.063 0 0.008 0 0.063 0.008 0 0 0 0 0 0 0 0 0 0 0.031 0 0.005 0 0 0.02 0.25 0.286 0 0 0 0.154 0.01 0 0 0 0.02 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.038 0 0 0 0 0.083 0 0 0.005 0.125 0.038 0 0.016 0 0 0 0.038 0 0.01 0 0 0.083 0 0 0 0 0 0 0 0 0 0 0 0.031 0 0 0 0.063 0 0 0 0 0 0 0.077 0 0.094 0 0 0 0 0 0 0 0 0 0.313 0.083 0 0 0 0.083 0 0 0 0 0 0 0.031 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.071 0.071 0 0 0 0 0 0.071 Continued on next page Locus Allele Porter

115 Appendix B. Chapter 3 Beaver River, BC Beaver Creek, BC Coqui- halla River, BC Fraser River, BC Norrish Creek, BC Kanaka Creek, BC Alou- ette River, BC Coquit- lam River, BC Brunette River, BC Bertrand Creek, BC Table B.4 – continued from previous page Satsop River, WA Location Creek, WA 119135179191 0195 0199 0203 0207 0211 0 0215 0 0219 0 0.2223 0 0227 0 0231 0 0.04 0.2235 0 0.14 0 0.12239 0 0.2243 0 0.06 0.1247 0 0 0.08 0 0.06251 0.25 0.341 0 0255 0 0.04 0 0.1259 0.114 0 0.2 0.06263 0.136 0.591 0 0 0 0267 0 0 0.04 0279 0.091 0.045 0 0.06 0 0 0 0 0.212 0.068 0.02 0.1 0.04 0 0 0.015 0.136 0 0 0.008 0 0.06 0 0.008 0.045 0.136 0.201 0 0.06 0.005 0 0 0 0.182 0 0.015 0.005 0.026 0.25 0 0.005 0 0 0 0.103 0.38 0.02 0 0.098 0 0 0 0 0.144 0.098 0 0.234 0.053 0.196 0 0 0 0.038 0 0 0.5 0.191 0 0 0.098 0.005 0.022 0.015 0 0 0.031 0.174 0 0 0 0 0 0 0 0.043 0.076 0 0 0.015 0 0 0 0 0.027 0 0 0 0 0 0.023 0.083 0 0.182 0 0 0.021 0 0 0.318 0 0 0 0 0.154 0 0.167 0 0 0.021 0.115 0 0 0.25 0.167 0 0 0 0.083 0.083 0 0 0.125 0 0.077 0 0.038 0.5 0 0.008 0.038 0 0.077 0 0 0 0 0 0.192 0 0 0 0.063 0 0 0 0 0 0.188 0 0 0 0 0 0 0 0.077 0 0.094 0 0 0 0 0.188 0 0 0 0.083 0 0.063 0 0.063 0 0.188 0 0 0 0 0.083 0.063 0 0.077 0.063 0 0 0 0 0 0 0 0 0 0 0 0.154 0 0 0 0 0 0 0 0 0 0.125 0 0.063 0 0 0 0 0 0.063 0 0.031 0 0 0 0 0 0 0.063 0.063 0 0 20164168172176 0 0180 0184 0188 0192 0.6196 0 0.04 0200 0.02 0204 0 0208 0 0 0.64212 0 0.4216 0 0.045 0 0 0 0 0.455 0 0 0 0 0 0 0 0.16 0 0 0 0.068 0.08 0 0.02 0.045 0.045 0.091 0 0 0 0.455 0.295 0.04 0 0 0 0 0.28 0 0.015 0 0 0.091 0.5 0 0 0.005 0 0 0.187 0 0 0.136 0 0 0.015 0.114 0.409 0.005 0.023 0.609 0 0 0.082 0 0.02 0 0.131 0.374 0 0 0.008 0.071 0 0.091 0.015 0.136 0.038 0.228 0 0 0.03 0 0 0 0.056 0 0 0.083 0 0 0 0.045 0.409 0 0.038 0.231 0 0 0 0.005 0 0.167 0.038 0 0 0 0 0.318 0.25 0 0 0 0.192 0 0 0 0.333 0.083 0 0 0 0 0 0 0 0 0.077 0.154 0 0 0.125 0 0 0 0.077 0 0 0.313 0.094 0 0 0.5 0 0 0 0 0 0.063 0.038 0 0.25 0 0 0.063 0 0 0 0 0.063 0.031 0.063 0 0 Continued on next page 36 N 5 25 22 11 66 97 92 11 6 13 16 8 42 N 5 25 22 11 66 99 92 11 6 13 16 8 Locus AlleleRhca Porter Rhca

116 Appendix B. Chapter 3 Beaver River, BC Beaver Creek, BC Coqui- halla River, BC Fraser River, BC Norrish Creek, BC Kanaka Creek, BC Alou- ette River, BC Coquit- lam River, BC Brunette River, BC Bertrand Creek, BC Table B.4 – continued from previous page Satsop River, WA Location Creek, WA 220224228232 0236 0244 0380 0420 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.005 0 0 0 0 0 0 0 0 0.005 0 0.005 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.083 0 0.038 0 0 0.154 0 0 0 0 0.125 0 0 0 0.125 0 0 0 0 0.125 0 0.031 0 0.031 0 0 0 0 0 0 140150160165 0170 0175 0180 0.1185 0.8190 0 0195 0 0200 0 0.1 0.04205 0 0.72210 0215 0 0.02 0225 0.25 0 0 0 0.14235 0.591 0 0405 0.02 0435 0.023 0.02 0 0 0.182 0 0 0 0 0 0 0 0 0.591 0 0 0 0 0 0.379 0.04 0 0 0.023 0.182 0 0 0 0.008 0.53 0 0 0 0.045 0 0 0.114 0 0 0.025 0.341 0 0.023 0.015 0.739 0.045 0.005 0 0 0 0.027 0.096 0 0 0 0 0 0 0 0.016 0.909 0.068 0.015 0 0 0 0.087 0 0.023 0 0 0.833 0.061 0 0.005 0.106 0 0.008 0 0.045 0 0 0 0.04 0.385 0.033 0 0.083 0 0.131 0.04 0.01 0 0 0 0 0 0.281 0 0 0.038 0.077 0.033 0.015 0 0 0.005 0 0 0 0.214 0 0 0.192 0 0.045 0.011 0 0 0.005 0 0 0 0.005 0 0.25 0 0 0 0 0 0 0 0 0.011 0 0.031 0.077 0 0 0 0.192 0 0 0 0.083 0 0 0 0.031 0.077 0 0.156 0 0 0 0 0 0 0.188 0 0 0 0 0 0 0 0.031 0.143 0 0 0 0.643 0 0 0 0.031 0 0 0 0 0 0 0 0 0 0 0 119139143147 0151 0155 0159 0163 0167 0 0171 0 0175 0 0179 0 0183 0 0 0 0 0 0 0 0 0 0 0 0 0 0.023 0 0 0.364 0 0 0.205 0 0.773 0 0 0 0 0.227 0.136 0 0 0 0.048 0 0 0.087 0 0 0.008 0 0 0 0.01 0.119 0 0.051 0 0.03 0 0.005 0.048 0 0.011 0.087 0.116 0 0.033 0.025 0.033 0 0.111 0.005 0 0 0.111 0.04 0.011 0 0.005 0 0 0.04 0.008 0.005 0.011 0.045 0 0 0 0 0 0 0 0.227 0.076 0 0.182 0 0 0 0 0.091 0 0 0 0 0 0 0 0.033 0 0 0.083 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.031 0 0 0 0 0.2 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0 0 Continued on next page 5 N 5 25 22 11 66 99 92 11 6 13 16 7 45 N 5 25 22 11 63 99 92 11 6 13 16 5 Locus Allele Porter Rhca Rhca

117 Appendix B. Chapter 3 Beaver River, BC Beaver Creek, BC Coqui- halla River, BC Fraser River, BC Norrish Creek, BC Kanaka Creek, BC Alou- ette River, BC Coquit- lam River, BC Brunette River, BC Bertrand Creek, BC Table B.4 – continued from previous page Satsop River, WA Location Creek, WA 187191195199 0203 0207 0211 0215 0.1219 0.02 0223 0 0227 0 0231 0 0 0235 0 0239 0.02 0243 0 0.06 0247 0 0.06 0251 0 0.02 0 0255 0 0.091 0.04 0.1259 0 0.02 0263 0 0 0.06 0.1267 0 0 0 0.1 0.2271 0.023 0 0 0 0 0.1 0275 0.023 0279 0 0 0 0.1 0.08283 0 0 0 0 0 0.02287 0.04 0 0 0.024 0 0.023291 0.048 0.016 0.023 0.06 0 0.1295 0 0.06 0.2 0.06 0.071299 0 0 0 0.01 0 0 0 0.032303 0.024 0 0.01 0.005 0 0 0 0 0.016307 0.016 0 0 0 0 0.005311 0 0 0 0 0.02 0.02 0.01315 0.005 0 0.06 0 0 0.005 0 0.015323 0.005 0 0 0.02 0 0 0 0.043335 0 0 0 0.06 0343 0 0 0 0.005 0 0 0.02 0 0 0.011 0.091 0351 0 0 0 0 0 0 0.091355 0 0 0 0.04 0.015 0395 0 0 0 0.045 0.01 0 0.045 0.1 0 0.083 0 0.01 0 0 0 0 0 0.008 0.083 0 0 0 0 0.02 0 0 0 0 0 0 0.045 0 0 0 0.02 0.083 0 0.192 0.01 0 0.038 0.167 0 0.005 0 0.01 0 0.015 0 0 0 0.083 0 0 0 0 0 0.023 0.02 0.038 0.038 0.031 0.016 0 0 0 0.008 0 0.033 0.115 0 0 0 0 0.01 0.231 0.011 0.005 0 0.038 0 0.076 0 0 0.016 0 0 0.094 0 0.063 0 0 0.024 0.082 0.091 0 0 0 0.063 0 0.156 0.008 0.045 0.011 0 0.008 0 0 0.094 0.043 0.045 0.202 0.083 0 0 0.03 0 0 0 0 0 0 0.056 0 0.038 0 0.016 0 0.023 0 0 0.025 0.083 0 0.005 0 0.045 0 0.016 0 0 0 0.076 0 0 0.038 0.024 0 0.022 0 0.082 0.016 0.022 0 0.015 0 0.06 0 0 0.063 0 0 0 0.005 0 0.031 0 0 0 0 0 0.005 0.1 0 0 0.045 0.005 0 0.016 0 0 0 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0.083 0 0 0 0.031 0 0 0.063 0 0.167 0 0.038 0 0 0 0 0 0.1 0 0.038 0 0 0 0 0 0 0.038 0 0 0 0.1 0 0 0 0 0 0 0 0 0.038 0.031 0.077 0.077 0 0 0 0 0 0.1 0 0 0 0 0.031 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0 0.1 0 0 0 0 0 0 0.038 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.063 0 0 0 0 0 0 0 0 0 0 0 0.094 0 0 0 0 0 0.063 0 0 0 0 8088 0 0 0 0 0 0 0 0 0 0 0 0.005 0 0 0 0 0 0 0 0 0 0.031 0 0 Continued on next page 16 N 5 25 22 11 64 96 92 11 6 13 16 8 Locus Allele Porter Rhca

118 Appendix B. Chapter 3 Beaver River, BC Beaver Creek, BC Coqui- halla River, BC Fraser River, BC Norrish Creek, BC Kanaka Creek, BC Alou- ette River, BC Coquit- lam River, BC Brunette River, BC Bertrand Creek, BC Table B.4 – continued from previous page Satsop River, WA Location Creek, WA 9096100102104 0108 0 0110 0112 0.5114 0.4116 0 0118 0 0.02 0.1120 0.04 0 0.4122 0 0.08124 0126 0 0 0.04 0 0.24128 0.068 0 0 0130 0 0.12 0132 0.06 0134 0 0.386 0.5 0 0 0136 0 0 0144 0 0.023 0 0 0170 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0.039 0 0 0 0 0 0 0 0.148 0 0 0 0 0 0.336 0 0 0 0.313 0 0 0.005 0 0.057 0 0 0 0 0.495 0 0 0.023 0 0 0.146 0 0 0 0.049 0.315 0 0 0 0.457 0 0 0 0 0.109 0 0 0 0 0 0.136 0 0 0 0 0.409 0 0 0 0.136 0 0 0 0.167 0.021 0 0.141 0 0 0.083 0 0 0.136 0 0.167 0 0 0 0 0.077 0 0 0.008 0 0.266 0 0.192 0 0 0 0.385 0 0.016 0.063 0 0 0 0 0.06 0 0 0 0.219 0 0 0 0 0.005 0 0 0 0 0.005 0.038 0 0.091 0 0 0.045 0 0 0 0 0.125 0 0 0 0 0 0 0 0 0.5 0 0 0 0.045 0 0 0 0 0 0 0.005 0 0 0 0.308 0 0 0 0 0 0 0 0 0 0 0 0 0 0.083 0 0 0 0 0 0.563 0.438 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.125 0 0.063 0.25 0 0 0 0 0.125 0 0 0 0 21137181189193 0 0197 0201 0205 0209 0213 0 0 0217 0 0221 0 0225 0 0229 0 0.1233 0 0 0.04 0.1237 0 0.02 0241 0.045 0.08 0.1245 0 0.06 0 0.02249 0 0 0.1 0.12 0 0 0 0 0 0.227 0 0.08 0.1 0.12 0.023 0.045 0 0.045 0 0.02 0.1 0 0 0 0 0 0 0.045 0.008 0 0.114 0.039 0.02 0.04 0.008 0.318 0.114 0 0.114 0 0 0 0.136 0 0.016 0.008 0.045 0 0.023 0.016 0.023 0 0.008 0.016 0.227 0.063 0.318 0.078 0.042 0.011 0 0.011 0.084 0 0.023 0.037 0.011 0.055 0 0 0.063 0.079 0 0.023 0 0.026 0.005 0 0.154 0.027 0.198 0 0 0.159 0.068 0.037 0.071 0.058 0 0.016 0.016 0 0 0.044 0 0 0 0.091 0 0 0 0.088 0.273 0 0.005 0.037 0.227 0.047 0 0 0 0.047 0.091 0.1 0 0 0 0.1 0 0 0.005 0 0.084 0 0 0.045 0 0.033 0 0 0 0.038 0 0.077 0.005 0.2 0.077 0 0 0.1 0.038 0 0 0.038 0 0 0.031 0.156 0 0 0.219 0 0.077 0.038 0 0.038 0 0 0 0 0.063 0.375 0 0.1 0 0.063 0.094 0 0.188 0 0.031 0.1 0 0 0 0.038 0 0 0.125 0 0 0 0 0 0 0.077 0 0 0.031 0 0.188 0.063 0 0.031 0 0.063 0 Continued on next page 7 N 5 25 22 11 64 95 91 11 5 13 16 8 Locus Allele Porter Rhca

119 Appendix B. Chapter 3 Beaver River, BC Beaver Creek, BC Coqui- halla River, BC Fraser River, BC Norrish Creek, BC Kanaka Creek, BC Alou- ette River, BC Coquit- lam River, BC Brunette River, BC Bertrand Creek, BC Table B.4 – continued from previous page Satsop River, WA Location Creek, WA 253257261265 0269 0.2273 0277 0.1281 0.1285 0.02 0 0.1289 0293 0.02 0 0.06297 0 0.02301 0 0 0309 0.02 0313 0.045 0 0.02 0.1317 0 0 0321 0 0325 0 0 0 0 0 0329 0 0.02 0 0333 0 0337 0 0 0 0341 0 0 0 0.016345 0.023 0 0 0 0.031 0 0349 0 0 0 0353 0.008 0 0 0.047 0.047357 0.008 0 0 0.042 0 0361 0 0 0 0 0.008365 0 0.021 0 0 0 0 0.033 0.023 0369 0.016 0 0 0.027 0 0373 0 0 0 0.011381 0 0.016 0 0 0 0 0.182 0.021385 0 0 0 0 0 0 0 0.016389 0.031 0 0 0 0 0 0393 0.021 0 0 0 0 0 0 0397 0 0 0 0 0 0.045401 0.016 0 0 0 0 0 0409 0.011 0 0.016 0 0 0.023 0 0417 0.016 0 0 0 0.1 0.045 0 0421 0.005 0.008 0 0 0.023 0 0 0 0429 0 0.011 0.023 0 0 0.023 0 0.011 0437 0 0 0 0 0 0 0 0 0 0 0 0 0 0.115 0 0.023 0 0.077 0 0 0.016 0.008 0 0 0 0 0 0.115 0 0 0.023 0 0 0 0 0 0 0.031 0.008 0.031 0 0 0 0 0.011 0 0.005 0 0.011 0.039 0 0 0 0.038 0 0 0 0.1 0 0.005 0 0.031 0 0 0 0.005 0 0 0 0.016 0 0 0 0 0 0 0 0.038 0 0 0 0 0 0 0 0 0 0.005 0.016 0 0 0 0 0 0 0 0 0 0 0.063 0 0.063 0 0 0.005 0 0 0 0 0 0 0.016 0 0.005 0 0.038 0 0 0 0 0 0.023 0.031 0 0 0 0 0 0 0 0.016 0 0 0 0 0 0.016 0 0 0 0 0 0 0 0 0 0.005 0.008 0 0 0 0 0 0 0 0.016 0 0 0 0 0 0 0 0 0 0 0 0.005 0 0 0 0 0 0 0 0 0.005 0 0 0 0 0 0 0 0.016 0 0 0 0 0.016 0 0 0.031 0 0 0.1 0 0.011 0.005 0.008 0 0 0 0.011 0 0 0 0 0 0 0 0 0 0.005 0.011 0 0 0 0 0 0.005 0 0 0.038 0 0 0 0 0.005 0 0 0 0 0 0 0 0.005 0 0 0 0 0 0 0 0 0 0 0 0.063 0 0 0 0.038 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Continued on next page 23 N 4 25 22 11 63 96 86 10 6 13 16 8 Locus Allele Porter Rhca

120 Appendix B. Chapter 3 Beaver River, BC Beaver Creek, BC Coqui- halla River, BC Fraser River, BC Norrish Creek, BC Kanaka Creek, BC Alou- ette River, BC Coquit- lam River, BC Brunette River, BC Bertrand Creek, BC Table B.4 – continued from previous page Satsop River, WA Location Creek, WA 204212214216 0218 0234 0236 0238 0240 0 0244 0 0246 0 0248 0 0250 0 0254 0 0 0256 0 0 0258 0 0.04 0260 0 0.02 0262 0 0 0.25264 0 0 0.04 0.25266 0 0 0 0 0.25268 0 0 0 0270 0.16 0 0 0.25272 0 0.04 0 0 0274 0 0.22 0 0278 0 0 0 0 0 0288 0 0 0.14 0 0.08 0 0.114300 0.008 0 0 0.386 0 0 0 0.04 0 0 0 0.12 0 0 0 0 0 0.136 0.01 0 0 0.08 0 0 1 0 0.01 0 0 0 0 0.02 0 0 0 0.364 0 0 0 0 0 0 0 0 0.095 0 0 0 0 0 0.04 0 0.524 0 0 0 0 0 0 0.056 0.13 0 0 0.008 0 0 0 0.328 0 0 0 0.01 0.15 0 0.048 0.006 0 0.006 0.031 0.036 0 0 0.026 0.206 0 0.005 0.25 0 0.506 0 0.125 0.023 0.1 0 0 0 0 0.083 0 0.012 0 0 0 0 0 0 0.281 0 0 0.1 0 0.45 0 0 0.023 0 0 0.308 0 0.016 0.167 0.005 0.157 0 0 0 0 0 0.083 0 0.038 0 0.038 0.25 0 0 0 0 0 0 0 0 0.038 0.006 0.05 0 0 0 0 0 0 0 0 0.077 0 0 0 0.154 0 0 0.25 0 0.167 0 0.038 0 0 0 0 0 0 0 0 0 0 0 0 0.156 0 0 0 0.154 0.115 0 0.75 0 0 0 0.038 0 0.006 0 0 0 0 0 0 0 0.188 0.094 0 0.006 0 0 0 0 0 0 0 0.063 0 0 0.375 0 0 0 0 0 0 0 0 0.15 0 0 0.125 0 0 0.25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Locus Allele Porter

121 Appendix B. Chapter 3 Beaver R., ON 05. . 0 Beaver Cr., BC P < Coqui- halla R. Fraser R. Norrish Cr. Kanaka Cr. Alou- ette R. Coquit- lam R. Brunette R. values for all sampling locations. All are signiﬁcant at ST F Bertrand Cr. W. Fork Satsop R. Porter Cr. Table B.5: Pairwise Porter Cr.W.Fork Satsop R.Bertrand Cr.Brunette R. 0.0227Coquitlam R.Alouette R. — —Kanaka Cr. 0.1005Norrish Cr. 0.3463Fraser 0.0896 R. 0.0799Coquihalla R.Beaver 0.0227 0.0789 0.0799 Cr., 0.2721 BC 0.0797Beaver 0.0832 Cr., — ON 0.1134 0.1005 0.0896 0.2721 0.2292 0.0883 0.0498 0.0797 0.1704 0.0682 0.1121 0.128 0.3463 0.0493 0.0797 — 0.1039 0.1697 0.2292 0.0409 0.1549 0.0926 0.147 0.0896 0.1894 0.1144 0.0896 0.1097 — 0.0498 0.2216 0.1664 0.0887 0.1697 0.0789 0.0235 0.1571 0.3051 0.0797 0.266 0.0493 0.0491 0.3343 0.312 0.1894 0.0832 — 0.0235 0.3432 0.1015 0.128 0.0409 0.0504 0.0619 0.1394 0.0552 0.2216 0.1134 0.0491 0.1209 0.0857 0.0926 0.1144 — 0.0548 0.1474 0.0504 0.0315 0.3051 0.0682 0.1015 0.1255 0.1161 0.1039 0.0887 0.1152 0.1664 0.0857 0.0805 0.312 0.0883 0.0552 0.1598 — 0.1161 0.1549 0.1097 0.0859 0.1833 0.0315 0.0808 0.1704 0.0619 0.1657 0.266 0.0805 0.147 0.1664 0.0338 0.126 0.0548 0.0808 — 0.1121 0.1394 0.1199 0.1152 0.1571 0.3343 — 0.1192 0.1474 0.0859 0.1209 0.1205 0.1664 0.3432 0.0338 — 0.1255 0.1833 0.1848 0.1598 0.1192 0.126 0.1657 — 0.1205 0.1848 0.1199

122 Appendix B. Chapter 3 Beaver River, ON (LND) Beaver Creek, BC (LND) Coqui- halla River (LND) 05, except for those . 0 Fraser River (LND) P < Nor. Creek (LND) Kan. Creek NSD 0.0008 0.0943 0.0565 0.0929 0.1492 0.1354 Kan. Creek LND 0.0008 — 0.115 0.0736 0.1098 0.162 0.1597 Al. River NSD 0.0027 0.0374 0.0454 0.0874 0.0262 0.0484 0.1411 0.1208 Al. River LND 0.0027 — 0.0403 0.0531 0.0764 0.0363 0.0476 0.1422 0.1219 0005 0.0153 0.0234 0.039 0.0473 0.0972 0.0527 0.0596 0.1465 0.1118 . 0 Coq. River NSD − 0005 — 0.0208 0.039 0.045 0.0439 0.1084 0.0509 0.0587 0.1512 0.1258 . 0 Coq. River LND − Brunette River (NSD) Bertrand Creek (NSD) for all sampling locations, with each sympatric population broken down into subsets ST Satsop River (NSD) F Porter Creek (NSD) Al. R. NSD 0.0728 0.084 0.0555 0.2136 0.0234 0.039 Porter Cr.Satsop R.Bertrand Cr.Brunette R.Coq. R. LNDCoq. R. — NSD 0.1005 0.0227 0.3463Kan. 0.0799 Cr. — 0.0853 LNDKan. 0.0227 Cr. 0.2721 — 0.0906 NSD 0.0784 0.1005 0.2292 0.0867 0.0475 0.0799 0.3463 0.0747 — 0.0554 0.2292 0.0833 0.184 0.2721 0.0853 0.0792 0.2059 0.0475 0.0715 0.0784 0.0906 0.0375 — 0.184 0.0554 0.0379 0.0867 0.0755 0.264 0.0509 0.2237 0.2059 0.0868 0.0728 0.039 0.0555 0.0473 0.1874 0.084 0.0747 0.0375 0.045 0.0439 0.2136 0.0833 0.0792 0.0379 0.0454 0.264 0.1134 0.0374 0.0715 0.1144 0.0531 0.0682 0.0403 0.2237 0.128 0.0887 0.0883 — 0.3051 0.0926 0.1097 0.1704 0.312 0.1039 0.1664 0.1121 0.266 0.1549 0.1571 0.147 0.3343 0.3432 Al. R. LND 0.0755 0.0868 0.0509 0.1874 0.0153 0.0208 — Norrish Cr.Fraser R.Coquihalla R.Beaver Cr., BCBeaver R., ON 0.1134 0.0883 0.1704 0.128 0.0682 0.1039 0.1121 0.1549 0.0926 0.1144 0.1097 0.147 0.1664 0.0887 0.3051 0.266 0.3343 0.1571 0.312 0.0972 0.0596 0.1465 0.3432 0.1084 0.0527 0.0587 0.1512 0.1118 0.0874 0.0509 0.0484 0.1411 0.1258 0.0764 0.0262 0.0476 0.1422 0.1208 0.0943 0.0363 0.0929 0.1492 0.1219 0.115 0.0565 0.1098 0.162 0.1354 0.0736 — 0.0859 0.1597 0.1833 0.0808 0.0338 0.1657 0.126 — 0.0808 — 0.1199 0.1192 0.0859 0.1205 — 0.0338 0.1833 0.1192 0.1848 0.126 0.1657 0.1205 — 0.1848 0.1199 marked in gray. Table B.6: Pairwise for those dace with LND mtDNA and those with NSD mtDNA. All are signiﬁcant at

123