Examining patterns of genetic variation in Canadian marine molluscs through

DNA barcodes

by

Kara Layton

A Thesis

presented to

The University of Guelph

In partial fulfilment of requirements

for the degree of

Master of Science

in

Integrative Biology

Guelph, Ontario, Canada

© Kara Layton, January, 2012

ABSTRACT

Examining patterns of genetic variation in Canadian marine molluscs through

DNA barcodes

Kara Layton Advisor:

University of Guelph, 2013 Professor P.D.N Hebert

In this thesis I investigate patterns of sequence variation at the COI gene in Canadian marine

molluscs. The research presented begins the construction of a DNA barcode reference library for this

phylum, presenting records for nearly 25% of the Canadian fauna. This work confirms that the COI gene

region is an effective tool for delineating species of marine molluscs and for revealing overlooked

species. This study also discovered a link between GC content and sequence divergence between

congeneric species. I also provide a detailed analysis of population structure in two bivalves with similar

larval development and dispersal potential, exploring how Canada’s extensive glacial history has shaped

genetic structure. Both bivalve species show evidence for cryptic taxa and particularly high genetic

diversity in populations from the northeast Pacific. These results have implications for the utility of DNA

barcoding both for documenting biodiversity and broadening our understanding of biogeographic patterns

in Holarctic species.

iii

Acknowledgements

Firstly, I would like to thank my advisor Dr. Paul Hebert for providing endless guidance and

support during my program and for greatly improving my research. You always encouraged my

participation in field collections and conferences, allowing many opportunities to connect with colleagues

and present my research to the scientific community. I am also incredibly grateful for the invaluable

feedback and input I received from my committee members, Dr. Elizabeth Boulding and Dr. André

Martel. Your enthusiasm for this project always kept me motivated and I thank you for teaching me all

about the wonderful world of malacology.

I would like to thank the Natural Sciences and Engineering Research Council of Canada

(NSERC), the International Barcode of Life project and Genome Canada through the Ontario Genomics

Institute for funding this research. I am also grateful to Aboriginal Affairs and Northern Development

Canada for providing me with a Northern Scientific Training Program grant that aided in field collections

in Churchill, Manitoba. Lastly, I thank the Canadian Centre for DNA Barcoding (CCDB) for help in

sequence acquisition.

Research teams at the Kasitsna Bay Lab, the Churchill Northern Studies Centre and the

Huntsman Marine Science Centre provided logistical support for field collections and I am extremely

grateful for their help. I would also like to extend my gratitude to Sarah Hardy, Katrin Iken, Suzanne

Dufour, Barry McDonald, Robert Frank, Nicholas Jeffrey and Paolo Pierrossi for aid in specimen

collections.

A sincere thank you to all graduate students in the Hebert, Adamowicz, Smith, Crease, Hajibabaei

and Gregory labs for providing input into my project and contributing greatly to my happiness during this

program. I especially want to thank Christy Carr for her advice, patience and compassion- you truly are a

fantastic scientist. Finally, I need to thank my close friends and family, particularly my parents and Ryan,

who not only provided unconditional love but have shaped the person I am today.

iv

Table of Contents

Abstract……………………………………………………………………………………………………ii

Acknowledgements……………………………………………………………………………………….iii

List of Tables……………………………………………………………………………………………...vi

List of Figures……………………………………………………………………………………………vii

General Introduction………………………………………………………………………………...........1

Chapter 1: Patterns of DNA barcode variation in Canadian marine molluscs………………….........3

Abstract…………………………………………………………………………………………….........3

Introduction……………………………………………………………………………………………...4

Methods…………………………………………………………………………………………….........5

Specimen collection and data scrutiny………………………………………………………………..5

DNA extraction, amplification and sequencing……………………………………………………….5

Data analysis…………………………………………………………………………………………..6

Results…………………………………………………………………………………………………..7

Sequence recovery…………………………………………………………………………….............7

COI variation in molluscs……………………………………………………………………………..7

Variation in nucleotide composition…………………………………………………………………..8

Distribution of indels…………………………………………………………………………………..8

Discussion………………………………………………………………………………………………8

Sequencing success in Mollusca………………………………………………………………………8

Patterns of sequence variation…………………………………………………………………….......9

Insertions and deletions in COI……………………………………………………………………...10

Patterns of nucleotide composition…………………………………………………………………..11

Conclusions…………………………………………………………………………………………..12

Chapter 2: Geographic patterns of mtCOI diversity in two species of

Canadian marine bivalves……………………………………………………………………………23

Abstract………………………………………………………………………………………………..23

Introduction……………………………………………………………………………………………24

Methods………………………………………………………………………………………………..25

Specimen collection…………………………………………………………………………………..25

DNA extraction, amplification and sequencing……………………………………………………...26

Data analysis…………………………………………………………………………………………26

Results…………………………………………………………………………………………………27

Sequence recovery and haplotype diversity………………………………………………………….27

v

Patterns of genetic diversity…………………………………………………………………………28

Population structure…………………………………………………………………………………29

Discussion……………………………………………………………………………………………..30

Comparing diversity and structure in two bivalves with planktotrophic larval development……....30

Implications for glacial refugia in the northeast Pacific……………………………………………30

Evidence of sibling species…………………………………………………………………………..31

Conclusions…………………………………………………………………………………………..32

General Conclusions……………………………………………………………………………………..44

Summary of findings…………………………………………………………………………………44

The application of a DNA barcode library for Canadian marine molluscs………………………....44

Gene flow in marine populations…………………………………………………………………….45

Conclusions and implications for conservation……………………………………………………...46

Literature Cited………………………………………………………………………………………….47

Appendix A: Specimen Preservation…...……………………………………………………………....56

Appendix B: Species Identifications…………………………………………………………………....57

Appendix C: Chapter 1 Supplementary Material……………………………………………………..61

Appendix D: Chapter 2 Supplementary Material……………………………………………………..65

Appendix E: R Code……………………………………………………………………………………..74

vi

List of Tables

Chapter 1 ………………………………………………………………………………………………...13

Table 1.1. Parameter settings for each OTU algorithm………………….………...………………….13

Table 1.2. Percent success in recovery of a COI sequence …………....……………………………..13

Table 1.3. The number of COI sequences and BINs, intraspecific and nearest neighbour

distances and mean GC content for each of 33 orders………………………………………………...14

Table 1.4. Mean intraspecific divergence, number of genetic clusters, number of

individuals sampled and locality information for each potential cryptic species complex

in this study……………………………………………………………………………………………15

Table 1.5. Intra and interspecific distances (K2P) for taxonomic groups examined by

DNA barcoding in previous literature…………………………………………………...……………15

Chapter 2…………………………………………………………………………………………………33

Table 2.1. Genetic diversity in populations of the bivalve species, Hiatella arctica and

Macoma balthica……………………………………………………………………………………..33

Table 2.2. Overall genetic structure measured by AMOVA for Hiatella arctica…...………………..34

Table 2.3. Overall genetic structure measured by AMOVA for Macoma balthica balthica…...…….34

Table 2.4. Overall genetic structure measured by AMOVA for ATL Macoma balthica…..………...35

Table 2.5. FST for populations of Hiatella arctica, Macoma balthica balthica and

ATL Macoma balthica……………………………………………………………………………….36

Appendix B……………………………………………………………………………………………….57

Table B.1. References for species identifications………………...…………………………………58

Appendix C………………………..…………………………………………………………………...…61

Table C.1. List of COI primers used for molecular techniques in Chapter 1…………..……………61

Table C.2. List of GenBank specimens used for analysis in Chapter 1……………………………...61

Appendix D……………………………………………………………………………….………………65

Table D.1. Detailed collection information for all 172 Hiatella arctica specimens…………………65

Table D.2. Detailed collection information for all 196 Macoma balthica specimens.........................67

vii

List of Figures

Chapter 1…………………………………………………………………………………………………16

Figure 1.1. Sampling locations and the number of specimens examined in this study……...…….…16

Figure 1.2. Rarefaction curves for the five classes of Canadian marine mollusc

represented in this study……………...……………………………………………………………….17

Figure 1.3. Mean intraspecific divergences (K2P) and nearest neighbour distances

for all specimens in this study………………...………………………………………………………18

Figure 1.4. Maximum and mean intraspecific divergences plotted against the number

of individuals analyzed for 157 species……………………………………………………………….18

Figure 1.5. Box plots comparing mean nearest neighbour distance with the

number of species sampled from each genus with ≥ 2 representative species………………………..19

Figure 1.6. Neighbour-joining trees (K2P), with locality information, for 9 cryptic

species complexes in this study……………………………………………………………………….20

Figure 1.7. Mean nearest neighbour distance (K2P) plotted against mean GC

content for the 33 orders of Mollusca represented in this study………………………………………21

Figure 1.8. Secondary structure of COI marked with insertions and deletions for

gastropods and bivalves……………………………………………………………………………….22

Chapter 2…………………………………………………………………………………………………37

Figure 2.1. Collection sites for H. arctica and M. balthica…………………………………………..37

Figure 2.2. Intraspecific sequence divergence (K2P) for H. arctica and M. balthica……………......38

Figure 2.3. Neighbour-joining tree based on K2P distances for H. arctica. The top scale

shows estimated divergence time in millions of years while the bottom scale bar shows

sequence divergence…………………………………………………………………………………..39

Figure 2.4. Neighbour-joining tree based on K2P distances for M. balthica. Subspecies names

suggested by Väinölä (2003) are provided. The top scale shows estimated divergence time in millions

of years while the bottom scale bar shows sequence divergence........................................................40

Figure 2.5. Median-joining haplotype networks for H. arctica and M. balthica constructed

with maximum parsimony…………………………………………………………………………….41

Figure 2.6. FST values (Slatkin’s linearized) plotted against geographic distance for

populations within the main lineage of H. arctica…………………………………………………….42

Figure 2.7. FST values (Slatkin’s linearized) plotted against geographic distance for

populations of M. balthica balthica…………………………………………………………………...42

Figure 2.8. FST values (Slatkin’s linearized) plotted against geographic distance for

populations of ATL M. balthica………………………………………………………………………43

viii

Appendix B……………………………………………………………………………………………….57

Figure B.1. Hinge dentition in bivalves………………………………………………………...…….59

Figure B.2. View of girdle scales on Tonicella marmorea and Tonicella rubra…………......……...60

Appendix C………………………………………………………………………………………………61

Figure C.1. Neighbour-joining tree (K2P) for all barcoded specimens……………………………...62

Appendix D……………………………………………………………………………………………….65

Figure D.1. Calculations for DXY, Tajima’s D, FST and Bray Curtis index……………….………….70

Figure D.2. Bray Curtis similarity values between populations of Hiatella arctica…………………71

Figure D.3. Bray Curtis similarity values between populations of Macoma balthica…….…………72

Figure D.4. Images of hinge dentition in each of the four cryptic lineages of Hiatella arctica……..73

1

General Introduction

The discovery and quantification of marine biodiversity is crucial for evaluating ecosystem

structure and interactions, factors of growing importance in the face of climate change (Archambault et al.

2010). Species discovery also forms a basis for conservation and restoration of marine biodiversity

(Snelgrove 2010). Traditional approaches to species discrimination have relied solely on morphological

traits and often resulted in cryptic species going undetected (Hebert et al. 2003). In addition, the

identification of specimens was often impossible because of life stage diversity or damage to diagnostic

traits (Hebert et al. 2003, Carr et al. 2010, Radulovici et al. 2010). These facts make clear the need to

integrate molecular data into species delineation techniques. The use of the cytochrome c oxidase subunit

1 (COI) gene as a genetic barcode for animal taxa has created a standardized approach for species

delineation (Hebert et al. 2003). DNA barcoding is based on the observation that sequence divergences

among species are generally much greater than those within species (Hebert et al. 2003). DNA barcoding

studies typically employ distance-based methods to calculate intra and interspecific divergences, gaining

insight into sequence variation both within species and among congeners (Zou et al. 2011). Prior studies

have shown that a sequence divergence threshold of 2% is effective in delineating many animal species

(Hebert et al. 2003, Witt et al. 2006, Kerr et al. 2009). Moreover, such analysis often highlights cases of

cryptic diversity, particularly in marine species with broad ranges (Knowlton 2000, Carr et al. 2010,

Bucklin et al. 2011). The frequent discovery of distinct genetic clusters in past investigation on species

with Holarctic distributions suggests that genetic studies are a critical element if one wishes to accurately

quantify marine biodiversity (Knowlton 2000, Carr et al. 2010, Bucklin et al. 2011).The extension of

knowledge on biodiversity in Canada’s marine ecosystems is critical because of stressors originating from

climate change, eutrophication, acidification, overfishing and pollution (Archambault et al. 2010). In

addition, human-mediated dispersal, linked to increased vessel traffic, has resulted in invasive species

replacing the natural marine fauna (Carlton 1999, Bax et al. 2003, Archambault et al. 2010). These

invasive species not only threaten environmental functions, but hurt the economy through impacts on

commercially important fish and invertebrate species.

My thesis extends knowledge of Canadian species diversity in the most diverse marine phylum,

the Mollusca (Bouchet et al. 2002). This phylum is not only speciose, but ubiquitous as its member

species occupy diverse habitats and posses varied life history strategies. This study not only uses COI for

delineating species, but also integrates these results with traditional taxonomic methods to provide a

multi-dimensional approach to species identification. Exploring genetic variation in marine molluscs

provides insight into diversity patterns in Canadian oceans and will aid future conservation strategies.

2

Chapter 1 reports progress in the assembly of a barcode reference library for Canada’s marine

molluscs. This chapter explores patterns of sequence variation, both within and between species, and also

provides a detailed analysis of variation in GC content across molluscan orders. In addition, it probes taxa

with deep intraspecific divergences with a view towards the revelation of potential cryptic complexes

while also providing comparative analyses of species identification techniques. Chapter 2 involves a

focused examination of sequence variation and population structure in two bivalve species with

planktonic larval development and a Holarctic distribution. This study was motivated by recent evidence

for deep sequence divergence and phylogeographic partitioning in other marine species which were once

thought to possess broad ranges (Knowlton 2000, Carr et al. 2010, Bucklin et al. 2011). Furthermore, it

has been shown that patterns of population structure can differ in species with similar life history

strategies, a result that likely reflects their differential responses to the glacial cycles that have impacted

Canada’s coasts for much of the last two million years (Bernatchez & Wilson 1998). In fact, repeated

glaciations have played a key role in shaping contemporary patterns of species distribution and population

divergence in Canadian waters (Bernatchez & Wilson 1998, Wares & Cunningham 2001).

This thesis demonstrates that COI is a useful tool for analyzing patterns of genetic variation in

marine molluscs, on local and regional scales. It is also useful for highlighting cases of deep intraspecific

divergence that may signal cryptic species. In this study, COI was successful in clarifying patterns of

intraspecific variation in two bivalve species, providing evidence for unrecognized species in both taxa,

and identifying locations of potential glacial refugia. This work highlights the need for greater effort at

species documentation, not only to further understanding of marine biodiversity, but also to aid

conservation and the implementation of marine protected areas.

3

Chapter 1

Patterns of DNA Barcode Variation in Canadian Marine Molluscs

Abstract

A 648 base pair segment of the cytochrome c oxidase subunit 1 gene has proven useful for the

identification and discovery of species in many animal lineages. This study begins the assembly of a

comprehensive barcode reference library for Canadian marine molluscs, examining patterns of sequence

variation in 234 morphospecies, about 25% of the marine mollusc fauna in Canada. These taxa showed a

mean intraspecific sequence divergence of 0.51%, while congenerics showed a mean divergence of

13.5%. Nine cases of deep (>2%) intraspecific divergence were detected, suggesting possible overlooked

species. Structural variation was detected in the barcode region with indels in 38 species, most (71%) in

bivalves. GC content varied from 32 – 43% and there was a significant positive correlation between GC

content and nearest neighbour distance.

4

Introduction

DNA barcoding employs sequence diversity in a 648 base pair region of the cytochrome c

oxidase subunit 1 (COI) gene to distinguish species (Hebert et al. 2003, Kerr et al. 2009, Carr et al. 2010).

Past work has shown that sequence divergences are generally much greater between than within species

(Hebert et al. 2003). Because of this fact, DNA barcoding aids both the identification of known species

and the discovery of overlooked taxa (Witt et al. 2006). The latter application has revealed that the

incidence of sibling species is often high enough to lead to serious inaccuracies in estimates of

biodiversity (Knowlton 2000, Carr et al. 2010). In light of this, it is increasingly recognized that

molecular approaches need to be incorporated into biodiversity surveys. DNA barcoding is a particularly

useful tool for groups with high diversity, especially those which have seen little taxonomic attention.

Although marine molluscs have been the subject of considerable research, the number of species in

Canadian waters remains uncertain with estimates ranging from 700 to 1200. In part, this uncertainty

reflects taxonomic problems linked to the fact that molluscs are the most diverse phylum of marine life

with more than 50,000 described species (Bouchet 2006). In addition, molluscs exhibit complex larval

stages, frequent cryptic taxa and substantial phenotypic plasticity, all factors that impede morphological

approaches to species identification (Drent et al. 2004, Marko & Moran 2009). In fact, the differing

juvenile stages of molluscs have sometimes been treated as distinct species generating inaccurate

estimates of biodiversity and geographic ranges (Johnson et al. 2008). The extreme phenotypic plasticity

in shell morphology which is common in molluscs poses additional problems for taxonomy (Johnson et

al. 2008, Zu et al. 2011). Because traditional morphological approaches to identification confront so many

challenges in molluscs, it is imperative to integrate molecular diagnostics into this process.

Several prior studies have validated the efficacy of DNA barcoding in the discrimination of

mollusc species, but most of this work has targeted a particular order or family. For instance, Meyer and

Paulay (2005) presented a detailed study of barcode diversity in cowries (Family: Cypraeidae),

demonstrating the general effectiveness of the approach, but showing that a fixed sequence threshold

could not be used for species diagnosis. They concluded that DNA barcoding was a powerful aid for

mollusc identification when paired with strong taxonomic validation and comprehensive sampling. More

recent studies have extended these results by establishing the value of DNA barcoding in resolving

cryptic species complexes in the molluscan families Muricidae, Thyasiriidae, Yoldiidae, Nuculidae and

Lepetodrilidae (Mikkelsen et al. 2007, Johnson et al. 2008, Zou et al. 2012). While Zou et al. (2011)

found that distance-based analyses were less effective than those based on characters, the former

approach successfully delineated 40 Chinese neogastropod species.

Despite the demonstrated utility of DNA barcoding in molluscs, no study has aimed to assemble a

comprehensive barcode registry for a large geographic region. The present investigation addresses this

5

deficit, beginning the construction of a DNA barcode reference library for Canadian marine molluscs. It

compares intra and interspecific divergences among 33 molluscan orders with prior values, and also

examines the utility of different approaches for the designation of OTUs (Operational Taxonomic Units)

based on the analysis of sequence variation at COI. This study also investigates variation in nucleotide

composition among molluscs and how this property impacts levels of genetic divergence. Finally,

insertions and deletions in the COI region are analyzed for all molluscs examined in this study.

Methods

Specimen collection and data scrutiny

A total of 2471 specimens were collected from 2007 to 2012 at sites across Canada (Figure 1.1).

Figure 1.2 provides rarefaction curves for each class as a measure of sampling efficiency. Specimen

details, sequences and trace files are available on BOLD (www.boldsystems.org, Ratnasingham & Hebert

2007), while the specimens are held at the Biodiversity Institute of Ontario. Typically five specimens per

species were collected from intertidal or subtidal habitats using plankton nets, small dredges and SCUBA

diving, but samples from the Beaufort Sea were collected from deep subtidal soft-bottom habitats using

an Agassiz trawl. Specimens were immediately fixed in 90-100% ethanol, with regular replacement of

ethanol to prevent its dilution. During fixation, the opercula of gastropods and the shells of bivalves were

separated to ensure preservation of internal tissues. After each collecting trip, specimens were placed in

fresh 95% ethanol and stored at -20C. When possible, specimens were identified to a species level with

name usage following the World Register of Marine Species (WoRMS). Approximately 3% of the

barcoded specimens could not be identified to a species-level because they were immature, but they were

assigned to a genus and an interim species.

DNA extraction, amplification and sequencing

Doubly uniparental inheritance (DUI) has been found in some bivalve groups and is characterized

by the transmission of a maternal and paternal mitochondrial lineage through eggs and sperm,

respectively (Ghiselli et al. 2012). While DUI can cause deep divergences between male and female

conspecifics avoiding gonadal tissue during sampling can resolve this problem given that male somatic

tissue is still dominated by the female genome (Passamonti & Ghiselli 2009, Zouros 2012). In turn, DNA

extracts were prepared from a small sample of muscle tissue from each specimen. Tissue samples were

placed in cetyltrimethylammonium bromide (CTAB) lysis buffer solution with proteinase K and

incubated for 12 hours at 56°C. DNA was then extracted using a manual glass fibre plate method

(Ivanova et al. 2008). After incubation, the DNA was eluted with 40 µl of ddH2O. After re-suspension, 2

µl of each DNA extract was placed into a well into another additional plate and 18 µl of ddH2O was

added to dilute salts or mucopolysaccharides that might inhibit PCR. Three primer sets were employed to

maximize amplicon recovery (dgLCO1490/dgHCO2198, LCO1490_t1/HCO2198_t1 and

6

BivF4_t1/BivR1_t1). The primer set that generated an amplicon for a particular specimen, and the primer

sequences are available on BOLD. A primer cocktail (C_LepFolF/C_LepFolR) was used in a second

round of PCR for specimens that failed to amplify in the first round of PCR. Each well was filled with 2

µl of diluted DNA and the following reagents were added to total a 12.5 µl PCR reaction: 6.25 µl 10%

trehalose, 2 µl ddH20, 1.25 µl 10× PCR buffer, 0.625 µl MgCl2 (50 mM), 0.125 µl of each forward and

reverse primer (10 µM), 0.0625 µl dNTP (10 mM) and 0.06 µl Platinum Taq polymerase. The

thermocycling regime consisted of one cycle of 1 min at 94°C, 40 cycles of 40 s at 94°C, 40 s at 52°C,

and 1 min at 72°C, and finally 5 min at 72°C. E-Gels (Invitrogen) were used to screen for amplification

success and all positive reactions were bidirectionally sequenced using BigDye v3.1 on an ABI 3730xl

DNA Analyzer (Applied Biosystems). Sequences were manually edited using CodonCode Aligner and

aligned both by eye in MEGA5 and through the BOLD aligner algorithm (CodonCode Corporation,

Tamura et al. 2011). MEGA5 was also used to assess the prevalence and location of insertions and

deletions (indels) (Tamura et al. 2011). Sequences containing more than 1% ambiguities, stop codons,

double peaks or that were shorter than 220 bp were removed from further analysis. Sequencing success

was assessed for this study and a Pearson’s Chi-Squared test was used to determine whether significant

differences existed between sequence recovery in each class.

Data analysis

A Kimura-2-parameter (K2P) distance model was employed in MEGA5 to construct a neighbour-

joining (NJ) tree which served as a preliminary basis for species recognition (Kimura 1980, Tamura et al.

2011). Genetic distances, including intra and interspecific divergence along with nearest-neighbour

distance, were calculated with the K2P distance model (Kimura 1980) and overall data were compared

using the ‘Distance Summary’ and ‘Barcode Gap Analysis’ tools on BOLD (Ratnasingham & Hebert

2007). Maximum intraspecific divergence was plotted against nearest neighbour (NN) distance to

determine how often NN distances surpassed intraspecific divergences, ultimately indicating the presence

of a barcode gap. In addition, the ‘Sequence Composition’ tool on BOLD was used to examine variation

in GC content among species in each order (Ratnasingham & Hebert 2007). Species numbers were

determined by two approaches: i) morphology, through the examination of shell characters and soft tissue,

and ii) through the analysis of sequence divergence patterns at COI to ascertain the number of sequence

clusters present. The morphological approach involved the use of the national mollusc collection

deposited at the Canadian Museum of Nature in Gatineau, Québec. The latter approach employed four

algorithms designed for this purpose - Barcode Index Number (BIN) (Ratnasingham and Hebert 2013),

Automated Barcode Gap Discovery (ABGD) (Pulliandre et al. 2011), Clustering 16S rRNA for OTU

Prediction (CROP) (Hao et al. 2011) and jMOTU (Jones et al. 2011). The BIN algorithm only analyzed

7

sequences greater than 500 bp in length while the other three algorithms examined all sequences greater

than 400 bp. Parameter settings for each OTU algorithm can be found in Table 1.1.

Various packages in Revolution R were used to analyze levels of sequence variation. The Picante

and VEGAN packages were used to generate rarefaction curves to assess sampling effort for each class of

mollusc (Dixon 2003, Kembel et al. 2010). These software packages were also used to perform linear

regressions to determine if the number of individuals sampled within a species impacted values of

intraspecific divergence (Dixon 2003, Kembel et al. 2010). P-values less than 0.05 were considered

significant. Finally, these packages were used to generate a box plot for comparing mean nearest

neighbour distance between genera with two or more species sampled (Dixon 2003, Kembel et al. 2010).

For box plot analysis, significance was determined from an analysis of variance (ANOVA). A chi-square

test of homogeneity was used to determine whether nucleotide frequency was homogeneous among

classes. Species with intraspecific divergences greater than 2% were treated as potential cryptic

complexes. Genetic variation, both within and between species, as well as the number of individuals and

genetic clusters, was analyzed for each cryptic species. Lastly, the boot and Hmisc packages were used to

test whether mean nearest neighbour distance was correlated with mean GC content in molluscan orders

(Harrell Miscellaneous 2012).

Results

Sequence recovery

Among the 2471 marine mollusc specimens analyzed, 1334 COI sequences were recovered from

234 morphospecies. The LCO1490_t1 and HCO2198_t1 primer set, along with a 1:10 dilution of DNA

and an annealing temperature of 52°C for amplification, generated the highest success in sequence

recovery. Success rates were significantly different among chitons, bivalves and gastropods (p <.0001)

(Table 1.2). Sequences ranged in length from 223 to 658 bp, but 88% were greater than 600bp. Table 1.3

displays the number of sequences and species examined in the 5 classes and 33 orders represented in this

study, along with measures of intra and interspecific divergence and GC content. The overall mean

values for intra and interspecific divergence were 0.51% and 13.5% respectively. Values of maximum

intraspecific divergence ranged from 0 to 30.58%.

COI variation in marine molluscs

Morphological study indicated the presence of 234 species; 77 were represented by a single

specimen while the other 157 species had an average of 8 specimens (range 2-67). All but one of these

species had one or more sequence records >400 bp in length. A barcode gap was present for the majority

of species in this study with exceptional cases likely reflecting cryptic complexes (Fig 1.3). Algorithms

for OTU determination generated estimates of 242 (BIN), 255(jMOTU), 270(CROP) and 271(ABGD).

However, 72 specimens representing 27 morphologically identified species lack BIN assignments because

8

their sequence records were <500bp. The BIN, jMOTU, CROP and ABGD algorithms generated 71, 97,

100 and 114 singletons, respectively. Values for intraspecific variation, both maximum and mean, were

not significantly associated with the number of individuals analyzed (Figure 1.4; p = 0.71, p = 0.40).

Furthermore, mean nearest neighbour distances were not correlated with the number of species analyzed

from a genus (Figure 1.5; p=0.07). Lastly, nine species in this study demonstrated deep intraspecific

divergences that were greater than 2%. Table 1.4 provides values of mean intraspecific divergence, the

number of individuals and clusters and locality information detailing the distribution of each cryptic

species across Canadian oceans. Figure 1.6 provides neighbour-joining trees (K2P) for each of the nine

cases where deep intraspecific divergence was detected.

Variation in nucleotide composition

Mean GC content averaged 37.1% for all species (range 32-43%). A chi-square test of homogeneity

demonstrated that nucleotide frequencies were not identical among species in each of the five molluscan

classes (p <0.001). Mean nearest neighbour distances appeared positively correlated with mean GC content,

with a correlation coefficient of 0.51, suggesting that as GC content increases across orders so does the

genetic distance between congeneric species (Figure 1.7; p=0.002).

Distribution of indels

Indels were only detected in two of the five classes, Bivalvia and Gastropoda, but they occurred

in nearly half (47%) of the bivalve species versus just 9% of the gastropods. Indels were detected in 27

bivalve species involving representatives of 12 families, and in 11 species of gastropods from 4 families.

Indels were conserved in 7 of the 10 bivalve families, but varied between genera in 2 families and

between species in 1 family. Indels were conserved in 3 of the 4 gastropod families but varied between

genera in the Lottidae. In bivalves, 1 deletion was observed in Cyclocardia borealis, Mactromeris

polynyma, Saxidomus gigantea and in all six Tellinidae species while 2-3 deletions occurred in

Delectopecten greenlandicus, Astarte montagui and both Crassostrea species. Moreover, 1 insertion

occurred in all species of Myidae, Mytilidae, Arcidae and Glycymerididae, while three insertions

occurred in all species of Thyasiridae. Finally, 1 insertion and 3 deletions occurred in Astarte borealis

while 2 insertions and 3 deletions occurred in Cyclocardia crassidens. In gastropods, all five Lottia

species had one insertion and all Pyramidellidae (Boonea cf. bisuturalis, Odostomia sp.1, Odostomia sp.

2) and Onchidiidae (Onchidella borealis and Onchidella cf. carpenteri) species had one deletion, while

Limacina helicina had four deletions. Indels ranged in length from 3 to 18 nucleotides in bivalves and

from 3 to 12 nucleotides in gastropods. All indels were in multiples of 3 nucleotides, suggesting they did

not derive from pseudogenes. Indels were mapped onto the secondary structure of COI and often

appeared in proximity to external loops (Figure 1.8).

9

Discussion

Sequencing success in Mollusca

Most of the sequences recovered in this study were greater than 600 bp, but some were as short as

250 bp, reflecting the use of internal primers. This study employed multiple rounds of PCR, tested different

primer cocktails and modified PCR regimes to minimize contamination and pseudogene recovery. These

optimization studies revealed that a 1:10 dilution of DNA, coupled with tailed Folmer primers and a PCR

regime with an annealing temperature of 52°C, generated the highest success. This protocol usually

produced single amplicons that lacked evidence of pseudogene amplification. Mucopolysaccharides are

present in many mollusc groups and because they are not always removed during DNA extraction, they can

interfere with DNA polymerase, reducing PCR amplification success. Because only half of the specimens

generated an amplicon, significant challenges in barcode recovery remain. No barcode sequences were

recovered from six species (Anomia simplex, Astarte crenata, Astarte moerchi, Littorina scutulata,

Notoacmea testudinalis and Argopecten irradians). Sequencing success significantly differed between

classes, with polyplacophorans delivering the highest success (82.8%) and bivalves the lowest success

(32.9%). Future work should focus on the development of primer sets that target specific lineages to

generate greater success in sequence recovery. Despite the challenges with some mollusc groups, this study

still presents sequence records for nearly 25% of the Canadian marine mollusc fauna.

Patterns of sequence variation

Considering all 156 morphospecies represented by two or more records, Canadian marine molluscs

showed a mean intraspecific divergence of 0.51%, a value lower than that reported for echinoderms (0.62%)

but higher than in polychaetes (0.38%), marine fishes (0.39%) and decapods (0.46%)(Ward et al. 2005,

Costa et al. 2007, Ward et al. 2008, Carr et al. 2010). The relatively high intraspecific divergence in

Canadian molluscs likely reflects, at least in part, the impact of overlooked species. For instance, a mean

intraspecific divergence of 16.8% was detected for Tachyrhynchus erosus in this study, while Sun et al.

(2011) found a far lower mean intraspecific divergence (0.44%) for species in this order (Caenogastropoda).

When the nine cases of deep sequence divergence detected in this study were excluded, mean intraspecific

divergence dropped to 0.42%. In any case, there was little overlap between intra and interspecific

divergence (Figure 1.3), suggesting that DNA barcoding is very effective in delineating marine mollusc

species.

Levels of sequence variation in this study differed greatly from some other recent studies (Table

1.5). While interspecific divergences were consistently high among taxa, some cases of high intraspecific

divergence have been reported (Table 1.5). This difference may reflect the fact that many prior studies

focused on a single genus or family while this study involved a phylum-wide analysis, potentially allowing

for greater coverage of sequence variation in the former. However, the high intraspecific values in the

10

literature may also reflect misidentifications which would inflate values of within species divergence.

However, the present study does confirm the particularly high interspecific variation in the Vetigastropoda

(Table 1.5)(Meyer & Paulay 2005). Members of this order are a diverse group of gastropods that inhabit

diverse marine environments from the shallow intertidal to deep-sea hydrothermal vents. Their success in

invading such a variety of habitats may be facilitated by a high evolutionary rate that is reflected in high

levels of sequence divergence between congeneric taxa (Colgan et al. 1999).

This study revealed nine taxa in which intraspecific divergences were greater than 2% (Table 1.4;

Figure 1.6). Prior work has established that deep mtDNA divergences do really exist in some mollusc

species, such as the land snail, Cepaea nemoralis, where distances reach 12.9% (Thomaz et al. 1996).

However, in most other cases, deep divergences, especially those involving allopatric lineages, are now

thought to represent different species. For example, the deep divergences in populations of the bipolar

pteropods Limacina helicina and Clione limacina are now viewed as evidence for separate species in the

Arctic and Antarctic (van der Spoel & Dadon 1999, Hunt et al. 2010, Jennings et al. 2010). This study

extends the earlier conclusion, suggesting the possible presence of two Clione species in the Arctic Ocean

although their divergences are far lower than the major Antarctic/Arctic lineages (Table 1.4; Figure 1.6).

Glaciation in Canada has played a key role in shaping the genetic structure of contemporary populations and

may be responsible for the segregation and differentiation of lineages on opposing coasts (Hewitt 1996,

Bernatchez & Wilson 1998, Wares & Cunningham 2001, Maggs et al. 2008, Dapporto 2009). For example,

the deeply divergent lineages of Hiatella arctica, Macoma balthica and Tachyrhynchus erosus detected in

this study may represent sibling species whose origin is linked to isolation in glacial refugia (Table 1.4;

Figure 1.6, Layton unpublished). Moreover, the two distinct clusters of Mya truncata and Mya arenaria

found in this study may include one of three cryptic species of Mya recorded from the Arctic Ocean

(Peterson 1999). In any case, deep intraspecific divergences often flag overlooked species in a lineage (Witt

et al. 2006). For instance, DNA barcoding unveiled five cryptic species complexes in the Leptodrilidae, a

family of limpets inhabiting deep-sea hydrothermal vents (Johnson et al. 2008). Similarly, COI analysis

unveiled that the cold-seep bivalve species, Acesta bullisi, should be separated into two species (Järnegren et

al. 2007). Not only have these discoveries been made in molluscs, but DNA barcoding has revealed

overlooked diversity in numerous marine taxa, including fishes of Pacific Canada and asteroid species of

southeast Australia (Naughton & O’Hara 2009, Steinke et al. 2009a).These results highlight the need for

integrating molecular approaches into species identifications. Future work should focus on evaluating

population structure in these potential cryptic cases and include a more detailed examination of genetic

variants.

Insertions and deletions in COI

11

This study has demonstrated that indels are considerably more prevalent in bivalves than in other

molluscan classes. Interestingly, high rates of nucleotide substitution occur in lineages containing indels

(Tian et al. 2008). For instance, a high mutational load was observed in oysters (Crassostrea) and mussels

(Mytilus), genera for which three deletions and one insertion were detected in this study (Figure 9)

(Hedgecock et al. 2004, An and Lee 2012). In fact, Vetsigian and Goldenfeld (2005) found that indels

stimulate diversification fronts in the genome and over a short time can facilitate sequence divergence

between populations. Moreover, the discovery of a single amino acid deletion in the Heterostropha and

Pulmonata groups corroborates findings from Grande et al. (2004) which suggest this deletion may either be

due to convergence, as a result of a length constraint, or several deletions arising in some pulmonates and

the Heterstropha (Figure 9). Most indels in bivalves and gastropods occurred in close proximity to the first

loop, a pattern that Remigio and Hebert (2003) also observed in Heterobranchia and Patellogastropoda

(Figure 9). Furthermore, the presence of a single amino acid insertion in the Lottidae (Patellogastropoda)

and four amino acid deletions in Limacina helicina may be correlated to accelerated rates of substitution in

these groups (Remigio and Hebert 2003). Four bivalve orders demonstrated a single insertion event at

position 37 (Figure 9). This insertion likely arose independently in each order as it was not possessed by all

descendents of the monophyletic group (Plazzi et al. 2011). Interestingly, Mikkelsen et al. (2007) discovered

that Thyasira specimens harboured 3 to 4 additional codons in the COI gene, a pattern corroborated by our

finding of a three amino acid insertion in all Thyasiridae specimens. Together with prior work, the present

study has established that insertions and deletions in the COI gene are relatively common in molluscs,

suggesting that future work should aim to determine the functional relevance of this sequence variation as

well as its implications for rates of molecular evolution. Finally, indel patterns are useful for inferring

phylogenetic relationships, a particularly important application for a phylum which often faces taxonomic

scrutiny.

Patterns of nucleotide composition

After examining the frequency of each nucleotide (A, T, G and C) in all sequences, and grouping

these sequences by class, heterogeneity was apparent in nucleotide frequencies between each class.

Differences in nucleotide composition can provide insight into ratios of nonsynonymous to synonymous

substitutions and may also be linked to extrinsic factors (Albu et al. 2008). For instance, Dixon et al. (1992)

found a positive correlation between rDNA melting temperature and environmental temperature in

hydrothermal-vent polychaetes. Dixon et al. (1992) interpreted this finding to mean that organisms living in

more extreme conditions will have a higher GC content. While Wu et al. (2012) recognize that

environmental factors may impact GC content, they suggest that GC content is likely governed by more

intrinsic controls, particularly mutator genes. We also discovered a moderate, positive correlation between

GC content and sequence divergence between congeneric taxa, although unequal sampling between orders

12

may have contributed to this finding. Future work should aim to remove sampling bias and incorporate

phylogenetic analyses to further probe this relationship. Examining GC content in a lineage can also provide

insight into whole-genome patterns. For instance, Clare et al. (2008) found that sequence composition

patterns in COI are likely reflective of patterns in the entire mitochondrial genome, potentially having

implications for rates of mitochondrial evolution. Moreover, Wu et al. (2012) found that increased genome

size in bacteria is based on an increase in GC content of the genome. In light of these findings, future work

should focus on comparing GC content between taxa for which ecological and genomic data are available.

Conclusions

There is a pressing need to gain a more detailed understanding of Canadian biodiversity. Although

barcode records are available for some groups, Canadian marine molluscs have, in the past, been the subject

of very little attention with regards to molecular taxonomy. The present study begins to address this gap,

providing barcode coverage for 25% of the Canadian fauna and establishing the effectiveness of this

approach in delineating species of marine molluscs. Because 30% of the species in this study were

represented by a single specimen, future studies should extend sample sizes for such cases to confirm that

they do not contain cryptic species. This study has revealed that DNA barcoding is not only useful for

documenting biodiversity, but also for unveiling patterns of genetic variation and sequence composition

across a broad taxonomic group on a large geographic scale.

13

Table 1.1. Parameter settings for each OTU algorithm.

OTU Algorithm Parameter Settings

jMOTU 2% threshold = 13 bp differences

low BLAST identity filter = 99

sequence alignment overlap = 60% of min. sequence length

CROP l 0.3 -u 0.5 (1%)

l 0.6 -u 1 (2%)

s (3%)

l 1.2 -u 2 (4%)

g (5%)

ABGD Pmin = 0.0006

Pmax = 0.17

Table 1.2. Percent success in recovery of a COI sequence for specimens (n=1964) in three classes of

Mollusca.

Class Specimens Processed Sequencing Success

Bivalvia 586 32.9%

Gastropoda 1256 42.5%

Polyplacophora 122 82.8%

14

Table 1.3. The number of COI sequences and BINs, intraspecific and nearest neighbour distances and

mean GC content for each of 33 orders. *denotes no BIN(s) assigned.

Class Order Sequences BINs

mean intra (%

K2P) (SE)

mean inter (%

K2P) (SE)

mean GC

(%) (SE)

Bivalvia Arcoida 5 2 0.26 (0.03) 0 (0) 37.3 (0.7)

Carditoida 4 2 0.11 (0.03) 58.2 (0.07) 35.0 (0.8)

Euheterodonta 42 10 3.5 (0.04) 19.8 (0.03) 36.3 (0.3)

Myoida 2 0* 0.47 0 (0) 40.5

Mytiloida 147 8 0.39 (0) 17.5 (0) 37.5 (0.1)

Nuculanoida 38 8 0.20 (0.002) 31.8 (0.9) 39.4 (0.5)

Ostreoida 21 3 0.29 (0.009) 25.7 (0.008) 39.0 (0.3)

Pectinoida 10 1 0.07 (0.004) 0 (0) 42.6 (0.6)

Pholadomyoida 3 2 0 (0) 0 (0) 38.5 (0.2)

Veneroida 112 20 0.4 (0.001) 19.3 (0.004) 35.5 (0.3)

Cephalopoda Decapodiformes 12 2 0.09 (0.002) 8.0 (0.2) 35.0 (0.2)

Myopsida 3 1 0.06 (0.03) 0 (0) 39.2 (0.3)

Octopoda 2 2 0 (0) 0 (0) 32.5

Oegopsida 22 2 0.65 (0.003) 0 (0) 37.8 (0.1)

Gastropoda Archaeogastropoda 55 11 0.12 (0.002) 21.0 (0.02) 39.4 (0.3)

Caenogastropoda 7 3 16.8 (0.7) 0 (0) 39.9 (3.2)

Cephalaspidea 28 6 0.65 (0.007) 11.8 (0.04) 31.9 (0.3)

Docoglossa 10 1 0.15 (0.004) 0 (0) 41.8 (0.09)

Gymnosomata 17 2 2.2 (0.01) 0 (0) 37.6 (0.1)

Heterostropha 4 3 0.31 (0) 21.5 (0) 35.8 (1.0)

Littorinimorpha 223 23 0.43 (0.01) 9.6 (0.06) 37.4 (0.1)

Mesogastropoda 1 1 0 (0) 0 (0) 39.1

Neogastropoda 162 40 0.23 (0.001) 8.4 (0.001) 35.3 (0.1)

Nudibranchia 107 29 0.63 (0.1) 20.2(0.3) 37.2 (0.2)

Opisthobranchia 12 4 0.24 (0.009) 0 (0) 34.0 (1.3)

Patellogastropoda 52 6 0.17 (0.001) 23.9 (0.004) 38.4 (0.6)

Pulmonata 27 3 0.57 (0.003) 11.1 (0.01) 34.6 (0.03)

Sorbeoconcha 19 4 0.45 (0.009) 19.3 (0.03) 38.2 (1.5)

Vetigastropoda 17 6 0.11 (0.004) 39.0 (0.4) 39.9 (0.8)

Polyplacophora Chitonida 115 15 0.54 (0) 15.4 (0.003) 36.6 (0.2)

Neoloricata 29 7 1.1 (0.007) 16.8 (0.007) 36.7 (0.3)

Scaphopoda Dentaliida 20 11 7.2 (0.8) 14.6 (0.2) 32.3 (0.5)

Gadilida 6 4 0.22 (0.1) 0 (0) 35.8 (0.9)

15

Table 1.4. Mean intraspecific divergence (% K2P), number of genetic clusters, number of individuals

sampled and locality information for each potential cryptic species complex in this study. *(Layton

unpublished)

Table 1.5. Intra and interspecific distances (% K2P) for taxonomic groups examined by DNA barcoding in

previous literature. These values are compared to values obtained from similar taxa investigated in this

study.

Species N Mean Intra Clusters Cluster Locality

Clione limacina 17 2.15 2 2 Arctic

Mya arenaria 13 2.45 2 1 Pacific/Atlantic, 1 Pacific

Cryptonatica affinis 8 2.47 2 2 Arctic

Hiatella arctica* 172 3.9 4 1 Tri-oceanic, 2 Pacific, 1 Atlantic

Mya truncata 6 4.41 2 2 Arctic

Macoma balthica* 163 7.7 3 1 Pacific/Arctic, 2 NW Atlantic

Thyasira gouldi 7 10.04 2 2 Atlantic

Tachyrhynchus erosus 7 16.81 3 2 Arctic, 1 Atlantic

Triopha catalinae 3 17.99 2 2 Pacific

Group

This Study:

Mean intra

Mean inter

Literature:

Mean intra

Mean inter

Citation

Muricidae 0.12 6.5-11.4 0.4 6.7-25.2 Zou et al. 2012

Littorinimorpha1

0.43 9.6 0.81 - Meyer & Paulay

2005

Turbinidae 0.07 - 0.18 - Meyer & Paulay

2005

Lottidae 0.17 23.9 0.25 - Meyer & Paulay

2005

Vetigastropoda2

0-0.34 38.3-39.7 0.10-1.34 3.01-31.25 Johnson et al.

2008

Neogastropoda 0.23 8.4 0.64 8.1 Zou et al. 2011 1 Cypraeidae used in literature but no Canadian representatives of this family 2 Lepetodrilidae used in literature but no representatives collected in this study

16

Figure 1.1. Sampling locations and the number of specimens examined in this study. Sequences obtained

from GenBank are not included as they lack locality information.

17

Figure 1.2. Rarefaction curves for the five classes of Canadian marine mollusc represented in this study.

Plot A provides curves for Bivalvia and Gastropoda while plot B provides curves for Cephalopoda,

Polyplacophora and Scaphopoda.

A)

B)

18

Figure 1.3. Maximum intraspecific divergence plotted against nearest neighbour distance for all species in

this study. All data points falling above the 1:1 line indicate a barcode gap is present for these species.

Figure 1.4. Maximum and mean intraspecific divergences (% K2P) plotted against the number of

individuals analyzed for 157 species. The regression between sample size and both maximum and mean

divergences were insignificant (p = 0.71, p = 0.4, respectively).

19

Figure 1.5. Box plots comparing mean nearest neighbour distance (% K2P) with the number of species

sampled from each genus with ≥ 2 representative species (N=43). The ANOVA was insignificant (p =

0.069). Four morphospecies were excluded because they lack genus-level identification.

20

Figure 1.6. Neighbour-joining trees (K2P), with locality information, for 9 cryptic species complexes in

this study. NJ trees are coloured blue for bivalves and red for gastropods and triangles represent

compressed clades, with sample size provided in brackets. *(Layton unpublished).

A) Mya truncata B) Cryptonatica affinis

C) Mya arenaria D) Clione limacina

E) Tachyrhynchus erosus F) Thyasira gouldi

G) Triopha catalinae

H) Macoma balthica * I) Hiatella arctica *

21

Figure 1.7. Mean nearest neighbour distance (% K2P) plotted against mean GC content (%) for the 33

orders of Mollusca represented in this study.

22

Figure 1.8. Secondary structure of COI marked with insertions and deletions for A) gastropods and B)

bivalves. Insertions are marked with a blue + sign and deletions are marked with a red x.

B)

A)

23

Chapter 2

Geographic Patterns of mtCOI Diversity in Two Species of Canadian Marine Bivalves

Abstract

Variation in modes of larval development has strong impacts on dispersal potential and gene flow

among populations of marine invertebrates. However, Pleistocene glaciations have also played an

important role in shaping population structure in benthic taxa in the northern hemisphere, even those with

planktotrophic larvae. This study examines patterns of COI sequence divergence in two bivalve species,

Hiatella arctica and Macoma balthica, which share a similar mode of larval development

(planktotrophic) and a vast distribution in the Nearctic. This study reveals that both species possess high

genetic diversity in the northeast Pacific, but H. arctica has less phylogeographic structure and more

sequence variation across its range than M. balthica. Three North American lineages of M. balthica were

detected, corroborating a recent taxonomic revision. This study also provides the first evidence that H.

arctica may include four species. Ecological differences between these species have likely played a role

in their differing biogeographical patterns.

24

Introduction

Life history attributes, vicariance events and environmental tolerances all play a role in

determining where species occur (Reid 1990, Hewitt 2000). In fact, contemporary patterns of population

structure in the northern hemisphere can only be understood by considering dispersal capacity and past

glacial events and how these factors impacted population isolation and differentiation (Wares &

Cunningham 2001). Patterns of population structure in Canadian marine benthic invertebrates are

particularly interesting (Meehan 1985) because many species rely on planktonic larvae for long-range

dispersal (Meehan 1985, Lee & Boulding 2009). Some larvae spend weeks in the plankton where passive

dispersal by oceanic currents can cause enough gene flow to ensure near panmixis across the species

range (Jablonski 1986, Bradbury et al. 2008, Keever et al. 2009, Lee & Boulding 2009). Although

planktotrophic species generally demonstrate less regional genetic divergence than congeneric species

lacking a planktonic stage (Jablonski 1986, Bradbury et al. 2008, Keever et al. 2009, Lee & Boulding

2009), substantial population structure can still occur in some species with planktonic larvae. For

instance, Macoma balthica shows clear genetic divergence between populations in the northwest and

northeast Atlantic (Väinölä 2003, Nikula et al. 2007). Cases such as this indicate that spatial homogeneity

does not inevitably accompany planktonic larval stages.

Glacial periods had a dramatic impact on Canadian marine environments. During the last

(Wisconsin) glacial maxima, global sea levels declined by up to 170 metres and the Arctic Ocean was

covered by persistent ice. The Atlantic coast of Canada was heavily impacted by the Laurentide ice sheet,

but the Cordilleran ice sheet on the west coast produced lesser impacts (Bernatchez & Wilson 1998,

Rohling et al. 1998, Hewitt 2000, Mandryk et al. 2001, Marko 2004). The recurrent opening and closing

of the Bering Strait linked to glacial cycles acted as a secondary factor in causing intermittent isolation

and exchange of species between the Pacific and Atlantic Oceans (Vermeij 1991, Taylor & Dodson 1994,

Dodson et al. 2007). While range expansions following the last deglaciation are responsible for current

distributions, prior episodes of glacial activity undoubtedly shaped both levels and patterns of genetic

variation (Bernatchez & Wilson 1998). During each glacial advance, species retreated to refugia and then

expanded their range during the subsequent interglacial (Vermeij et al. 1990, Hewitt 2000). For example,

studies of mitochondrial DNA diversity in two marine species with planktonic larvae - the fish, Mallotus

villosus, and the sea urchin, Strongylocentrotus droebachiensis, demonstrated their persistence in glacial

refugia in the northwest Atlantic and northeast Pacific (Addison & Hart 2005, Dodson et al. 2007).

Because each glacial advance tended to fragment species distributions in a consistent way, populations

were effectively separated for prolonged periods, setting the stage for their differentiation (Hewitt 1996,

Maggs et al. 2008, Dapporto 2009). As species expanded their range during interglacials, divergent

lineages re-established contact at sites along Canada’s coasts (Harper & Hart 2007). Despite their shared

25

exposure to these environmental changes, species with similar life history attributes, but differing

ecological preferences, have undoubtedly responded differently to glacial cycles (Bernatchez & Wilson

1998). Thus, glacial cycles have significantly impacted contemporary genetic structure and these patterns

vary with dispersal potential and ecological tolerance.

Past studies have provided insights into how glacial cycles have shaped contemporary patterns of

genetic variation in some marine taxa, but little work has been directed toward molluscs. The scope for

investigation is vast with regards to marine molluscs as nearly 1000 species occur across Canada’s three

oceans. However, of all the marine molluscs occurring in Canada, fewer than 25 have distributions

spanning all three oceans. Species in this subset are ideal candidates for investigations of population

structure and this study targets two wide-ranging bivalves, Hiatella arctica and Macoma balthica. No

prior study has examined the genetic structure of H. arctica, a species with a Holarctic distribution

(Alison & Marincovich 1982, Schneider & Kaim 2012) and a long planktonic larval phase that lasts

several weeks (Lebour 1938). Adults of Hiatella display considerable phenotypic plasticity, its shell

growth being impacted differentially depending on the substrate or microhabitat in which the bivalve

grows, making identifications in this genus particularly difficult (Alison & Marincovich 1982). The

second species examined in this study, M. balthica, also has a Holarctic distribution (Gofas 2012) and a

long-lived planktonic larval stage that extends for 1-2 months, allowing for extensive offspring dispersal

along coasts. This species has seen prior genetic analysis which revealed considerable differentiation

across its range. In fact, Väinölä (2003) concluded that M. balthica includes two subspecies - M. balthica

balthica from the northeast Pacific and Baltic Sea and M. balthica rubra from Europe.

This study had the primary goal of comparing the extent and patterns of genetic structure in

Canadian populations of H. arctica and M. balthica, although European populations of M. balthica were

also considered. If component populations of these two species were repeatedly isolated into separate

glacial refugia, then this should be reflected in their current population structure and possibly by the

occurrence of two or more lineages at sites of secondary contact. Alternatively, if certain populations

were extirpated during glaciations, then patterns of reduced genetic diversity should reflect localized

extinction and subsequent postglacial recolonization.

Methods

Specimen collection

From 4-60 specimens of M. balthica and H. arctica were collected at various sites in the Pacific

Ocean (British Columbia, Alaska), the Arctic Ocean (Manitoba) and the Atlantic Ocean (New Brunswick,

Nova Scotia, Prince Edward Island, Newfoundland) between 2007 and 2011 (Figure 2.1). Specimens

were obtained from rock crevices, algal mats, holdfasts and soft bottoms in the intertidal and from

subtidal habitats using otter trawls, dredges and SCUBA diving. Specimens were transferred into 95%

26

ethanol immediately after collection to ensure tissue preservation. Morphological identification of all

specimens was confirmed by Dr. André L. Martel at the Canadian Museum of Nature, while subspecies

designations for M. balthica follow Väinölä (2003).

DNA extraction, amplification and sequencing

Doubly uniparental inheritance (DUI), characterized by the transmission of a maternal and

paternal mitochondrial lineage through eggs and sperm, can cause deep divergences between male and

female conspecifics (Passamonti & Ghiselli 2009, Ghiselli et al. 2012). Despite the presence of this

genetic phenomenon in some mollusc groups, somatic tissue is dominated by the female genome and thus

sampling only this tissue type will avoid problems posed by DUI (Zouros 2012). In turn, DNA extracts

were prepared from a small sample of adductor muscle tissue from each specimen. Tissue samples were

placed in cetyltrimethylammonium bromide (CTAB) lysis buffer solution with proteinase K. The samples

were then incubated for 12 hours at 56°C before a manual glass fibre plate method was used for DNA

extraction (Ivanova et al. 2008). Following incubation, the DNA was eluted with 40 µl of ddH2O. After

re-suspension, 2 µl of each DNA extract was placed into a well in a separate plate with 18 µl ddH2O to

ensure the dilution of salts or mucopolysaccharides that might inhibit PCR. Species-specific primer sets

were used: HiaF1/HiaR1: AAGTTGTAATCATCGAGATATTGG and

TAGACTTCTGGGTGCCCGAAAAACCA for H. arctica and MMacF1/LepR1:

CTTTTATTAGCTGCACCTGATAT and TAAACTTCTGGATGTCCAAAAAATCA for M. balthica.

Each well was filled with 2 µl of diluted DNA and the following reagents to generate a 12.5 µl PCR

reaction mix: 6.25 µl 10% trehalose, 2 µl ddH20, 1.25 µl 10× PCR buffer, 0.625 µl MgCl2 (50 mM),

0.125 µl of each forward and reverse primer (10 µM), 0.0625 µl dNTP (10 mM) and 0.06 µl Platinum

Taq polymerase. The thermocycling regime consisted of one cycle of 1 min at 94°C, 40 cycles of 40 s at

94°C, 40 s at 52°C, and 1 min at 72°C, and finally 5 min at 72°C. An E-GelH 96 (Invitrogen) was used to

check 3 µL of each PCR product and reactions that generated an amplicon were bidirectionally sequenced

using BigDye v3.1 on an ABI 3730xl DNA Analyzer (Applied Biosystems). Sequences were edited

manually using CodonCode (CodonCode Corporation) and were aligned by eye in MEGA5 (Tamura et al.

2011). The COI gene was amplified from 172 and 196 specimens of H. arctica and M. balthica,

respectively.

Data analysis

Neighbour-joining (NJ) trees were constructed in MEGA5 using the Kimura-2-parameter (K2P)

distance model and 1000 bootstrap replicates (Kimura 1980, Saitou & Nei 1987, Tamura et al. 2011). The

H. arctica and M. balthica NJ trees were rooted with Mya arenaria and Macoma inquinata as outgroups,

respectively. Each sequence cluster showing more than 2% divergence from other clusters was labelled.

Clustering patterns in each NJ tree were compared with those in the corresponding median-joining

27

haplotype network. These networks are based on maximum parsimony and were constructed in Network

4.6.1 (fluxus-engineering.com, Bandelt et al. 1999). Haplotype networks were then recreated in TCS 1.21

to identify ancestral haplotypes (Clement et al. 2000). Divergence times were estimated in MEGA5

assuming a substitution rate of 2% per million years (Hellberg & Vacquier 1999, Marko 2002, Donald et

al. 2005, Tamura et al. 2011).

Arlequin 3.5 (Excoffier & Lischer 2010) was employed to examine patterns of genetic structure

in each species. The haplotype and nucleotide diversity for each population was calculated, and Tajima’s

D test of neutrality with 10,000 simulated samples was used to infer the nature of selection pressures (Nei

1987, Tajima 1989). The number of haplotypes in each species was determined and the proportion of

unique haplotypes was quantified to ascertain which site possessed the highest genetic exclusivity. An

analysis of molecular variance (AMOVA) was conducted with a K2P distance model and significance

was tested with 1000 permutations (Kimura 1980). The AMOVA results were used to determine whether

the majority of genetic variation in each species existed within or between populations as a measure of

population differentiation (Excoffier & Lischer 2010). Fixation indices (FST) were estimated with a K2P

distance model and significance was tested with 100 permutations to determine the partitioning of

variance (Weir & Cockerham 1984). An FST value of 1 indicates that two populations share no haplotypes

while a value of 0 indicates all haplotypes are shared and have similar frequencies (Weir & Cockerham

1984). Slatkin’s linearized FST (Slatkin 1993) was subsequently plotted against geographic distance to

determine whether genetic variation among populations reflected long-term historical divergence or

geographic distance (Slatkin 1993, Kyle & Boulding 2000, Marko 2004, Keever et al. 2009). In order to

test the significance of isolation by distance, a Mantel test with 1000 permutations was conducted in

Arlequin 3.5 (p-values < 0.05 were treated as significant).

Results

Sequence recovery and haplotype diversity

The 196 COI sequences from M. balthica ranged in length from 377 - 655 bp (mean = 429 bp),

while all 172 sequences from H. arctica were 572 bp. All sequences contained less than 1% ambiguous

bases and none possessed stop codons or double peaks. The variable length of the M. balthica sequences

reflected the use of internal primers to recover sequences from some specimens with degraded DNA. For

the analysis of population structure, sequences from H. arctica were trimmed to 572 bp, while sequences

of the Pacific/Arctic M. balthica balthica and the northwest Atlantic (ATL) M. balthica were trimmed to

430 bp and 378 bp, respectively. The highest intraspecific divergences (23%) were observed in H.

arctica, while intraspecific divergences in the M. balthica complex peaked at 12.5% (Figure 2.2). NJ trees

indicated the presence of four lineages in both species, but one of the lineages of M. balthica was only

present in Europe (Figure 2.3 & 2.4).

28

Most sequences (155 of 172) and haplotypes (63 of 75) of H. arctica fell into a dominant cluster

which was collected at all sites, although the Alaska population had the greatest number of unique

haplotypes (27). All 63 members of this clade showed low divergence - just 1 to 3 mutational steps

(Figure 2.3; Figure 2.5A). The remaining 12 haplotypes included representatives of three probable cryptic

species, two in the Pacific (Cook Inlet, Alaska and Barkley Sound, British Columbia) and a third in New

Brunswick (Figure 2.3; Figure 2.5A). TCS suggested that a northwest Atlantic haplotype was ancestral to

the dominant cluster with a projected migration route into Hudson Bay (Churchill, MB) and then the

northeast Pacific (Alaska, British Columbia)(Figure 2.5A). Subsequent analysis of intraspecific variation

in H. arctica only examined specimens belonging to the dominant clade.

M. balthica also showed high intraspecific divergences, but this variation showed a clear

geographic pattern with up to 12.5% divergence between M. balthica balthica and ATL M. balthica

populations (Figure 2.4). In total, 42 haplotypes of M. balthica were observed with the greatest number

(14) in Europe. This high diversity likely reflects the analysis of larger sample sizes in Europe although

only one representative of each European haplotype was submitted to GenBank (Luttikhuizen et al. 2003,

Nikula et al. 2007, Becquet et al. 2012). These sequences were excluded from analysis in Arlequin

because they would have distorted estimates of haplotype diversity. Interestingly, the northeast Pacific

population grouped with both the Churchill and Baltic Sea populations, while the other European

sequences formed a distinct cluster (M. balthica rubra) that appeared more similar to M. balthica balthica

than to ATL M. balthica (Figure 2.5B). Populations from the northwest Atlantic only included members

of the ATL M. balthica cluster, except those from Newfoundland which included some representatives of

the M. balthica balthica cluster (Figure 2.5B). In fact, the median-joining network suggested that an

ancestral haplotype for the M. balthica balthica cluster was present in Newfoundland (Figure 2.5B).

Because the divergence between clusters 1 and 2 of M. balthica was only slightly greater than 3%, they

were considered as a single taxon for analysis in Arlequin (Figure 2.5B). Regional divergence was

obvious in the ATL M. balthica and M. balthica rubra lineages while M. balthica balthica was more

broadly distributed, occurring in the northeast Pacific, Arctic and Baltic Sea (Figure 2.4; Figure 2.5B).

This divergence is corroborated by the high number of mutational steps that separate each cluster of M.

balthica (Figure 2.5B).

The sequence divergences of the three cryptic groups (2, 3, 4) of H. arctica suggest that they

diverged from a common ancestor 3, 4.5 and 5.5 million years ago, respectively (Figure 2.3). The four

clusters of M. balthica all date to more than 1 million years ago, excluding the split between M. balthica

balthica and the other lineage in Newfoundland which dates at around 900K years (Figure 2.4).

Patterns of genetic diversity

29

Table 2.1 reports, for H. arctica and M. balthica, the number of unique haplotypes, nucleotide

diversity, haplotype diversity and Tajima’s D index for each population. Populations at Churchill,

Manitoba showed the lowest variation for both M. balthica and H. arctica. Populations of H. arctica from

New Brunswick and British Columbia possessed the highest values for nucleotide and gene diversity

respectively, while Newfoundland had the greatest gene diversity for both M. balthica clades. The high

genetic diversity for Newfoundland may be an artefact of the small sample size (N=5, N=2) for both M.

balthica lineages. Despite their exposure to intensive glaciations, populations of both H. arctica and M.

balthica from New Brunswick were very diverse. Nucleotide and haplotype diversity did not significantly

differ between species. Lastly, Tajima’s D values were negative for all populations except those from

Newfoundland, suggesting the presence of many low frequency haplotypes at these sites. Again, the

slightly positive Tajima’s D values for both Newfoundland populations may simply be a result of

undersampling at this location.

Population structure

There was evidence for population subdivision in M. balthica, but not in H. arctica where most of

the genetic variation resided within populations (Table 2.2A & B). This result for H. arctica was

unaffected by grouping populations from each coast. By contrast, most genetic variation in M. balthica

balthica existed among the coasts with deep divergence between populations in the Atlantic and Pacific

(Table 2.3A & B). The opposite was true for ATL M. balthica as most of its variation existed within

populations (Table 2.4). Fixation indices provided further insight into the partitioning of variation in H.

arctica and M. balthica. In H. arctica, populations from Nova Scotia and New Brunswick showed little

divergence (FST = 0.03), while those from British Columbia and New Brunswick were considerably more

divergent (FST = 0.28) (Table 2.5A). Populations of M. balthica balthica from Alaska and British

Columbia were the most similar (FST = 0.02), while those from Churchill and Newfoundland showed high

divergence (FST = 0.93) (Table 2.5B), a surprising result given the proximity of Hudson Bay and the

Atlantic coast. The ATL lineage of M. balthica also showed unique genetic structure with a high FST (FST

= 0.23) between the Newfoundland and Prince Edward Island populations, although they were only

separated by the narrow Northumberland and Cabot Straits (Table 2.5C). When Slatkin’s linearized FST

values were plotted against geographic distance, only H. arctica demonstrated evidence of isolation by

distance (Figure 2.6) with a strong, positive correlation (R2 = 0.83) and a Mantel test confirmed its

significance (p= 0.011). By contrast, M. balthica balthica demonstrated no evidence for isolation by

distance (Figures 2.7), while ATL M. balthica (Figure 2.8) demonstrated a negative relationship (R2 =

0.78), but Mantel tests indicated that neither value was significant. Only three populations were used to

examine evidence of isolation by distance in the ATL M. balthica, suggesting future work should aim to

gather additional data.

30

Discussion

Comparing diversity and structure in two bivalves with planktotrophic larval development

While a widespread lineage was present in both bivalve species, the M. balthica complex also

showed evidence of regional divergence. Divergence between species with shared life history strategies

has been demonstrated in other molluscs including the direct developing gastropods Nucella lamellosa

and Nucella ostrina (Marko 2004). H. arctica showed less regional variation across Canada than M.

balthica, although one lineage of the latter species occurs in both Canadian waters and in the Baltic Sea.

The presence of widespread lineages in H. arctica and M. balthica suggests both species have high gene

flow among their populations (Keever et al. 2009, Lee & Boulding 2009). The spatial homogeneity

demonstrated by the main H. arctica lineage may be the result of adults burrowing in kelp holdfasts and

attaching to ship hulls, providing a secondary dispersal mechanism (Helmuth et al. 1994). H. arctica and

M. balthica differ radically in some life history strategies, including habitat and feeding behaviours

(Newell 1965, Ali 1970, Hines & Comtois 1985). M. balthica lives in soft sediment and occupies

shallower habitats and thus may have been more susceptible to local extinctions during glacial maxima,

similar to the intertidal dogwhelk Nucella lapillus (Dorjes et al. 1986, Colson & Hughes 2004. Patterns of

population fragmentation during glacial periods are often also reflected in measures of genetic diversity.

For instance, reduced genetic diversity has been demonstrated in populations of marine fishes severely

impacted by glaciation (Bernatchez & Wilson 1998) as well as in Mya arenaria, the softshell clam

(Strasser & Barber 2009). However, some taxa which have undergone repeated extinctions during

glaciations possess high diversity (Bernatchez & Wilson 1998, Strasser & Barber 2009). For example,

high genetic diversity in the dogwhelk, Nucella lapillus, has been linked to rapid expansion following a

severe population bottleneck (Colson & Hughes 2004). A similar process may explain the high diversity

in populations of both H. arctica and M. balthica from New Brunswick despite their likely exposure to

severe glacial conditions (Briggs 1970, Wares & Cunningham 2001). By contrast, the low genetic

diversity of populations of both species at Churchill may reflect both the fact that Hudson Bay only

formed 8000 years ago (Ashworth 1996) and its isolation from glacial refugia. Conversely, Alaskan

populations of both species were diverse perhaps reflecting their foundation through the admixture of

lineages from two or more refugia (Kelly et al. 2006, Sakaguchi et al. 2011).

Implications for glacial refugia in the northeast Pacific

The admixture of previously isolated lineages is thought to explain high levels of genetic

diversity in certain populations of the longnose dace (Rhinichthys cataractae) and those of the bluestriped

snapper (Lutjanus kasmira) (Girard & Angers 2006, Gaither et al. 2010). Because genetic diversity can be

elevated in both zones of admixture and at sites that served as glacial refugia, it is difficult to determine

which process explains high diversity in any particular situation (Petit et al. 2003, Kelly et al. 2006,

31

Sakaguchi et al. 2011). Populations of both bivalves examined in this study were diverse in the northeast

Pacific, a pattern also seen in the direct developing gastropod N. lamellosa and the brooding sea

cucumber Cucumaria pseudocurata (Arndt & Smith 2002, Marko 2004). Although the northeast Pacific

coast was glaciated, the extent of glaciation along the Pacific was much less severe than in the northwest

Atlantic (Mandryk et al. 2001, Marko 2004). As well, there may have been several glacial refugia in the

North Pacific despite literature suggesting that populations were displaced to the south (Warner et

al.1982, Hewitt 2000, Mandryk et al. 2001, Marko 2004). Populations in proximity to refugia often

possess many unique haplotypes, while haplotypes in admixture zones combine variants found in two or

more refugia (Provan & Bennett 2008). The presence of many unique haplotypes in Alaskan population

of Nucella lamellosa was invoked as evidence for a northern refugium (Marko 2004). H. arctica and M.

balthica reinforce this pattern as 78% and 92% of the haplotypes in their Alaskan population were unique.

While most of their haplotypes are unique to this area, the presence of some shared haplotypes suggests

possible admixture. The potential for postglacial admixture in Alaska has important implications given

that founder populations may be more fit than parental lineages in these environments, ultimately

accelerating rates of evolution (Wares et al. 2005, Kelly et al. 2006).

Evidence of sibling species

Populations isolated into separate glacial refugia inevitably diverge, leading in time to speciation

(Dodson et al. 2007, Maggs et al. 2008, Dapporto 2009). M. balthica shows evidence of incipient

speciation as indicated by the recognition of two subspecies (Väinölä 2003) with largely allopatric ranges.

The detection of both M. balthica balthica and ATL M. balthica lineages in Newfoundland suggests that

this area is a zone of secondary contact between Pacific and Arctic haplotypes. It also suggests that this

region may be an ideal location for more detailed genetic analysis to determine if these lineages show

incipient or complete reproductive isolation. It is possible that populations near Newfoundland

experienced localized extinction and recolonization, a process reported for some taxa in the northwest

Atlantic (Briggs 1970, Wares & Cunningham 2001). In any case, the presence of more than 8% sequence

divergence between members of these two clusters suggests they may warrant recognition as sibling

species. H. arctica also shows evidence for sibling species with the discovery of four genetically

divergent lineages across Canada, despite the limited evidence for regional differentiation in the most

abundant of these groups. Additional work is required to ascertain if the divergence at COI is

accompanied by divergence at nuclear loci. These findings suggest that several glacial refugia occurred

on the Pacific and Atlantic coasts of Canada and that these refugia fostered speciation. Coyer et al. (2011)

discovered two separate glacial refugia for macroalgae (Fucus distichus) in the Newfoundland area alone.

Moreover, Nikula et al. (2007) suggest that multiple refugia existed for M. balthica, likely contributing to

the deep divergences observed in this complex. In all, the genetic structure of species with planktonic

32

larvae has not only been influenced by dispersal potential but has also been significantly shaped by

Canada’s extensive glacial history (Bernatchez & Wilson 1998, Dodson et al. 2007).

Conclusions

This study constitutes the first investigation of population structure in Hiatella arctica and further

contributes to our understanding of population structure in Macoma balthica. The present study has

shown that while some lineages of M. balthica and H. arctica demonstrate spatial homogeneity across

Canada, there is also evidence of genetic subdivision, suggesting that population structure varies in

species with similar life history strategies. Because the taxonomic status of the three lineages in the M.

balthica complex is uncertain, future work should incorporate additional genes and studies of

reproductive compatibility to determine their status. Little phylogeographic structure was present in the

main lineage of H. arctica, but the detection of three other deeply divergent lineages suggests the

presence of overlooked sibling species. Whether there are morphological differences, at the larval or early

juvenile stages, among these divergent H. arctica lineages remains to be determined and certainly

warrants further taxonomic research. In addition, this study has revealed that the northeast Pacific is a

zone of high diversity, suggesting it served as a glacial refugium or that it is a zone of secondary contact.

Future work should compare intertidal and subtidal populations to rule out the possibility of depth related

differentiation as well as include a survey of key environments, such as the Chukchi and Labrador Seas,

which remain unsampled. Lastly, future work should aim to calibrate evolutionary rates in each genus as

well as gain an understanding of genetic diversity in populations in Asian waters.

33

Table 2.1. Genetic diversity in populations of the bivalve species, H. arctica and M. balthica, as measured

by number of haplotypes, haplotype diversity (h), nucleotide diversity (π) and Tajima’s D.

Population N Haplotypes Unique H h π Tajima’s D

NS 60 24 17 0.88 0.0071 -1.35

AK 43 23 18 0.94 0.0075 -1.40

MB 19 7 3 0.71 0.0051 -0.82

NB 25 18 13 0.93 0.0078 -1.73

BC 8 7 5 0.96 0.0060 -0.06

Population N Haplotypes Unique H h π Tajima’s D

AK 38 12 11 0.69 0.0045 -1.48

MB 19 4 3 0.66 0.0019 -0.20

BC 4 3 2 0.83 0.0035 -0.75

NFLD1 5 4 4 0.90 0.0056 0

Population N Haplotypes Unique H h π Tajima’s D

PEI 33 9 6 0.74 0.0053 -1.04

NB 51 12 9 0.83 0.0090 0.01

NFLD2 2 2 0 1 0.016 0

C) ATL M. balthica

B) M. balthica balthica

A) H. arctica

34

Table 2.2. Overall genetic structure measured by AMOVA (analysis of molecular variance) for H. arctica

both A) grouped by coastal populations and B) with no grouping. P-values < 0.05 were treated as

significant.

Variation df Sum of Squares Variance

Components

% of Variation P-value

Among Coasts 2 36.9 0.30 12.7 0.06

Among Populations w/in

Coasts

2 8.4 0.09 3.8 0.03

Within Populations 150 301.0 2.0 83.6 0.00

Total 154 346.3 2.40

Table 2.3. Overall genetic structure measured by AMOVA for M. balthica balthica A) grouped by coastal

populations and B) with no grouping. P-values < 0.05 were treated as significant.

Variation df Sum of Squares Variance

Components

% of Variation P-value

Among Populations 4 45.3 0.33 14.1 0.00

Within Populations 150 301.0 2.0 85.9

Total 154 346.3 2.33

Variation df Sum of Squares Variance

Components

% of Variation P-value

Among Coasts 2 72.0 2.1 70.9 0.34

Among Populations w/in

Coasts

1 1.1 0.04 1.4 0.19

Within Populations 62 50.3 0.81 27.8 0.00

Total 65 123.4 2.95

Variation df Sum of Squares Variance

Components

% of Variation P-value

Among Populations 3 73.1 1.9 69.6 0.00

Within Populations 62 50.3 0.81 30.4

Total 65 123.4 2.71

A)

B)

A)

B)

35

Table 2.4. Overall genetic structure measured by AMOVA for ATL M. balthica with no grouping. P-

values < 0.05 were treated as significant.

Variation df Sum of Squares Variance

Components

% of Variation P-value

Among Populations 2 7.0 0.10 6.2 0.07

Within Populations 83 120.3 1.4 93.9

Total 85 127.3

36

Table 2.5. FST for populations of A) H. arctica, B) M. balthica balthica and C) ATL M. balthica. P-values

< 0.05 are marked with an asterisk.

Nova Scotia New

Brunswick

Alaska B.C. Churchill

Nova Scotia 0

New Brunswick 0.03* 0

Alaska 0.16* 0.22* 0

British Columbia 0.25* 0.28* 0.08 0

Churchill 0.05* 0.06* 0.15* 0.25* 0

Alaska B.C. Churchill Newfoundland

Alaska 0

British Columbia 0.02 0

Churchill 0.09* 0.14* 0

Newfoundland 0.88* 0.88* 0.93* 0

P.E.I. New Brunswick Newfoundland

Prince Edward Island (P.E.I.) 0

New Brunswick 0.07* 0

Newfoundland 0.23 -0.19 0

A)

B)

C)

37

Figure 2.1. Collection sites for H. arctica and M. balthica. The sample size for each species at a site is

shown in the pie.

38

Figure 2.2. Intraspecific sequence divergence (K2P) for A) H. arctica and B) M. balthica.

B)

A)

39

Figure 2.3. Neighbour-joining tree based on K2P distances for H. arctica. The top scale shows estimated

divergence times in millions of years, while the bottom scale bar shows % sequence divergence (K2P).

Bootstrap probabilities are shown on the NJ tree and red triangles represent compressed clades, with

sample size provided in brackets.

2

3

4

1

40

Figure 2.4. Neighbour-joining tree based on K2P distances for M. balthica. Subspecies names suggested by Väinölä (2003) are provided. The top

scale shows estimated divergence times in millions of years, while the bottom scale bar shows sequence divergence (% K2P). Bootstrap

probabilities are shown on the NJ tree and blue triangles represent compressed clades, with sample size provided in brackets.

M. balthica complex

1: M. balthica balthica

2

3: M. balthica rubra

4

41

Figure 2.5. Median-joining haplotype networks for A) H. arctica and B) M. balthica constructed in

Network 4.6.1 with maximum parsimony. All mutational steps are equal to 1 unless shown by numeral.

Presumptive ancestral haplotypes are marked with a white star. The size of circles in each network varies

with the number of sequences belonging to each haplotype. M. balthica rubra sequences are included.

1

3

4

2

A)

3

1

2

4 B)

42

Figure 2.6. FST values (Slatkin’s linearized) plotted against geographic distance (km) for populations

within the main lineage of H. arctica to examine the extent of isolation by distance. Results from a

Mantel test for significance are provided on the plot.

Figure 2.7. FST values (Slatkin’s linearized) plotted against geographic distance (km) for populations of

M. balthica balthica to examine the extent of isolation by distance. Results from a Mantel test for

significance are provided on the plot.

43

Figure 2.8. FST values (Slatkin’s linearized) plotted against geographic distance (km) for populations of

ATL M. balthica to examine the extent of isolation by distance. Results from a Mantel test for

significance are provided on the plot.

44

General Conclusions

Summary of findings

My thesis has explored patterns of sequence variation in the COI gene in Canadian marine

molluscs both at the phylum and species level. The results of this work suggest that intraspecific variation

at COI may be higher in molluscs than in other marine phyla, and that patterns of sequence divergence

vary among species with similar life history strategies. My results expand our knowledge of species

diversity and population structure in molluscs and provide crucial insight for conservation strategies,

particularly for translocations. Chapter 1 makes an important contribution toward the construction of a

comprehensive barcode library for Canadian marine molluscs, generating records for nearly 25% of the

known malacological fauna of Canada. This work provides novel insight into how sequence divergence

varies among molluscs, and also reveals a correlation between nearest neighbour distance and GC

content. While my work has begun to fill the gap in barcode coverage for Canadian molluscs, many

species still lack data. My investigations in Chapter 1 revealed deep intraspecific divergences in 9 species,

motivating the two case studies in Chapter 2. In this chapter, I show that population structure can differ

greatly in species with similar larval development and dispersal potential, suggesting that vicariance

events have provoked variable outcomes in genetic divergence patterns. Certainly prior glaciations have

fragmented the populations of both species, provoking genetic subdivision and the formation of species

complexes. Given that patterns of population fragmentation vary between species, future research needs

to examine how habitat preferences and larval resilience have impacted the contemporary distribution of

species.

The application of a DNA barcode library for Canadian marine molluscs

DNA barcoding has proven effective in delineating species boundaries across many animal

groups, but my work is the first attempt to examine its performance in a large number of Canadian marine

molluscs. My investigations revealed 9 cases of deep intraspecific divergence (>2%) that likely represent

cryptic species, potentially representing new Canadian records. Webb et al. (2012) similarly discovered

that apparent cases of high intraspecific divergence in North American Ephemeroptera were largely due

to overlooked species complexes that showed large divergence between their haplotype clusters. Such

overlooked taxa inflate mean intraspecific divergences and highlight the importance of integrating

molecular and morphological approaches to advance our understanding of species boundaries. In light of

this, it is imperative that future barcode studies include detailed taxonomic work to resolve

misidentifications, cases of synonymy, and overlooked species that otherwise generate a misleading sense

of sequence divergence.

Although my study surveyed many species, future work should aim to fill gaps in species

coverage, especially by sampling deep sea habitats where many species await discovery (Archambault et

45

al. 2010). It would also be valuable to compare patterns of genetic variation in marine molluscs to those in

their freshwater and terrestrial counterparts to gain insight into how patterns of sequence differentiation

vary across these habitats. For instance, the majority of marine species in the tropical Pacific were found

to be more widespread and to show less differentiation than terrestrial species (Paulay and Meyer 2002).

Future work should extend this analysis by comparing patterns of divergence in molluscs from the Arctic,

temperate zone and tropics to better understand how species diversity, as well as rates of speciation and

diversification, varies among these environments. This research would help to provide the data needed to

verify that lower extinction rates are responsible for the greater species richness evident in tropical

settings (Schemske 2012).

Gene flow in marine populations

While gene flow is undoubtedly greater in species with planktonic larvae, recent studies have

indicated that panmixis is not inevitable. In fact, spatial heterogeneity was noted in a planktotrophic

barnacle (Semibalanus balanoides), while the direct developing gastropod (Nucella ostrina) showed

spatial homogeneity (Holm & Bourget 1994, Marko 2004). These results suggest that while dispersal

potential is important, other factors also impact population structure. While my study assessed population

structure in two species with planktotrophic larval development, only a few studies have compared

patterns between species with different modes of larval development. Lee and Boulding (2009) found that

population structure in littorinid snails from the northeast Pacific differed both within and between larval

types. Given this complexity, future work should aim for a broad analysis of population structure in

planktotrophic, lecithotrophic and direct developing species. Furthermore, while planktonic larvae

facilitate gene flow among populations, gene flow may vary if larvae differ in their tolerance to

environmental conditions. As well, some species show delayed metamorphosis when exposed to low

temperatures, so gene flow can be impacted by environmental temperatures (Pechenik 1980). In light of

such complexities, a solid understanding of the physiological tolerances of planktonic larvae and of the

duration of the larval stage would bring new insight into population differentiation.

If dispersal potential were the only factor determining population structure then differing

phylogeographic patterns would always be apparent between species with planktotrophic larval

development and those with direct development. The fact that they are not suggests that vicariance events

play a key role. Multiple glacial cycles have impacted Canada’s coasts and while this study utilizes

coalescent methods to examine the impact of glaciation on population structure, fossil data would provide

crucial insight into both historical distributions and the location of glacial refugia. Lineages showing

marked sequence divergence were discovered in about 7% of the species examined in this study. My

work emphasizes the value of coalescent approaches for population genetics because of the difficulty of

discriminating cryptic species through the fossil record alone.

46

Conclusions and implications for conservation

Protecting marine biodiversity is a challenging and difficult task, but one which is critical given

the rate at which i) marine ecosystems are being impacted by human activities and ii) species are

becoming extinct. My work has begun the construction of a DNA barcode library for Canadian marine

molluscs that not only provides taxonomic assignments, but also locality information. This study is a

particularly important contribution to the DNA barcoding literature because it highlights the efficacy of

COI for teasing apart patterns of genetic variation on both a broad and local scale. In addition, the

documentation of genetic variation in this phylum lends itself to comparative studies with other marine

phyla. Such research is crucial to better understand speciation in marine environments and to broaden

knowledge of the factors promoting diversification in marine phyla. Discovering cryptic diversity in the

marine realm, particularly in species with a Holarctic distribution, has implications for conservation

efforts that target both species diversity and genetic diversity. For instance, identifying distinct genetic

clusters in species thought to have broad distributions can impact translocation efforts which aim to

replenish locally extirpated populations. These findings can also significantly impact bivalve mariculture.

In all, this thesis broadens our understanding of genetic variation in Canadian marine molluscs, aiding

future conservation efforts.

47

Literature Cited

Addison, J.A. & Hart, M.W. (2005). Colonization, dispersal, and hybridization influence phylogeography

of North Atlantic sea urchins (Strongylocentrotus droebachiensis). Evolution, 59, 532-543.

Albu, M., Min, X.J., Hickey, D. & Golding, B. (2008). Uncorrected nucleotide bias in mtDNA can mimic

the effects of positive Darwinian selection. Molecular Biology and Evolution, 25, 2521-2524.

Ali, R.M. (1970). The influence of suspension density and temperature on the filtration rate of Hiatella

arctica. Marine Biology, 6, 291-302.

Alison, R.C. & Marincovich, L.J. (1982). A late Oligocene or earliest Miocene molluscan fauna from

Sitkinak Island, Alaska. In: Jurassic (Oxfordian and late Callovian) Ammonites from the Western

Interior Region of the United States (ed Imlay, R.W.). Geological Survey professional paper,

Washington, 1232, 115.

An, H.S. & Lee, J.W. (2012). Development of microsatellite markers for the Korean mussel, Mytilus

coruscus (Mytilidae) using next-generation sequencing. Journal of Molecular Science, 13, 10583-

10593.

Archambault, P., Snelgrove, P.V.R., Fisher, J.A.D., Gagnon, J.M., Garbary, D.J., et al. (2010). From sea

to sea: Canada’s three oceans of biodiversity. PLoS ONE, 5, e12182.

Arndt, A. & Smith, M.J. (2002). Genetic diversity and population structure in two species of sea

cucumber: differing patterns according to mode of development. Molecular Ecology, 7, 1053-

1064.

Ashworth, A. (1996). The response of arctic Carabidae (Coleoptera) to climate change based on the fossil

record of the Quaternary. Annales Zoologici Fennici, 33, 125-131.

Bandelt, H-J., Forster, P. & Röhl, A. (1999). Median-joining networks for inferring intraspecific

phylogenies. Molecular Biology and Evolution, 16, 37-48.

Bax, N., Williamson, A., Aguero, M., Gonzalez, E. & Geeves, W. (2003). Marine invasive alien species:

a threat to global biodiversity. Marine Policy, 27, 313-323.

Becquet, V., Simon-Bouhet, B., Pante, E., Hummel, H. & Garcia, P. (2012). Glacial refugium versus

range limit: Conservation genetics of Macoma balthica, a key species in the Bay of Biscay

(France). Journal of Experimental Marine Biology and Ecology, 432-433, 73-82.

Bernatchez, L. & Wilson, C.C. (1998). Comparative phylogeography of Nearctic and Palearctic fishes.

Molecular Ecology, 7, 431-452.

Bouchet, P., Lozouet, P., Maestrati, P. & Heros, V. (2002). Assessing the magnitude of species richness

in tropical marine environments: exceptionally high numbers of molluscs at a New Caledonia

site. Biological Journal of the Linnean Society, 75, 421-436.

Bouchet, P. (2006). The magnitude of marine biodiversity. In: The exploration of marine biodiversity:

scientific and technological challenges (ed. Duarte, C.M.). Fundacion BBVA, Bilbao, Spain, 31-

64.

48

Bradbury, I.R., Laurel, B., Snelgrove, P.V.R., Bentzen, P. & Campana, S.E. (2008). Global patterns in

marine dispersal estimates: the influence of geography, taxonomic category and life history.

Proceedings of the Royal Society of London: Biological Series, 275, 1803–1809.

Briggs, J.C. (1970). A faunal history of the North Atlantic Ocean. Systematic Zoology, 19, 19–34.

Bucklin, A., Steinke, D. & Blanco-Bercial, L. (2011). DNA barcoding of marine metazoa. Annual

Review of Marine Science, 3, 471-508.

Campillo, S., Serra, M., Carmona, M.J. & Gomez, A. (2011). Widespread secondary contact and new

glacial refugia in the halophilic rotifer Brachionus plicatilis in the Iberian Peninsula. PLoS ONE,

6, e20986.

Canestrelli, D., Sacco, F. & Nascetti, G. (2011). On glacial refugia, genetic diversity, and

microevolutionary processes: deep phylogeographical structure in the endemic newt Lissotriton

italicus. Biological Journal of the Linnaean Society, 105, 42-55.

Carlton, J.T. (1999). The scale and ecological consequences of biological invasions in the world’s oceans.

In: Invasive species and biodiversity management (eds Sandlund, O.T., Schei, P.J. & Viken, A.).

Kluwer Academic Publishers, Dordrecht, 195-212.

Carr, C., Hardy, S.M., Brown, T.M., Macdonald, T.A. & Hebert, P.D.N. (2010). A tri-oceanic

perspective: DNA barcoding reveals geographic structure and cryptic diversity in Canadian

polychaetes. PLoS ONE, 6, e22232.

Clare, E.L., Kerr, K.C.R., von Königslöw, T.E., Wilson, J.J. & Hebert, P.D.N. (2008). Diagnosing

mitochondrial DNA diversity: applications of a sentinel gene approach. Journal of Molecular

Evolution, 66, 362:367.

Clement, M., Posada, D. & Crandall, K. (2000). TCS: a computer program to estimate gene genealogies.

Molecular Ecology, 9, 1657-1660.

Colgan, D.J., Ponder, W.F. & Eggler, P.E. (1999). Gastropod evolutionary rates and phylogenetic

relationships assessed using partial 28S rDNA and histone H3 sequences. Zoologica Scripta, 29,

29-63.

Colson, I. & Hughes, R.N. (2004). Rapid recovery of genetic diversity of dogwhelk (Nucella lapillus L.)

populations after local extinction and recolonization contradicts predictions from life-history

characteristics. Molecular Ecology, 13, 2223-2233.

Costa, F.O., deWaard, J.R., Boutillier, J., Ratnasingham, S., Dooh, R.T., Hajibabaei, M. & Hebert, P.D.N.

(2007). Biological identifications through DNA barcodes: the case of the Crustacea. Canadian

Journal of Fisheries and Aquatic Sciences, 64, 272-295.

Coyer, J.A., Hoarau, G., Van Schaik, J., Luijckx, P. & Olsen, J.L. (2011). Trans-Pacific and trans-Arctic

pathways of the intertidal macroalga Fucus distichus L. reveal multiple glacial refugia and

colonizations from the North Pacific to the North Atlantic. Journal of Biogeography, 38, 756-

771.

Dapporto, L. (2009). Speciation in Mediterranean refugia and post-glacial expansion of Zerynthia

polyxena (Lepidoptera, Papilionidae). Journal of Zoological Systematics and Evolutionary

Research, 48, 229-237.

49

Dixon, D.R., Simpson-White, R. & Dixon, L.R.J. (1992). Evidence of thermal stability of ribosomal DNA

sequences in hydrothermal vent organisms. Journal of the Marine Biological Association of the

United Kingdom, 72, 519-527.

Dixon, P. (2003). VEGAN, a package of R functions for community ecology. Journal of Vegetation

Science, 14, 927–930.

Dodson, J.J., Tremblay, S., Colombani, F., Carscadden, J.E. & Lecomte, F. (2007). Trans-Arctic

dispersals and the evolution of a circumpolar marine fish species complex, the capelin (Mallotus

villosus). Molecular Ecology, 16, 5030-5043.

Donald, K.M., Kennedy, M. & Spencer, H.G. (2005). Cladogenesis as the result of long-distance rafting

events in South Pacific topshells (Gastropoda, Trochidae). Evolution, 59, 1701-1711.

Dorjes, J., Michaelis, H. & Rhode, B. (1986). Long-term studies of macrozoobenthos in intertidal and

shallow subtidal habitats near the island of Norderney (East Frisian Coast, Germany).

Hydrobiologia, 142, 217-232.

Drent, J., Luttikhuizen, P.C. & Piersma, T. (2004). Morphological dynamics in the foraging apparatus of

a deposit feeding marine bivalve: phenotypic plasticity and heritable effects. Functional Ecology,

18, 349-356.

Excoffier, L. & Lischer, H.E.L. (2010). Arlequin suite ver 3.5: a new series of programs to perform

population genetics analyses under Linux and Windows. Molecular Ecology Resources, 10, 564-

567.

Gaither, M.R., Bowen, B.W., Toonen, R.J., Planes, S., Messmer, V., Earle, J. & Robertson, D.R. (2010).

Genetic consequences of introducing allopatric lineages of bluestriped snapper (Lutjanus

kasmira) to Hawaii. Molecular Ecology, 19, 1107-1121.

Ghiselli, F., Milani, L., Chang, P.L., Hedgecock, D., Davis, J.P., Nuzhdin, S.V. & Passamonti, M. (2012).

De Novo assembly of the Manila clam Ruditapes philippinarum transcriptome provides new

insights into expression bias, mitochondrial doubly uniparental inheritance and sex determination.

Molecular Biology and Evolution, 29, 771-786.

Girard, P. & Angers, B. (2006). The impact of postglacial marine invasions on the genetic diversity of an

obligate freshwater fish, the longnose dace (Rhinichthys cataractae), on the Quebec peninsula.

Canadian Journal of Fisheries and Aquatic Sciences, 63, 1429-1438.

Gofas, S. (2012). Macoma balthica (Linnaeus, 1758). Accessed through: World Register of Marine

Species at http:// marinespecies.org/aphia.php?p=taxdetails&id=141579.

Grande, C., Templado, J., Cervera, J.L. & Zardoya, R. (2004). Molecular phylogeny of Euthyneura

(Mollusca: Gastropoda). Molecular Biology and Evolution, 21, 303-313.

Hao, X., Jiang, R. & Chen, T. (2011). Clustering 16S rRNA for OTU prediction: a method of

unsupervised Bayesian clustering. Bioinformatics, 27, 611-618.

Harrell, F.E. & Miscellaneous (2012). Hmisc: Harrell miscellaneous. R package version 3.9-3.

50

Harper, F.M. & Hart, M.W. (2007). Morphological and phylogenetic evidence for hybridization and

introgression in a sea star secondary contact zone. Invertebrate Biology, 126, 373-384.

Hebert, P.D.N., Cywinska, A., Ball, S.L. & deWaard, J.R. (2003). Biological identifications through

DNA barcodes. Proceedings of the Royal Society of London Series B: Biological Sciences, 270,

313-321.

Hebert, P.D.N., Penton, E.H., Burns, J.M., Janzen, D.H., Hallwachs, W. (2004). Ten species in one: DNA

barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator.

Proceedings of the National Academy of Sciences of the United States of America, 101,

14812-14817.

Hedgecock, D., Li, G., Hubert, S., Bucklin, K. & Ribes, V. (2004). Widespread null alleles and poor

cross-species amplification of microsatellite DNA loci cloned from the Pacific oyster

(Crassostrea gigas). Journal of Shellfish Research, 23, 379-385.

Hellberg, M.E. & Vacquier, V.D. (1999). Rapid evolution of fertilization selectivity and lysin cDNA

sequences in teguline gastropods. Molecular Biology and Evolution, 16, 839-848.

Helmuth, B., Veit, R.R. & Holberton, R. (1994). Long distance dispersal of subantarctic brooding bivalve

(Gaimardia trapesina) by kelp rafting. Marine Biology, 120, 421-426.

Hewitt, G.M. (1996). Some genetic consequences of ice ages, and their role in divergence and speciation.

Biological Journal of the Linnaean Society, 58, 247–276.

Hewitt, G. (2000). The genetic legacy of the Quaternary ice ages. Nature, 405, 907-913.

Hines, A.H. & Comtois, K.L. (1985). Vertical distribution of infauna in sediments of a subestuary of

central Chesapeake Bay. Estuaries, 8, 296-304.

Holm, E.R. & Bourget, E. (1994). Selection and population genetic structure of the barnacle Semibalanus

balanoides in the northwest Atlantic and Gulf of St. Lawrence. Marine Ecological Progress

Series, 113, 247-256.

Hunt, B., Strugnell, J., Bednarsek, N., Linse, K., Nelson, R.J., Pakhomov, E., Seibel, B., Steinke, D. &

Würzberg, L. (2010). Poles apart: The “bipolar” pteropod species Limacina helicina is genetically

distinct between the Arctic and Antarctic Oceans. PLoS ONE, 5, e9835.

Ingolfsson, A. (1992). The origin of the rocky shore fauna of Iceland and the Canadian Maritimes.

Journal of Biogeography, 19, 705-712.

Ivanova, N.V., Fazekas, A.J. & Hebert, P.D.N. (2008). Semi-automated, membrane-based protocol for

DNA isolation from plants. Plant Molecular Biology Reporter, 26, 186-198.

Jablonski, D. (1986). Larval ecology and macroevolution in marine invertebrates. Bulletin of Marine

Science, 39, 565-587.

Järnegren, J., Schander, C., Sneli, J.A., Rønningen, V. & Young, C. (2007). Four genes, morphology and

ecology: distinguishing a new species of Acesta (Mollusca; Bivalvia) from the Gulf of Mexico.

Marine Biology, 152, 43-55.

51

Jennings, R.M., Bucklin, A., Ossenbrügger, H. & Hopcroft, R.R. (2010). Species diversity of planktonic

gastropods (Pteropoda and Heteropoda) from six ocean regions based on DNA barcode analysis. Deep-Sea Research Part II: Topical Studies in Oceanography, 57, 2199-2210.

Johnson, S.B., Waren, A. & Vrijenhoek, R.C. (2008). DNA barcoding of Lepetodrilus limpets reveals

cryptic species. Journal of Shellfish Research, 27, 43-51.

Jones, M., Ghoorad, A. & Blaxter, M. (2011). jMOTU and Taxonerator: turning DNA barcode sequences

into annotated operational taxonomic units. PLoS ONE, 6, e19359.

Keever, C.C., Sunday, J., Puritz, J.B., Addison, J.A., Toonen, R.J., Grosberg, R.K. & Hart, M.W. (2009).

Discordant distribution of populations and genetic variation in a sea star with high dispersal

potential. Evolution, 63, 3214-3227.

Kelly, D.W., Muirhead, J.R., Heath, D.D. & MacIsaac, H.J. (2006). Contrasting patterns in genetic

diversity following multiple invasions of fresh and brackish waters. Molecular Ecology, 15,

3641-3653.

Kembel, S.W., Cowan, P.D., Helmus, M.R., Cornwell, W.K., Morlon, H., Ackerly, D.D., Blomberg, S.P.

& Webb, C.O. (2010). Picante: R tools for integrating phylogenies and ecology. Bioinformatics,

26, 1463-1464.

Kerr, K.C.R., Lijtmaer, D.A., Barreira, A.S., Hebert, P.D.N. & Tubaro, P.L. (2009). Probing evolutionary

patterns in neotropical birds through DNA barcodes. PLoS ONE, 4, e4379.

Kimura, M. (1980). A simple method for estimating evolutionary rates of base substitutions through

comparative studies of nucleotide sequences. Journal of Molecular Evolution, 16, 111-120.

Knowlton, N. (2000). Molecular genetic analyses of species boundaries in the sea. Hydrobiologia, 420,

73–90.

Kyle, C.J. & Boulding, E.G. (2000). Comparative population genetic structure of marine gastropods

(Littorina spp.) with and without pelagic larval dispersal. Marine Biology, 137, 835-845.

Lebour, M.V. (1938). Notes on the breeding of some lamellibranchs from Plymouth and their larvae.

Journal of the Marine Biological Association of the United Kingdom, 23, 119-144.

Lee, H.J. & Boulding, E.G. (2009). Spatial and temporal population structure of four northeastern Pacific

littorinid gastropods: the effect of mode of larval development on variation at one mitochondrial

and two nuclear DNA markers. Molecular Ecology, 18, 2165-2184.

Luttikhuizen, P.C., Drent, J. & Baker, A.J. (2003). Disjunct distribution of highly diverged mitochondrial

lineage clade and population subdivision in a marine bivalve with pelagic larval dispersal.

Molecular Ecology, 12, 2215–2229.

Maggs, C.A., Castilho, R., Foltz, D., Henzler, C., Jolly, M.T., Kelly, J., Olsen, J., Perez, K.E., Stam, W.,

Väinolä, R., Viard, F. & Wares, J. (2008). Evaluating signatures of glacial refugia for North

Atlantic benthic marine taxa. Ecology, 89, S108-S122.

52

Mandryk, C.A.S., Josenhans, H., Fedje, D.W. & Mathewes, R.W. (2001). Late Quaternary

paleoenvironments of Northwestern North America: implications for inland versus coastal

migration routes. Quaternary Science Review, 20, 301-314.

Marko, P.B. (2002). Fossil calibration of molecular clocks and the divergence times of geminate species

pairs separated by the Isthmus of Panama. Molecular Biology and Evolution, 19, 2005-2021.

Marko, P.B. (2004). ‘What’s larvae got to do with it?’ Disparate patterns of post-glacial population

structure in two benthic marine gastropods with identical dispersal potential. Molecular Ecology,

13, 597-611.

Marko, P.B. & Moran, A.L. (2009). Out of sight, out of mind: high cryptic diversity obscures the

identities and histories of geminate species in the marine bivalve subgenus Acar. Journal of

Biogeography, 36, 1861-1880.

Meehan, B.W. (1985). Genetic comparison of Macoma balthica (Bivalvia, Telinidae) from the eastern

and western North Atlantic Ocean. Marine Ecological Progress Series, 22, 69-76.

Meyer, C. (2003). Molecular systematics of cowries (Gastropoda: Cypraeidae) and diversification

patterns in the tropics. Biological Journal of the Linnean Society, 79, 401-459.

Meyer, C.P. & Paulay, G. (2005). DNA barcoding: error rates based on comprehensive sampling. PLoS

Biology, 3, e422.

Mikkelsen, N.T., Schander, C. & Willassen, E. (2007). Local scale DNA barcoding of bivalves

(Mollusca): a case study. Zoologica Scripta, 36, 455-463.

Naughton, K.M. & O’Hara, T.D. (2009). A new brooding species of the biscuit star Tosia

(Echinodermata: Asteroidea: Goniasteridae), distinguished by molecular, morphological and

larval characters. Invertebrate Systematics, 23, 348-366.

Nei, M. (1987). Molecular Evolutionary Genetics. Columbia University Press, New York.

Newell, R.C. (1965). The role of detritus in the nutrition of two marine deposit feeders, the prosobranch

Hydrobia ulvae and the bivalve Macoma balthica. Proceedings of the Royal Zoological Society of

London, 144, 25- 45.

Nikula, R., Strelkov, P. & Väinölä, R. (2007). Diversity and trans-arctic invasion history of mitochondrial

lineages in the North Atlantic Macoma balthica complex (Bivalvia: Tellinidae). Evolution, 61,

928-941.

Passamonti, M. & Ghiselli, F. (2009). Doubly uniparental inheritance: two mitochondrial genomes, one

precious model for organelle DNA inheritance and evolution. DNA and Cell Biology, 28, 79-89.

Paulay, G. & Meyer, C.M. (2002). Diversification in the tropical Pacific: comparisons between marine

and terrestrial systems and the importance of founder speciation. Integrative and Comparative

Biology, 42, 922-934.

Pechenik, J.A. (1980). Growth and energy balance during the larval lives of three prosobranch

gastropods. Journal of Experimental Marine Biology and Ecology, 44, 1-28.

53

Peterson, G.H. (1999). Five recent Mya species, including three new species and their fossil connections.

Polar Biology, 22, 322-328.

Petit, R.J., Aguinagalde, I., de Beaulieu, J-L., Bittkau, C., Brewer, S., et al. (2003). Glacial refugia:

hotspots but not melting pots of genetic diversity. Science, 300, 1563-1565.

Plazzi, F., Ceregato, A., Taviani, M. & Passamonti, M. (2011). A molecular phylogeny of bivalve

mollusks: ancient radiations and divergences as revealed by mitochondrial genes. PLoS ONE, 6,

e27147.

Puillandre, N., Lambert, A., Brouillet, S. & Achaz, G. (2011). ABGD, Automated Barcode Gap

Discovery for primary species delineation. Molecular Ecology, 21, 1864-1877.

R Development Core Team (2008). R: A language and environment for statistical computing. R

Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL

http://www.R-project.org.

Radulovici, A.E., Archambault, P. & Dufresne, F. (2010). DNA barcodes for marine biodiversity: moving

fast forward? Diversity, 2, 450-472.

Ramel, C. (1998). Biodiversity and intraspecific genetic variation. Pure and Applied Chemistry, 70, 2079-

2084.

Ratnasingham, S. & Hebert, P.D.N. (2007). BOLD: The Barcode of Life Data System

(www.barcodinglife.org). Molecular Ecology Notes, 7, 355-364.

Reid, D.G. (1990). Trans-Arctic migration and speciation induced by climate change: The biogeography

of Littorina (Mollusca: Gastropoda). Bulletin of Marine Science, 47, 35-49.

Remigio, E.A. & Hebert, P.D.N. (2003). Testing the utility of partial COI sequences for phylogenetic

estimates of gastropod relationships. Molecular Phylogenetics and Evolution, 29, 641-647.

Rohling, R.J., Fenton, M., Jorissen, F.J., Bertrand, P., Ganssen, G., Caulet, J.P. (1998). Magnitudes of

sea-level lowstands of the past 500,000 years. Nature, 394, 162–165.

Saitou, N. & Nei, M. (1987). The neighbor-joining method: a new method for reconstructing phylogenetic

trees. Molecular Biology and Evolution, 4, 406-425.

Sakaguchi, S., Takeuchi, Y., Yamasaki, M., Sakurai, S. & Isagi, Y. (2011). Lineage admixture during

postglacial range expansion is responsible for the increased gene diversity of Kalopanax

septemlobus in a recently colonised territory. Heredity, 107, 338-348.

Schemske, D.W. (2009). Biotic interactions and speciation in the tropics. In: Speciation and patterns of

diversity (eds Butlin, R.K., Bridle, J.R. & Schulter, D.). Cambridge University Press, British

Ecological Society, 219-239.

Schneider, S. & Kaim, A. (2012). Early ontogeny of middle Jurassic hiatellids from a wood-fall

association: implications for phylogeny and paleoecology of Hiatellidae. Journal of Molluscan

Studies, 78, 119-127.

54

Slatkin, M. (1993). Isolation by distance in equilibrium and non-equilibrium populations. Evolution, 47,

264-279.

Snelgrove, P.V.R. (1999). Getting to the bottom of marine biodiversity: sedimentary habitats. Bioscience,

49, 129-138.

Snelgrove, P.V.R. (2010). Discoveries of Census of Marine Life: making ocean life count. Cambridge

University Press.

Steinke, D., Zemlak, T.S., Boutillier, J.A. & Hebert, P.D.N. (2009a). DNA barcoding Pacific Canada’s

fishes. Marine Biology, 156, 2641-2647.

Sun, Y., Li, Q., Kong, L. & Zheng, X. (2011). DNA barcoding of Caenogastropoda along coast of China

based on the COI gene. Molecular Ecology Resources, 12, 209-218.

Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Genetics, 123, 585-595.

Tamura, K., Peterson, D., Peterson, N., Stecher, G., Nei, M. & Kumar, S. (2011). MEGA5: molecular

evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum

parsimony methods. Molecular Biology and Evolution, 28, 2731-2739.

Taylor, E.B. & Dodson, J.J. (1994). A molecular analysis of relationships and biogeography within a

species complex of Holarctic fish (genus Osmerus). Molecular Ecology, 3, 235–248.

Thomaz, D., Guiller, A. & Clarke, B. (1996). Extreme divergence of mitochondrial DNA within species

of pulmonate land snails. Proceedings of the Royal Society B: Biological Sciences, 263, 363-368.

Tian, D., Wang, Q., Zhang, P., Araki, H., Yang, S., Kreitman, M., Nagylaki, T., Hudson, R., Bergelson, J.

& Chen, J.Q. (2008). Single-nucleotide mutation rate increases close to insertions/deletions in

eukaryotes. Nature, 455, 105–108.

Újvári, B., Madsen, T., Kotenko, T., Olsson, M., Shine, R. & Wittzell, H. (2002). Low genetic diversity

threatens imminent extinction for the Hungarian meadow viper (Vipera ursinii rakosiensis).

Biological Conservation, 105, 127-130.

van der Spoel, S. & Dadon, J.R. (1999). Pteropoda. In: South Atlantic Zooplankton (ed. Boltovskoy, D.)

Bachhuys Publishers, Leiden Netherlands, 649–706.

Väinölä, R. (2003). Repeated trans-Arctic invasions in littoral bivalves: molecular zoogeography of the

Macoma balthica complex. Marine Biology, 143, 935-946.

Vermeij, G. (1991). Anatomy of an invasion: the trans-Arctic interchange. Paleobiology, 17, 281–307.

Vetsigian, K. & Goldenfeld, N. (2005). Global divergence of microbial genome sequences mediated by

propagating fronts. Proceedings of the National Academy of Sciences of the United States of

America, 102, 7332-7337.

Ward, R.D., Zemlak, T.S., Innes, B.H., Last, P.R. & Hebert, P.D.N. (2005). DNA barcoding Australia’s

fish species. Philosophical Transactions of the Royal Society B: Biological Sciences, 360, 1847–

1857.

55

Ward, R.D., Holmes, B.H. & O’Hara, T.D. (2008). DNA barcoding discriminates echinoderm species.

Molecular Ecology Resources, 8, 1202-1211.

Wares, J.P. & Cunningham, C.W. (2001). Phylogeography and historical ecology of the North Atlantic

intertidal. Evolution, 55, 2455-2469.

Wares, J.P., Hughes, A.R. & Grosberg, R.K. (2005). Mechanisms that drive evolutionary change: insights

from species introductions and invasions. In: Species Invasions: Insights into Ecology, Evolution

and Biogeography (eds Sax, D.F., Stachowicz, J.J. & Gaines, S.D.). Sinauer Associates,

Sunderland, Massachusetts, 229-257.

Warner, B.G., Mathewes, R.W. & Clague, J.J. (1982). Ice-free conditions on the Queen Charlotte Islands,

British Columbia, at the height of the late Wisconsin glaciation. Science, 218, 675-677.

Webb, T. III & Bartlein, P.J. (1992). Global change during the last 3 million years: climatic controls and

biotic responses. Annual Review of Ecology, Evolution and Systematics, 23, 141–173.

Webb, J.M., Jacobus, L.M., Funk, D.H., Zhou, X., Kondratieff, B., Geraci, C.J., DeWalt, R.E., Baird,

D.J., Richard, B., Phillips, I. & Hebert, P.D.N. (2012). A DNA barcode library for North

American Ephemeroptera: progress and prospects. PLoS ONE, 7, e38063.

Weir, B.S. & Cockerham, C.C. (1984). Estimating F-statistics for the analysis of population structure.

Evolution, 38, 1358-1370.

Witt, J.D.S., Threloff, D.L. & Hebert, P.D.N. (2006). DNA barcoding reveals extraordinary cryptic

diversity in an amphipod genus: implications for desert spring conservation. Molecular Ecology,

15, 3073-3082.

Wu, H., Zhang, Z., Hu, S. & Yu, J. (2012). On the molecular mechanism of GC content variation among

eubacterial genomes. Biology Direct, 7, 1-16.

Zou, S., Li, Q., Kong, L., Yu, H. & Zheng, X. (2011). Comparing the usefulness of distance, monophyly

and character-based DNA barcoding methods in species identification: a case study of

Neogastropoda. PLoS ONE, 6, e26619.

Zou, S,. Li, Q. & Kong, L. (2012). Multigene barcoding and phylogeny of geographically widespread

muricids (Gastropoda: Neogastropoda) along the coast of China. Marine Biotechnology, 14, 21-

34.

Zouros, E. (2012). Biparental inheritance through uniparental transmission: the Doubly Uniparental

Inheritance (DUI) of Mitochondrial DNA. Evolutionary Biology, 1-31.

56

Appendices

Appendix A

Specimen Preservation

Most specimens were fixed in the field in 95% ethanol however specimens from southeast Alaska

were fixed in 100% ethanol due to available resources at the University of Fairbanks. Upon initial

fixation, the operculum of snails was carefully pulled back to prevent DNA degradation. Similarly, a

small, lateral incision was made along the shells of bivalves to ensure that interior tissues would be

properly preserved. Ethanol was replaced in the field once a day for the first 3 days and up to 3 additional

times when resources were available. Once arriving back at the lab, specimens were stored in a -20°C

freezer or a fridge, depending on available space. Ethanol was refreshed immediately upon returning to

the lab, even prior to sorting lots into individual specimens.

Five to ten specimens per species were chosen for processing and were assigned unique sample

names. These specimens were imaged with a Canon EOS 30D/50D camera and hydrated with several

drops of ethanol during imaging. All images, along with specimen information, can be found on the

Barcode of Life Database (BOLD). During sub-sampling, tissue was typically removed from the foot in

gastropods and chitons and from the adductor muscle or mantel in bivalves. After tissue was removed

from each specimen for molecular analysis, ethanol was refreshed in all specimens prior to being placed

back in cold storage. Interestingly, we saw high sequencing success in specimens from British Columbia

that were fixed in dry ice and stored in a -80°C freezer, suggesting these are the best preservation

techniques for marine molluscs.

57

Appendix B

Species Identifications

All specimens were assigned interim species names in the field through the references outlined in

Table B.1. After processing specimens, I traveled to the Canadian Museum of Nature to verify species

identifications for all barcode clusters recovered in Chapter 1. I worked alongside Dr. André Martel and

used the references outlined in Table B.1 to assign species-level identifications to each cluster.

Nudibranchs and microsnails were hydrated in 70% ethanol and identified under a microscope while

larger, shelled gastropods were identified dry. Shelled gastropods were identified by the number and

shape of radial ridges and striaea along their shell as well as by the operculum material, aperature teeth

and general whorl shape. Bivalves were often identified through shell morphology alone so tissue was

removed to view muscle scars, the pallial sinus and hinge dentition, all diagnostic features used for

identifying bivalves (Figure B.1). Chitons were hydrated in 70% ethanol under a microscope to view

girdle scales and spines, key features used for identification in this class. Species identifications are

particularly difficult in Tonicella, a diverse genus of chiton. Two commonly misidentified species, T.

marmorea and T. rubra, were distinguished through girdle scale patterns (Figure B.2). In T. rubra, girdle

scales are 2 to 3 times wider than in T. marmorea, the latter also possesses a much lighter girdle colour.

58

Table B.1. References for species identifications made in the field and at the Canadian Museum of

Nature.

Place of Identification Reference: Title Reference: Author

Field Sites National Audubon Society Regional Guide to

Atlantic and Gulf Coast: A Personal Journey

Amos, S.H. (1985)

The Marine Molluscs of Arctic Canada:

National Museums of Canada

Macpherson, E. (1971)

The Larousse Guide to Shells of the World Oliver, A.P.H. (1980)

Seashells of the Northeast Coast from Cape

Hatteras to Newfoundland

Gordon, J. & Weeks, T.E.

(1982)

Intertidal bivalves: a guide to the common

marine bivalves of Alaska

Foster, N.R. (1991)

National Audubon Society Field Guide to

North American Seashore Creatures

Meinkoth. N.A. (1981)

Shells & Shellfish of the Pacific Northwest:

A Field Guide

Harbo, R.M. (2009)

Canadian Museum of Nature Peterson Field Guides: Shells of the Atlantic

& Gulf Coasts & the West Indies

Abbott, R.T., Morris, P.A. &

Peterson, R.T. (1995)

Seashore Life of the Northern Pacific Coast:

An Illustrated Guide to Northern California,

Oregon, Washington, and British Columbia

Kozloff, E.N. (1983)

Between Pacific Tides Ricketts, E.F., Calvin, J. &

Hedgpeth, J.W. (1992)

American Seashells: The Marine Mollusca of

the Atlantic and Pacific Coasts of North

America

Abbott, R.T. (1974)

Peterson Field Guides: A Field Guide to the

Atlantic Seashore

Gosner, K.L. & Peterson,

R.T. (1999)

A New Species of the Genus Macoma

(Pelecypoda) from West American Coastal

Waters, with comments on Macoma calcarea

Dunnill, R.M. & Coan, E.V.

(1968)

Marine Bivalve Molluscs of the Canadian

Central and Eastern Arctic: Faunal

Composition & Zoogeography

Lubinksy, I. (1980)

Catalogue of the Marine Invertebrates of the

Estuary and Gulf of Saint Lawrence

Brunel, P., Bosse, L. &

Lamarche, G. (1998)

Seashells of North America: A Guide to Field

Identification

Abbott, R.T. (2001)

Bivalve Seashells of Western North America:

Marine Bivalve Molluscs from Arctic Alaska

to Baja California

Coan, E.V., Scott, P.V. &

Bernard, F.R. (2000)

59

Figure B.1. Hinge dentition in bivalves, a common character used for species identifications. A) Primitive

taxodont teeth displayed in a Nucula specimen and B) heterodont teeth present near the umbone in

Macoma balthica.

A)

B)

60

Figure B.2. View of girdle scales on A) Tonicella marmorea and B) Tonicella rubra, two cryptic species

of chiton in Canadian oceans. Scale patterns are diagnostic features for identification in this genus.

A)

B)

61

Appendix C

Chapter 1 Supplementary Material

Table C.1. List of COI primers used for molecular techniques in Chapter 1. * indicates those primers that

were tested but recovered little to no sequences.

Primer Name

(F/R)

Nucleotide Sequence (5’ to 3’) Reference

LCO1490_t1/

HCO2198_t1

TGTAAAACGACGGCCAGTGGTCAACAAATCATAAAGATATTGG/

CAGGAAACAGCTATGACTAAACTTCAGGGTGACCAAAAAATCA Floyd

dgLCO-1490/

dgHCO-2198

GGTCAACAAATCATAAAGAYATYGG/

TAAACTTCAGGGTGACCAAARAAYCA Meyer 2003

BivF4_t1/BivR1_t1 TGTAAAACGACGGCCAGTGKTCWACWAATCATAARGATATTGG/ CAGGAAACAGCTATGACTAMACCTCWGGRTGVCCRAARAACCA

Prosser

unpublished

C_LepFolF/

C_LepFolR

ATTCAACCAATCATAAAGATATTGGGGTCAACAAATCATAAAGATATTGG/

TAAACTTCTGGATGTCCAAAAAATCATAAACTTCAGGGTGACCAAAAAATCA Ivanova

LepF1/LepR1* ATTCAACCAATCATAAAGATATTGG/ TAAACTTCTGGATGTCCAAAAAATCA

Hebert et al. 2004

FishF2/FishR2* TCGACTAATCATAAAGATATCGGCAC/

ACTTCAGGGTGACCGAAGAATCAGAA Ward et al. 2005

C_GasF1_t1*/

GasR1_t1

TGTAAAACGACGGCCAGTTTTCAACAAACCATAARGATATTGGTGTAAAACG

ACGGCCAGTATTCTACAAACCACAAAGACATCGGTGTAAAACGACGGCCAGTTTTCWACWAATCATAAAGATATTGG/

CAGGAAACAGCTATGACACTTCWGGRTGHCCRAARAATCARAA

Prosser

unpublished

Table C.2. List of GenBank specimens used for analysis in Chapter 1.

Process ID Sample ID Class Species

GBML0009-06

GBML0013-06

GBML0014-06

GBML0015-06

GBML0016-06

GBML0017-06

GBML0018-06

GBML0019-06

GBML0020-06

GBML0021-06

GBML0022-06

GBML0024-06

GBML0025-06

GBML0026-06

GBML0027-06

GBML0028-06

GBML0029-06

GBML0030-06

GBML0031-06

GBML0032-06

GBML0033-06

GBML0035-06

GBML0062-06

GBML0064-06

GBML0065-06

AB084110

AF120639

AF120640

AY260813

AY260814

AY260815

AY260816

AY260817

AY260818

AY260821

AY260822

AY260824

AY260825

AY260826

AY260827

AY260828

AY260829

AY260830

AY260831

AY260832

AY260833

AY342055

DQ093531

NC_005840

NC_006162

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Scaphopoda

Episiphon yamakawai

Dentalium pilsbryi

Rhabdus rectius

Antalis antillarum

Antalis antillarum

Antalis dentalis

Antalis entalis

Antalis entalis

Antalis entalis

Dentalium pilsbryi

Antalis sp. PR-2003

Fissidentalium candidum

Graptacme eborea

Rhabdus rectius

Rhabdus rectius

Entalina tetragona

Gadila aberrans

Gadila aberrans

Polyschides carolinensis

Pulsellum salishorum

Pulsellum salishorum

Siphonodentalium lobatum

Antalis inaequicostata

Siphonodentalium lobatum

Graptacme eborea

62

63

64

Figure C.1. Neighbour-joining tree (K2P) for all barcoded specimens (1334).

65

Appendix D

Chapter 2 Supplementary Material

Table D.1. Detailed collection information for all 172 Hiatella arctica specimens in this study.

Sample ID Country Province Region Sector Site Descrip. Lat Long Depth

11BIOAK-0043

11BIOAK-0046

11BIOAK-0047

11BIOAK-0048

11BIOAK-0049

11BIOAK-0050

11BIOAK-0051

11BIOAK-0052

11BIOAK-0053

11BIOAK-0054

11BIOAK-0115

11BIOAK-0116

11BIOAK-0117

11BIOAK-0125

11BIOAK-0126

11BIOAK-0127

11BIOAK-0128

11BIOAK-0135

11BIOAK-0150

11BIOAK-0151

11BIOAK-0152

11BIOAK-0153

11BIOAK-0154

11BIOAK-0188

11BIOAK-0198

11BIOAK-0254

11BIOAK-0255

11BIOAK-0337

11BIOAK-0338

11BIOAK-0606

11BIOAK-0622

11BIOAK-0636

11BIOAK-0637

11BIOAK-0638

11BIOAK-0639

11BIOAK-0640

11BIOAK-0641

11BIOAK-0642

11BIOAK-0643

11BIOAK-0644

11BIOAK-0645

11BIOAK-0646

11BIOAK-0647

11BIOAK-0705

11BIOAK-0706

11BIOAK-0707

11BIOAK-0708

11BIOAK-0710

11BIOAK-0714

11BIOAK-0715

10BCMOL-00374

11POPHI-0007

11POPHI-0008

11POPHI-0009

11POPHI-0010

11POPHI-0011

11POPHI-0012

11POPHI-0013

11POPHI-0014

11POPHI-0015

11POPHI-0016

10PROBE-105770.1

10PROBE-105780.1

10PROBE-105790.1

10PROBE-105900.1

10PROBE-105910.1

10PROBE-105920.1

10PROBE-106050

10PROBE-106060

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

BC

BC

BC

BC

BC

BC

BC

BC

BC

BC

BC

MB

MB

MB

MB

MB

MB

MB

MB

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Bamfield

Edward King Is.

Scott's Bay

Scott's Bay

Scott's Bay

Scott's Bay

Scott's Bay

Prasiola Point

Prasiola Point

Prasiola Point

Scott's Bay

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Kasitsna Bay Lab

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Kachemak Bay

Kachemak Bay

Kachemak Bay

Outside Beach

Outside Beach

Outside Beach

Outside Beach

Kasitsna Bay Lab

Kasitsna Bay Lab

Kasitsna Bay Lab

Kasitsna Bay Lab

Kasitsna Bay Lab

Kasitsna Bay Lab

Little Tutka

Jakalof Bay

China Poot

China Poot

Little Tutka

Little Tutka

Camel Rock

Outside Beach

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Kasitsna Bay Lab

Kasitsna Bay Lab

Kasitsna Bay Lab

Kasitsna Bay Lab

Kasitsna Bay Lab

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Little Tutka

Churchill River

Churchill River

Churchill River

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

kelp trawl

kelp trawl

kelp trawl

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

dock side

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

dock side

kelp trawl

kelp trawl

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

dredge

dredge

dredge

intertidal

intertidal

intertidal

intertidal

intertidal

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.64

59.64

59.64

59.46

59.46

59.46

59.46

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.56

59.56

59.47

59.47

59.44

59.46

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

58.79

58.79

58.79

58.77

58.77

58.77

58.77

58.77

-151.55

-151.49

-151.49

-151.49

-151.49

-151.49

-151.49

-151.49

-151.49

-151.49

-151.37

-151.37

-151.37

-151.71

-151.71

-151.71

-151.71

-151.55

-151.55

-151.55

-151.55

-151.55

-151.55

-151.49

-151.54

-151.25

-151.25

-151.49

-151.49

-151.72

-151.71

-151.49

-151.49

-151.49

-151.49

-151.49

-151.49

-151.49

-151.55

-151.55

-151.55

-151.55

-151.55

-151.49

-151.49

-151.49

-151.49

-151.49

-151.49

-151.49

-94.21

-94.21

-94.21

-93.87

-93.87

-93.87

-93.87

-93.87

0

0

0

0

0

0

0

0

0

0

15

15

15

0

0

0

0

0

0

0

0

0

0

0

0

9

9

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

15

15

15

0

0

66

10PROBE-106070

10PROBE-106080

10PROBE-106090

10PROBE-106100

10PROBE-106110

10PROBE-106120

10PROBE-106130

10PROBE-106140

10PROBE-106150

10PROBE-106160

10PROBE-106170

10NBMOL-10008.1

10NBMOL-10009.1

10NBMOL-10010.1

10NBMOL-10011.1

11BFMOL-0017

11BFMOL-0018

11BFMOL-0019

11BFMOL-0020

11BFMOL-0021

11BFMOL-0022

11BFMOL-0023

11BFMOL-0024

11BFMOL-0025

11BFMOL-0026

11BFMOL-0027

11BFMOL-0040

11BFMOL-0041

11BFMOL-0042

11BFMOL-0043

11BFMOL-0044

11BFMOL-0045

11BFMOL-0068

11BFMOL-0069

11BFMOL-0070

11BFMOL-0087

11BFMOL-0132

11BFMOL-0157

11BFMOL-0158

11BFMOL-0159

11BFMOL-0160

11BFMOL-0161

11BFMOL-0297

11ECMOL-0364

11ECMOL-0365

11ECMOL-0366

11ECMOL-0367

11ECMOL-0368

11ECMOL-0369

11ECMOL-0370

11ECMOL-0371

11ECMOL-0372

11ECMOL-0373

11ECMOL-0374

11ECMOL-0375

11ECMOL-0376

11ECMOL-0377

11ECMOL-0378

11ECMOL-0379

11ECMOL-0380

11ECMOL-0381

11ECMOL-0382

11ECMOL-0383

11ECMOL-0384

11ECMOL-0385

11ECMOL-0386

11ECMOL-0387

11ECMOL-0388

11ECMOL-0389

11ECMOL-0390

11ECMOL-0391

11ECMOL-0392

11ECMOL-0393

11ECMOL-0394

11ECMOL-0395

11ECMOL-0396

11ECMOL-0397

11ECMOL-0398

11ECMOL-0399

11ECMOL-0400

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Passamaquoddy

Passamaquoddy

Passamaquoddy

Passamaquoddy

Spruce Island

Spruce Island

Spruce Island

Spruce Island

Spruce Island

Spruce Island

Spruce Island

Spruce Island

Spruce Island

Spruce Island

Spruce Island

The Wolves

The Wolves

The Wolves

The Wolves

The Wolves

The Wolves

Navy Island

Navy Island

Navy Island

Western Passage

Western Passage

Casco Bay Island

Casco Bay Island

Casco Bay Island

Casco Bay Island

Casco Bay Island

Indian Point

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

intertidal

intertidal

intertidal

intertidal

intertidal

intertidal

intertidal

intertidal

intertidal

intertidal

intertidal

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

boat dive

rocky intertidal

rocky intertidal

rocky intertidal

trawl

trawl

boat dive

boat dive

boat dive

boat dive

boat dive

rocky intertidal

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.77

45.08

45.08

45.08

45.08

44.97

44.97

44.97

44.97

44.97

44.97

44.97

44.97

44.97

44.97

44.97

44.95

44.95

44.95

44.95

44.95

44.95

45.06

45.06

45.06

44.95

44.95

44.96

44.96

44.96

44.96

44.96

45.07

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

-93.87

-93.87

-93.87

-93.87

-93.87

-93.87

-93.87

-93.87

-93.87

-93.87

-93.87

-67.08

-67.08

-67.08

-67.08

-66.91

-66.91

-66.91

-66.91

-66.91

-66.91

-66.91

-66.91

-66.91

-66.91

-66.91

-66.73

-66.73

-66.73

-66.73

-66.73

-66.73

-67.06

-67.06

-67.06

-67.02

-67.02

-66.93

-66.93

-66.93

-66.93

-66.93

-67.04

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

0

0

0

0

0

0

0

0

0

0

0

20

20

20

20

20

20

20

20

20

20

20

9

9

9

9

9

9

0

0

0

9

9

9

9

9

9

9

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

67

11ECMOL-0401

11ECMOL-0402

11ECMOL-0403

11ECMOL-0404

11ECMOL-0405

11ECMOL-0406

11ECMOL-0407

11ECMOL-0408

11ECMOL-0409

11ECMOL-0410

11ECMOL-0411

11ECMOL-0412

11ECMOL-0413

11ECMOL-0414

11ECMOL-0415

11ECMOL-0416

11ECMOL-0417

11ECMOL-0418

11ECMOL-0419

11ECMOL-0420

11ECMOL-0421

11ECMOL-0422

11ECMOL-0423

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

Bedford Basin

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

44.69

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

-63.64

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Table D.2. Detailed collection information for all 196 Macoma balthica specimens in this study.

* denotes GenBank specimens.

Sample ID Country Province Region Sector Site Descrip. Lat Long Depth

11BIOAK-0439

11BIOAK-0424

11BIOAK-0330

11BIOAK-0328

11BIOAK-0326

11BIOAK-0248

11BIOAK-0245

11BIOAK-0244

11BIOAK-0243

11BIOAK-0416

11BIOAK-0455

11BIOAK-0454

11BIOAK-0453

11BIOAK-0452

11BIOAK-0451

11BIOAK-0450

11BIOAK-0449

11BIOAK-0448

11BIOAK-0447

11BIOAK-0446

11BIOAK-0445

11BIOAK-0444

11BIOAK-0443

11BIOAK-0442

11BIOAK-0441

11BIOAK-0440

11BIOAK-0438

11BIOAK-0437

11BIOAK-0436

11BIOAK-0435

11BIOAK-0434

11BIOAK-0433

11BIOAK-0432

11BIOAK-0431

11BIOAK-0430

11BIOAK-0429

11BIOAK-0428

11BIOAK-0427

11BIOAK-0426

11BIOAK-0425

11BIOAK-0423

11BIOAK-0422

11BIOAK-0421

11BIOAK-0420

11BIOAK-0419

11BIOAK-0418

11BIOAK-0417

10BCMOL-00310

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

USA

Canada

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

AK

BC

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Cook Inlet

Haida Gwaii

Jakalof Bay

Jakalof Bay

Kasitsna Bay

Kasitsna Bay

Kasitsna Bay

China Poot

China Poot

China Poot

China Poot

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

Jakalof Bay

mud flats

mud flats

rocky intertidal

rocky intertidal

rocky intertidal

gravel mud flat

gravel mud flat

gravel mud flat

gravel mud flat

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

mud flats

59.47

59.47

59.47

59.47

59.47

59.57

59.57

59.57

59.57

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

59.47

-151.54

-151.54

-151.55

-151.55

-151.55

-151.30

-151.30

-151.30

-151.30

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

-151.54

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

68

10BCMOL-00312

10BCMOL-00314

10BCMOL-00315

10PROBE-106310

10PROBE-106300

10PROBE-106290

10PROBE-106280

10PROBE-106270

10PROBE-106260

10PROBE-106250

10PROBE-106240

10PROBE-106230

10PROBE-106220

10PROBE-106210

10PROBE-106200

10PROBE-105590.1

10PROBE-105600.1

10PROBE-105620.1

10PROBE-105710.1

10PROBE-106360

10PROBE-106340

10PROBE-106330

11BFMOL-0277

11BFMOL-0274

11BFMOL-0220

11BFMOL-0279

11BFMOL-0278

11BFMOL-0276

11BFMOL-0275

11BFMOL-0273

11BFMOL-0271

11BFMOL-0270

11BFMOL-0269

11BFMOL-0268

11BFMOL-0266

11BFMOL-0265

11BFMOL-0264

11BFMOL-0263

11BFMOL-0262

11BFMOL-0261

11BFMOL-0260

11BFMOL-0259

11BFMOL-0258

11BFMOL-0257

11BFMOL-0256

11BFMOL-0255

11BFMOL-0254

11BFMOL-0253

11BFMOL-0252

11BFMOL-0251

11BFMOL-0250

11BFMOL-0248

11BFMOL-0247

11BFMOL-0245

11BFMOL-0244

11BFMOL-0243

11BFMOL-0242

11BFMOL-0241

11BFMOL-0240

11BFMOL-0239

11BFMOL-0238

11BFMOL-0236

11BFMOL-0235

11BFMOL-0234

11BFMOL-0233

11BFMOL-0231

11BFMOL-0228

11BFMOL-0227

11BFMOL-0225

11BFMOL-0224

11BFMOL-0223

11BFMOL-0222

11BFMOL-0221

11MMMOL-00050

11MMMOL-00046

11MMMOL-00045

11MMMOL-00044

11MMMOL-00043

11MMMOL-00042

11MMMOL-00031

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

BC

BC

BC

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

MB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NB

NFLD

NFLD

NFLD

NFLD

NFLD

NFLD

NFLD

Haida Gwaii

Haida Gwaii

Haida Gwaii

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

Churchill

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

St. Andrews

Bonne Bay

Bonne Bay

Bonne Bay

Bonne Bay

Bonne Bay

Bonne Bay

Bonne Bay

Churchill River

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Bird Cove

Churchill River

Churchill River

Churchill River

Churchill River

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

Indian Point

dredge

intertidal

intertidal

intertidal

intertidal

intertidal

intertidal

intertidal

mud flats

mud flats

intertidal

intertidal

mud flats

mud flats

mud flats

dredge

dredge

dredge

dredge

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

rocky intertidal

St. Paul's

St. Paul's

DB lagoon

DB lagoon

DB lagoon

DB estuary

DB estuary

58.79

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.77

58.79

58.79

58.79

58.79

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

45.07

-94.21

-93.87

-93.87

-93.87

-93.87

-93.87

-93.87

-93.87

-93.84

-93.84

-93.87

-93.87

-93.84

-93.84

-93.84

-94.21

-94.21

-94.21

-94.21

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

-67.04

15

0

0

0

0

0

0

0

0

0

0

0

15

15

15

15

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

69

11ECMOL-0037

11ECMOL-0032

11ECMOL-0029

11ECMOL-0028

11ECMOL-0027

11ECMOL-0020

11ECMOL-0017

11ECMOL-0015

11ECMOL-0013

11ECMOL-0011

11ECMOL-0010

11ECMOL-0009

11ECMOL-0008

11ECMOL-0007

11ECMOL-0006

11ECMOL-0005

11ECMOL-0004

11ECMOL-0003

11ECMOL-0001

11ECMOL-0035

11ECMOL-0034

11ECMOL-0036

11ECMOL-0038

11ECMOL-0033

11ECMOL-0026

11ECMOL-0024

11ECMOL-0023

11ECMOL-0022

11ECMOL-0021

11ECMOL-0019

11ECMOL-0018

11ECMOL-0016

11ECMOL-0014

11ECMOL-0012

11ECMOL-0002

*HM756189.1

*HM756187.1

*HM756185.1

*HM756183.1

*HM756181.1

*HM756179.1

*HM756177.1

*HM756175.1

*HM756173.1

*HM756171.1

*HM756188.1

*HM756186.1

*HM756182.1

*HM756180.1

*HM756178.1

*HM756176.1

*HM756174.1

*HM756172.1

*HM756170.1

*AF443220.1

*AF443222.1

*AF443219.1

*AF443221.1

*AY162261.1

*AY162263.1

*AY162260.1

*AY162262.1

*EF044127.1

*EF044125.1

*EF044126.1

*AF443216.1

*AF443218.1

*AF443217.1 *EF044130.1

*EF044132.1

*EF044134.1

*EF044131.1

*EF044129.1

*EF044133.1

*EF044135.1

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

Canada

France

France

France

France

France

France

France

France

France

France

France

France

France

France

France

France

France

France

France

Europe

Europe

Europe

Europe

Europe

Europe

Europe

Europe

Arctic Ocean

Arctic Ocean

Arctic Ocean

Europe

Europe

Europe

Europe

Europe

Europe

Europe

Europe

Europe

Europe

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

PEI

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Wadden Sea

Wadden Sea

Wadden Sea

Wadden Sea

Baltic Sea

Baltic Sea

Baltic Sea

Baltic Sea

Chukchi Sea

Chukchi Sea

Chukchi Sea

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Pinette

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Bay of Biscay

Wadden Sea

Wadden Sea

Wadden Sea

Wadden Sea

Baltic Sea

Baltic Sea

Baltic Sea

Baltic Sea

Chukchi Sea

Chukchi Sea

Chukchi Sea

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

red mud

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

46.05

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

-62.91

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

70

BCD (i,j) = (

Figure D.1. Calculations for a) Tajima’s D, b) FST and c) Bray Curtis index.

A)

B)

C)

FST = between - within / between

π = mean pairwise difference

S = number of segregating sites

i,j = objects

k = index of variable

y = variable

n = total number of variables

d = distance matrix

71

Figure D.2. Bray Curtis similarity values between populations of Hiatella arctica A) including potential

cryptic species and B) excluding potential cryptic species (main lineage with shared haplotypes). Values

closer to 1 indicate that two populations are more similar.

A)

B)

72

Figure D.3. Bray Curtis values of similarity between populations of Macoma balthica based on identified

clusters/provisional species in the NJ tree. Values closer to 1 indicate that two populations are more

similar.

73

Figure D.4. Hinge dentition in each of the four cryptic lineages of Hiatella arctica. A) the main lineage

has two similar denticles on the left valve (LV) and one denticle on the right valve (RV), B) the New

Brunswick cryptic lineage has two very dissimilar denticles on the LV and one denticle on the RV C) the

first northeast Pacific cryptic lineage has one very prominent tubercle in the LV as well as an elongated

phalange, with a definite denticle in the RV and lastly D) the second northeast Pacific cryptic lineage

lacks any defined dentition. Denticles in the RV are marked with blue arrows and denticles in the LV are

marked with red arrows. An initial examination suggests that shell morphology may be different in each

genetic cluster, but future work will aim to focus on morphometrics and determine whether these traits

show phylogenetic signal.

A) Main Lineage B) New Brunswick Cryptic

C) NE Pacific Cryptic 1 D) NE Pacific Cryptic 2

LV

RV

RV

LV

LV

RV RV

LV

74

Appendix E

R Code

Chapter 1

Pearson’s Chi-Square for Sequencing Success

chi <- read.csv("chi Dec 5.csv")

chi

chisq.test(chi)

Testing for a correlation between mean GC content and sequence divergence between congenerics

order<-read.csv("GC vs NN R input Nov 28.csv", header=T, sep=",")

order

plot(order,xlab="Mean GC Content (%)",ylab="Mean Nearest Neighbour Distance (K2P)",pch=20,

col="darkgoldenrod2")

abline(lm(mean.NN ~ mean.GC, data= order),col= "red")

legend("topleft",legend=c("Correlation coefficient= 0.51","p= 0.002*"),col="black",box.col="black")

library(boot)

order1<-corr(order)

order1

# Correlations with significance levels

library(Hmisc)

rcorr(order$mean.GC, order$mean.NN, type="pearson")

Constructing rarefaction curves for Bivalvia and Gastropoda

Biv<-read.csv("BivAccumInput Nov 27.csv")

library(picante)

BivMatrix<-sample2matrix(Biv)

BivAccum<-specaccum(BivMatrix, "rarefaction")

plot(BivAccum, xlab="", ylab="", col="cyan4", xlim=c(0,800), ylim=c(0,150), ann=FALSE, ci=0,

lwd=2)

title(main="", col.main=FALSE, sub="", col.sub=FALSE, xlab="Sample", ylab="Species",

col.lab="black")

Gas<-read.csv("GasAccumInput Nov 27.csv")

library(picante)

GasMatrix<-sample2matrix(Gas)

GasAccum<-specaccum(GasMatrix, "rarefaction")

plot(GasAccum, add=T,xlab="", ylab="", col="darkgoldenrod2", xlim=c(0,800), ylim=c(0,150),

ann=FALSE, ci=0, lwd=2)

title(main="", col.main=FALSE, sub="", col.sub=FALSE, xlab="Sample", ylab="Species",

col.lab="black")

legend(5,150, c("Bivalvia","Gastropoda"), lty=c(1,1,1), lwd=c(2,2,2,2,2),

col=c("cyan4","darkgoldenrod2"), box.lwd=0, box.col="white")

Constructing rarefaction curves for Cephalopoda, Polyplacophora and Scaphopoda

Ceph<-read.csv("CephAccumInput Nov 27.csv")

library(picante)

75

CephMatrix<-sample2matrix(Ceph)

CephAccum<-specaccum(CephMatrix, "rarefaction")

plot(CephAccum, xlab="", ylab="", col="cornflowerblue", xlim=c(0,150), ylim=c(0,25), ann=FALSE,

ci=0, lwd=2)

title(main="", col.main=FALSE, sub="", col.sub=FALSE, xlab="Sample", ylab="Species",

col.lab="black")

Poly<-read.csv("PolyAccumInput Nov 27.csv")

library(picante)

PolyMatrix<-sample2matrix(Poly)

PolyAccum<-specaccum(PolyMatrix, "rarefaction")

plot(PolyAccum, add=T, xlab="", ylab="", col="mediumvioletred", xlim=c(0,150), ylim=c(0,25), ann=

FALSE, ci=0, lwd=2)

title(main="", col.main=FALSE, sub="", col.sub=FALSE, xlab="Sample", ylab="Species",

col.lab="black")

Scaph<-read.csv("ScaphAccumInput Nov 27.csv")

library(picante)

ScaphMatrix<-sample2matrix(Scaph)

ScaphAccum<-specaccum(ScaphMatrix, "rarefaction")

plot(ScaphAccum, add=T,xlab="", ylab="", col="black", xlim=c(0,150), ylim=c(0,25), ann=FALSE,

ci=0, lwd=2)

title(main="", col.main=FALSE, sub="", col.sub=FALSE, xlab="Sample", ylab="Species",

col.lab="black")

legend(5,25, c("Cephalopoda","Polyplacophora","Scaphopoda"), lty=c(1,1,1), lwd=c(2,2,2,2,2),

col=c("cornflowerblue","mediumvioletred","black"), box.lwd=0, box.col="white")

Barcode gap histogram

mean<-read.csv("mean intra R input Nov 27.csv", header=TRUE, sep=",")

mean

hist(mean$Mean.Intra, xaxt='n', col=rgb(0,0,1,0.5), xlab="Sequence Divergence (%),main="",

ylim=c(0,150), xlim=c(0,50), breaks= seq(0,50, by=2), axes = TRUE, plot = TRUE, labels = FALSE)

axis(side= 1, at = seq(0, 50, by=2))

NN<-read.csv("NN R input Nov 27.csv", header=TRUE, sep=",")

NN

hist(NN$NN, add=TRUE, col=rgb(1,0,0,0.5), breaks= seq(0,50, by=2))

legend(30, 130, c("Mean Intra-specific", "Nearest Neighbour"), cex=1.0, bty="n", c(col=rgb(0,0,1,0.5),

col=rgb(1,0,0,0.5)), box.lwd=0, box.col="white")

ANOVA for testing whether mean nearest neighbour distance is equal between genera with ≥ 2 species

genus<-read.csv("NNgenus vs species Nov 27.csv", header=T, sep=",")

genus

boxplot(genus$NN ~ genus$Species, ylab="Mean NN Distance (%)", xlab="Number of Species

Sampled")

legend("topright",legend=c("p-value = 0.069"), box.col="black")

genus.aov<-aov(genus$NN ~ genus$Species)

summary(genus.aov)

Linear regression modeling the relationship between intraspecific divergence and number of individuals

MaxIndi<-read.csv("max intra vs indi Nov 27.csv", header=T, sep=",")

76

MaxIndi

plot(MaxIndi$Max.Intra ~ MaxIndi$X..Individuals, xlim=c(0,70), ylim=c(0,35), col="mediumvioletred",

xlab="Number of Individuals", ylab="Intra-specific Divergence (%)", bty = "n", pch=20, cex=1.5)

Max1<-lm(MaxIndi$Max.Intra ~ MaxIndi$X..Individuals)

summary(Max1)

abline(Max1, col="mediumvioletred")

MeanIndi<-read.csv("mean intra vs indi Nov 27.csv", header=T, sep=",")

MeanIndi

par(new=TRUE)

plot(MeanIndi$Mean.Intra ~ MeanIndi$X..Individuals, xlim=c(0,70), ylim=c(0,35), axes=FALSE,

col="darkgoldenrod2", xlab="Number of Individuals", ylab="Intra-specific Divergence (%)", bty = "n",

pch=20, cex=1.5)

Mean1<-lm(MeanIndi$Mean.Intra ~ MeanIndi$X..Individuals)

summary(Mean1)

abline(Mean1, col="darkgoldenrod2")

legend(50,35, legend=c("Maximum", "Mean"), box.col="white", text.col=c("black"), pch=c(20,20),

col=c("mediumvioletred","darkgoldenrod2"))

text(62,4,c("R2=-0.006"),col=c("black"), cex=0.75)

text(62,1,c("R2=-0.002"),col=c("black"), cex=0.75)

Chapter 2

Paired t-test for determining whether mean haplotype and nucleotide diversity are equal between species

pop<-read.csv("diversity input POP GEN.csv", header=TRUE, sep=",")

pop

boxplot(pop$Haplotype ~ pop$Species, ylab="Haplotype Diversity", xlab="Species")

t.test(Haplotype~Species, data=pop)

nuc<-read.csv("nucleotide input POP GEN.csv", header=TRUE, sep=",")

nuc

boxplot(nuc$Nucleotide ~ nuc$Species, ylab="Nucleotide Diversity", xlab="Species")

t.test(Nucleotide~Species, data=nuc)

Histogram displaying the range of intraspecific divergence in Hiatella arctica and Macoma balthica

HiDiv <- read.csv("Hia divergences Nov 24.csv", header=TRUE, sep=",")

hist(HiDiv$Divergence, xlab="Sequence Divergence (%)", ylab="Frequency", main="", xlim=c(0,25),

ylim=c(0,13000), col="red")

MacDiv <- read.csv("Mac divergences Nov 24.csv", header=TRUE, sep=",")

hist(MacDiv$Divergence, xlab="Sequence Divergence (%)", ylab="Frequency", main="", xlim=c(0,15),

ylim=c(0,6000),col="blue")