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The Demographic History and Population Structure of Three Species of Wolffishes Across the North Atlantic Ocean

Megan R. McCusker

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Dalhousie University Halifax, Nova Scotia April 2009

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Appendices Copyright Releases (if applicable) Dedication

I dedicate this thesis to my husband, Arturo Orellana, and son, Javier Orellana. Thank you for your patience and support throughout my Ph.D.

IV Table of Contents

List of Tables xi

List of Figures xv

Abstract xviii

Acknowledgements xix

Chapter 1: Introduction 1

Chapter 2: Mitochondrial and Microsatellite Genetic Diversity is Correlated with Abundance in Fishes 3

2.1 Abstract 3

2.2 Introduction 3

2.3 Materials & Methods 6

2.3.1 Data Collection 6

2.3.2 Analysis ,..7

2.4 Results 10

2.5 Discussion 18

Chapter 3: Phylogeography of Three North Atlantic Wolffish Species (Anarhichas spp.) with Phylogenetic Relationships within the Family

Anarhichadidae ; 26

3.1 Abstract 26

3.2 Introduction 26

3.3 Materials & Methods 29

3.3.1 Phylogenetic Relationships and Speciation Timing 30

3.3.2 Intra-specific Variation 32

V 3.4 Results 34

3.4.1 Phylogenetic Relationships 34

3.4.2 Mutation Rates and Speciation Timing 36

3.4.3 Intra-specific Variation 37

3.4.4 Population Expansion and Intra-specific Divergence 38

3.5 Discussion 41

3.5.1 Origins of Family Anarhichadidae 41

3.5.2 Post-glacial Population Expansion 42

3.5.3 Mutation Rate Estimate 43

3.5.4 Glacial Refugia 45

3.5.5 Conservation Implications 46

Chapter 4: Microsatellite Markers Discriminate Three Species of North Atlantic Wolffishes (Anarhichas spp.) 57

4.1 Abstract 57

4.2 Introduction 57

4.3 Materials & Methods 59

4.3.1 Sample Collection 59

4.3.2 Genetic Analysis 59

4.3.3 Statistical Methods 62

4.4 Results 63

4.4.1 Microsatellite Characteristics 63

4.4.2 Species-Level Identification 65

4.5 Discussion 65

vi Chapter 5: Historical Influences Dominate the Population Genetic Structure of a Sedentary Marine Fish, Atlantic wolffish (Anarhichas lupus), Across the North Atlantic Ocean 69

5.1 Abstract 69

5.2 Introduction 69

5.3 Materials & Methods 72

5.3.1 Sample and Data Collection 72

5.3.2 Analytical Methods 74

53.2.1 Microsatellites 74

5.3.2.UAFLP 76

5.4 RESULTS 77

5.4.1 Microsatellite Characteristics 77

5.4.2 Population Structure and Dispersal 78

5.4.3 Effective Population Size and Historical Demography 80

5.4.4 AFLP Characteristics 82

5.5 DISCUSSION 84

5.5.1 Population Structure Across the Range of Atlantic Wolffish 84

5.5.2 The Role of Contemporary vs. Historical Factors 87

5.5.3 AFLP vs. Microsatellites 91

5.5.4 Conservation Implications 92

Chapter 6: Genetic Variation in Northern and Spotted Wolffishes (Anarhichas denticulatus and A. minor) Across the North Atlantic Ocean: Low Effective

Population Sizes in Two 'Threatened' Species 101

6.1 Abstract 101

6.2 Introduction 101

Vll 6.3 Materials & Methods 104

6.3.1 Sample and Data Collection 104

6.3.2 Analytical Methods 106

6.4 Results 109

6.4.1 Microsatellite Characteristics 109

6.4.2 AFLP Characteristics 110

6.4.3 Population Structure and Dispersal Ill

6.4.3.1 Northern Wolffish Ill

6.4.3.U Spotted Wolffish 114

6.4.4 Bottlenecks and Effective Population Size 115

6.5 Discussion 118

6.5.1 Microsatellite and AFLP Comparisons 118

6.5.2 Dispersal and Life History 121

6.5.3 Effective Population Size 123

6.5.4 Conclusion 125

Chapter 7: Conclusion 132

Reference List 135

Appendix 1. List of marine fishes and citations used 182

Appendix 2. List of freshwater and diadromous fishes and citations used 186

Appendix 3. Observed and expected heterozygosity for freshwater/diadromous (above) and marine fishes (below) relative to sample sizes. Only sample sizes less than 100 are shown 190

Appendix 4. Representative graphs of the relationships between diversity and sample size in the primary dataset (all fishes). Only sample sizes less than 100 are shown 191

Vlll Appendix 5. Graphs of the highest correlations between genetic diversity and catch. Those for mtDNA data are from Tables 5 and 5b and those for microsatellite data are from Table 4 193

Appendix 6. MtDNA primers DL-F, DL-R, ND1-F, and ND1-R were used for PCR amplification. Internal primers (labeled with "Int") were used for sequencing PCR (see text) 195

Appendix 7. Amino acid variation at the following amino acid sites in the ND1 region within the family Anarhichadidae. Blanks indicate no change in amino acid from the first one listed 195

Appendix 8. Haplotypes found across the range of Atlantic wolffish. Haplotype numbers correspond with the phylogenetic tree and minimum spanning networks (Figures 4, 7) 196

Appendix 9. Haplotypes found across the range of spotted wolffish. Haplotype numbers correspond with the phylogenetic tree and minimum spanning networks (Figures 4, 7) 197

Appendix 10. Haplotypes found across the range of northern wolffish. Haplotype numbers correspond with the phylogenetic tree and minimum spanning networks (Figures 4, 7) 197

Appendix 11. Dominant current patterns in the North Atlantic Ocean (http ://www.mar-eco.no/learning-zone/ data/page/471 /currentl _LG.jpg) 198

Appendix 12. Number of alleles and expected heteroyzogisty per sample across 14 loci used for population analysis in Atlantic wolffish 199

Appendix 13. AFLP diversity data from AFLP-Surv (Lynch and Milligan method). Polymorphic loci are defined as those with allele frequencies between 5-95%. Hj is expected heterozygosity assuming Hardy-Weinberg genotypic proportions 200

Appendix 14. Characteristics of fifteen microsatellite loci in northern and spotted wolffishes 201

Appendix 15a. Northern wolffish population data from AFLP-SURV [Lynch & Milligan method]: frequency-based (upper) and Bayesian approach (lower) 202

Appendix 15b. Spotted wolffish population data from AFLP-SURV [Lynch & Milligan method]: frequency-based (upper) and Bayesian approach (lower) 202

IX Appendix 16. Chebyshev inequality based on effective densities of 10 for northern and spotted wolffishes (ind km"1). Slopes used for this analysis were based on range-wide FST for northern wolffish and with Barents Sea samples removed for spotted wolffish 203

Appendix 17. Comparative sizes of microsatellite repeat arrays among three wolffish species for loci found to be monomorphic in either spotted or northern wolffishes 203

Appendix 18. Copyright permission for Chapter 3 204

Appendix 19. Copyright permission for Chapter 4 205

X List of Tables

Table 1. Correlation statistics between genetic diversity and catch statistics for the original dataset (top) and for those data with sample sizes >20 for mtDNA and >40 for microsatellites. Significant correlations (p<0.05) are highlighted in gray 12

Table 2. Correlation statistics between genetic diversity and body size for the original dataset (top) and for those data with sample sizes >20 for mtDNA and >40 for microsatellites. Significant correlations (p<0.05) are highlighted in gray 13

Table 3. Correlation statistics between mtDNA and microsatellite diversity for marine, FW-anadromous species, and for the combined data set. Significant correlations (p<0.05) are highlighted in gray 14

Table 4. The effect of number of microsatellite loci on correlations between abundance (catch, size) and genetic diversity. Results based on data with sample sizes of 40 or more are also presented. Significant correlations (p<0.05) are highlighted in gray 15

Table 5a. Results for three different classifications of mtDNA: CR, non-CR, and mixture. Significant correlations (p<0.05) are highlighted in gray 16

Table 5b. Results for three different classifications of mtDNA: CR, non-CR, and mixture. Data based on sample sizes less than 20 were removed. Significant correlations (p<0.05) are highlighted in gray 16

Table 6a. Family-level relationships between genetic diversity and abundance for all fishes. Family-level values were derived by averaging among all species within a family and using one species per family (see text for details). Significant correlations (p<0.05) are highlighted in gray 17

Table 6b. Family-level relationships between genetic diversity and abundance, using average values per family (left), and one species per family (right). Significant correlations (p<0.05) are highlighted in gray 17

Table 7. Sample locations and sample sizes of each species in the family Anarhichadidae 29

Table 9. Percent sequence divergence (pairwise differences) among species based on D-loop (below diagonal) andNDl (above diagonal) with standard deviation 36

XI Table 10. Divergence time estimates among species (yrs) based on D-loop (below diagonal) and ND1 (above diagonal) with standard deviation. Estimates of 3.5 mya between North Atlantic species and Bering wolffish were by definition (see text) 37

Table 12. IM estimates (above) and converted parameters (below) from IM with 95% confidence intervals. Nei and Ne2 are current effective population sizes of western and eastern Atlantic populations, respectively. NeA is the ancestral population size, and 2Nimi, 2N2m.2 are the number of effective migrants per generation into western and eastern populations, respectively 41

Table 13. General locations, NAFO/ICES regions, years collected, and sample sizes of wolffish sample collections 60

Table 14. Repeat motif, Genbank number, and primer sequences of 16 microsatellite loci for amplification in Anarhichas spp 61

Table 15. Characteristics of 16 microsatellite loci in Atlantic, spotted, and northern wolffishes (HE = expected heterozygosity; Ho = observed heterozygosity). Estimates are based on 167 Atlantic, 55 spotted, and 68 northern wolffish individuals 64

Table 16a. Sample sizes and locations for microsatellite analysis. Note that three samples have temporal replicates 73

Table 16b. Sample sizes and locations for AFLP analysis 74

Table 17. FST values (Fstat) and p-values based on contingency tests (TFPGA) for Atlantic wolffish samples, using 14 loci 79

Table 19. Effective population sizes on a regional and local scale across the range of Atlantic wolffish based on ThetaF (left) and LDNE (right). LDNe estimates were based on the random mating model with minimum allele frequencies of 0.05 and 0.01, and the jacknife approach was used for confidence intervals 82

Table 20. Ne:N ratios based on harmonic means of census size in Newfoundland and West Greenland waters 83

Xll Table 21. FST values (below diagonal) for AFLP data using band-based (upper) and allele frequence-based (middle) methods, as well as for microsatellite data (lower) for the same individuals. P-values (above diagonal) are based on contingeny tests using TFPGA. Significant results from permutation tests for AFLP data using AFLP-SURV are also presented (p<0.05 in bold). AFLP analyses were performed without the two outlier loci 84

Table 22a. Sample sizes and locations for northern and spotted wolffishes across the North Atlantic Ocean for microsatellite analysis 105

Table 22b. Sample sizes and locations for northern and spotted wolffishes across the North Atlantic Ocean for AFLP analysis 106

Table 23a. FST (below diagonal) and p-value (above diagonal, based on contingency tests from TFPGA) (upper) and Jost's D (lower) for northern wolffish samples based on 10 microsatellite loci (Bonferroni corrected p-values <0.005 for loci or samples are in gray) 112

Table 23b. FST values (below diagonal) for AFLP data using band-based and allele frequency-based approaches. P-values (above diagonal) are based on contingeny tests for allele frequencies using TFPGA. Bonferroni-corrected significant results from permutation tests for AFLP data using AFLP-SURV are presented in bold 113

Table 24. Isolation by distance statistics including mean dispersal distance (MDD) estimates for northern wolffish 113

Table 25b. FST values (below diagonal) for AFLP data using band-based and allele frequency-based approaches. P-values (above diagonal) are based on contingeny tests for allele frequencies using TFPGA. Bonferroni-corrected significant results from permutation tests for AFLP data using AFLP-SURV are presented in bold 116

Table 26. Isolation by distance statistics including mean dispersal distance (MDD) estimates for spotted wolffish 116

Table 27. Effective population sizes across sample sites across the range of northern and spotted wolffishes, based on ThetaF (left) and LDNE (right). LDNE estimates were based on the random mating model with minimum allele frequencies of 0.05 and 0.01, with 95% confidence intervals based on the jacknife approach 117

Xlll Table 28. Census size and effective population sizes of northern and spotted wolffishes in Atlantic Canada (above) and spotted wolffish in West Greenland waters (below). Ne:N ratios are based on half of the harmonic mean of census size.... 118

XIV List of Figures

Figure 1. Haplotype and nucleotide diversity for three mtDNA datasets: 'CR', 'non-CR', and 'mixture' 24

Figure 2. Within population diversity versus total diversity (per study) for freshwater and marine species. Both haplotype (upper) and nucleotide (lower) diversity show that total diversity is higher than population-level diversity in freshwater species whereas they are comparable in marine species 25

Figure 3. Sample sites for wolffishes across the North Atlantic (see Table 7 for location names) 48

Figure 4. Phylogenetic tree for D-loop-NDl composites haplotypes from the family Anarhichadidae. The tree was created using neighbour-joining base on distance analysis in PAUP with 1000 bootstrap replicates. Bootstrap values greater than 90 are indicated. Wolf-eel was designated as an ougroup 49

Figure 5. Neighbour-joining tree showing relationships among samples from all five species in the family Anarhichadidae based on AFLP. The tree was created using genetic distance analysis in Phylip with 1000 bootstrap replicates, with wolf-eel as the outgroup 50

Figure 6. Gene diversity, nucleotide diversity, and mean pairwise differences across the range of three wolffish species (Atlantic wolffish, left; spotted wolffish, middle, northern wolffish, right) 51

Figure 7. Haplotype networks for Atlantic (upper), spotted (middle), and northern (lower) wolffishes based on composite D-loop-NDl haplotypes. Circle sizes are proportional to haplotype frequency. White haplotypes were only found in the western Atlantic, black haplotypes were only found in the eastern Atlantic, and gray haplotypes were found on both sides of the Atlantic Ocean. Asterisks represent single missing haplotypes. Haplotype numbers correspond to Apps. 8-10 and the phylogenetic tree (Figure 4) 52

Figure 8. Mismatch distribution observed and expected pairwise differences under a model of sudden expansion for Atlantic (upper), spotted (middle), and northern (lower) wolffishes (see text for details) 53

Figure 9. Bayesian skyline plots of effective population size for Atlantic (upper), spotted (middle), and northern (lower) wolffishes from BEAST (Drummond & Rambaut 2007). Fewer bars are displayed for Spotted wolffish because, given the two very distinct haplotypes (see text), the tMRCA (length of x axis) was much larger than for the other two species. Plots for all three species are displayed on the same scale to compare trajectories since the most recent glaciation and estimates are based on species-specific mutation rates 54

XV Figure 10. Atlantic (upper), spotted (middle), and northern (lower) posterior probability distributions of divergence time (in 1,000 years) from IM. Probabilities are based on 10 million iterations for spotted and northern wolffishes, and one million iterations for Atlantic wolffish. Estimates are based on species-specific mutation rates (see text) 55

Figure 11. Haplotype and nucleotide diversity in mtDNA from marine fish (taken from Grant and Bowen, 1998). Wolffish data from this study were included (see text for descriptions of different categories) 56

Figure 12. Two-dimensional factorial correspondence analysis of Atlantic (filled circles), spotted (open circles), and northern (filled triangles) wolffishes based on 14 loci 68

Figure 13. Sample locations for Atlantic wolffish samples across the North Atlantic (see Table 16 for names of locations) 94

Figure 14. Expected heterozygosity (dark circles) and allelic richness (open circles) plotted against longitude for Atlantic wolffish samples across range (see Table 16, Figure 13 for more on sample locations) 94

Figure 15. MDS plot of subdivided Atlantic wolffish samples based on FST values (see Table 16 or Figure 13 for more on sample locations) 95

Figure 16. Assignment test results for 14 microsatellites across all samples. Samples taken from various locations (x-axis) across the North Atlantic (see Table 16, Figure 13 for more on sample locations). Those individuals assigned to an Atlantic Canadian location are black, those assigned to eastern Grand Banks are medium gray, those assigned to a North Atlantic location are dark gray, and those assigned to Rockall are light gray 96

Figure 17. Spatial clustering analysis in BAPS across the range of Atlantic wolffish (upper) identified two clusters, shown in red and green. Polygons represent approximate sample locations (see Table 16, Figure 13 for more on sample locations), with x and y axes approximating longitude and latitude, respectively. The size of the polygon is related more to distance from other sample groups than to the size of the sampled area. The bar graph (lower) represents the change in log marginal likelihood if samples were to be grouped with the other cluster 97

Figure 18. MDS plot of Atlantic Canada (upper) and of North Atlantic samples (lower) based on FST values. Rockall samples were not included (see Table 16, Figure 13 for more on sample locations) 98

XVI Figure 19. FST/(1- ^ST) plotted against downstream current distance for all Atlantic wolffish samples. Genetic and geographic distances within and between major groups were indicated as follows: temporal replicates as well as NE Grand Banks v SE Grand Banks (dark circles); within western Atlantic Canada (white triangles); within North Atlantic (white squares), eastern Grand Banks versus all others (except Rockall) (hatched circle); North Atlantic versus Rockall (black squares); western Atlantic Canada versus North Atlantic (white circles), eastern Grand Banks versus Rockall (white stars), western Atlantic Canada versus Rockall (cross) 99

Figure 20. Assignment probabilities for each population based on microsatellite (upper) and AFLP (lower) data using GeneClass2 and AFLPOP, respectively. Each bar represents the total number of samples collected from the x-labeled location (see Table 16, Figure 13 for more on sample locations). Individuals from each location were assigned to different populations (indicated by shades of gray) throughout the range 100

Figure 21. Sample sites for northern and spotted wolffish samples across the North Atlantic (see Tables 22a,b) 126

Figure 22. Assignment results for microsatellites (upper) and AFLP (lower) for northern wolffish. The proportion of individuals from each population assigned to various populations are represented by shades of gray 127

Figure 23. Isolation by distance among northern wolffish samples based on FST (upper) and Jost's D (lower) using 10 microsatellite loci 128

Figure 24. Assignment results for microsatellites (upper) and AFLP (lower) for spotted wolffish. The proportion of individuals from each population assigned to various populations are represented by shades of gray 129

Figure 25. Isolation by distance among spotted wolffish samples based on FST (upper) and Jost's D (lower) using nine microsatellite loci 130

Figure 26. Comparative estimates of effective population size across Atlantic (white), northern (light gray), and spotted (dark gray) wolffish based on all variable loci for the particular species, as well as identical loci in various combinations (ten, eight, and six loci; see text for details) 131

XVll Abstract

This thesis comprises five research chapters, one consisting of a review and meta analysis of published information on genetic variation in fishes, and four describing new research on genetic variation in wolffishes. The principal finding from the review chapter was that both mtDNA and microsatellite diversity in fishes were correlated with abundance, as measured by global catch statistics, consistent with the prediction based on the theory of selective neutrality. Various factors, such as sample size, number of loci, and particular mtDNA region assessed, influenced the strength of correlations in predictable ways. The main goal of the thesis was to assess genetic variation in three species of wolffishes that are of conservation concern in Canadian waters. To this end, mitochondrial DNA, microsatellite, and amplified fragment length polymorphism markers were assessed in order to determine phylogeographic patterns, historical demography, and population structure in Atlantic (Anarhichas lupus Linnaeus 1758), spotted (A. minor Olafsen 1772), and northern {A. denticulatus Krayer 1845) wolffishes. Phylogeographic divergence across the range for all three species was low and estimated divergence times were broadly consistent with post-glacial colonization of the range from a single refuge from the eastern Atlantic Ocean. However, this inference was dependent on accurate mutation rate estimates, which remain uncertain. Atlantic wolffish exhibit significant population structure across the range, as revealed by microsatellite variation, defined by the presence of four distinct groups: two within Atlantic Canada, one across the North Atlantic, and Rockall Bank. Range-wide FST values were low (<0.035), despite life history attributes such as large benthic eggs, large larvae, a limited pelagic stage, and relatively sedentary adults that suggested potential for strong population structure. Population structure in northern wolffish was characterized by significant genetic differentiation between the Barents Sea samples versus the rest of the range, and population structure in spotted wolffish was weak overall, possibly related to the low diversity of marker loci. All three species, but northern and spotted wolffishes in particular, were characterized by low microsatellite diversity indicating low long-term effective population sizes. Results suggest that northern and spotted wolffishes could be at risk of reduced fitness due to inbreeding and reduced adaptive potential.

XVlll Acknowledgements

I want to thank my supervisor Paul Bentzen, my committee members Arran McPherson and Daniel Ruzzante, and all members of the Marine Gene Probe Lab for their help throughout this project. I am also grateful to the many people involved in sample collections. This includes, but is not limited to, A. McPherson, T. Hurlbut, J. McRuer, J. Spingle, F. Collier, D. Archambault, L. Currie for Canadian samples, as well as O. J0rgensen, A. Gunnarson, D. McDonald, E. Verspoor, P. Fossum, A. Aglen, H. Knutsen, I. Fossen, and M. Aschan for samples throughout the North Atlantic. This work was supported by a Dalhousie Graduate Student Scholarship and funding by Dr. Patrick Lett to M.R.M. and by the Natural Sciences and Engineering Research Council and the Department of Fisheries and Oceans Biotechnology Strategy to Paul Bentzen. Finally, I am grateful to my parents and family members for their constant support.

xix Chapter 1: Introduction

Four species of wolffishes (Atlantic wolffish, Anarhichas lupus Linnaeus 1758, spotted wolffish, A. minor Olafsen 1772, and northern wolffish, A. denticulatus Kroyer 1845, and the Bering wolffish, A. orientalis Pallas 1814), along with the wolf-eel, Anarrhichthys ocellatus Ayres 1855, together comprise the family Anarhichadidae in the order Perciformes. Atlantic, spotted, and northern wolffishes inhabit the North Atlantic and Arctic oceans, whereas the Bering wolffish and wolf-eel inhabit the Pacific (Scott & Scott 1988). Wolffishes are thought to form monogamous mating pairs; they exhibit paternal care of young, and they have some of the largest eggs of any marine fishes (up to 8 mm in diameter for northern wolffish). Wolffishes form nests on the rocky substrate of the ocean bottom and the eggs are guarded by the male until hatch, which for Atlantic wolffish can be up to 9 months in some areas. Larvae, which hatch at approximately 20 mm in length, are not neutrally buoyant and may be able to control their own movement to a large degree. Thus, wolffish eggs and larvae are much less subject to drift by ocean currents than those of other marine fishes with pelagic eggs and larvae that float at the surface for months. As adults, wolffishes are benthic and fairly sedentary, suggesting that they may have highly distinct populations. Many of these life history characteristics that make wolffishes interesting also make them particularly vulnerable to fishing mortality and habitat degradation. Due to a combination of extensive habitat degradation and by-catch in other fisheries, wolffishes in the Atlantic Ocean have suffered severe population declines since the 1970s. All three species of wolffishes in the Atlantic are currently assessed by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) as either 'special concern' (Atlantic wolffish) or 'threatened' (spotted wolffish and northern wolffish).

My research goals were to assess the genetic variation of the three North Atlantic wolffish species to better understand their evolutionary history and adaptive potential. I was particularly interested in two broad areas of research: phylogeography and population structure, which I assessed using an array of genetic markers including mtDNA, microsatellites, and AFLP. The goal of this work was to advance our understanding of the evolutionary history of wolffishes as well as gain insights into population connectivity and dispersal across the range. Given the conservation concern

1 surrounding these fishes, results from this study should be able to inform management and conservation practices for these species. A brief outline of the thesis follows.

Chapter 2: Given that the genetic markers used in this study can only address phylogeographic and population genetics questions they are selectively neutral, I begin with a broad assessment of the selective neutrality of mtDNA and microsatellites in fishes. The selective neutrality of both markers is assessed through correlations with abundance, as the neutral theory predicts that genetic diversity should be proportional to population size.

Chapter 3: I assess the phylogenetic relationships within the family Anarhichadidae and the phylogeography of wolffishes in particular. Two regions of the mtDNA were analyzed for these purposes, ND1 and the control region. A smaller survey of AFLP variation among species was included to determine whether nuclear markers corroborated phylogenetic relationships based on mtDNA.

Chapter 4: Sixteen tetranucleotide and dinucleotide microsatellite markers were isolated from Atlantic wolffish. These microsatellites were designed in order to assess population genetic patterns in Atlantic wolffish as well as in two congeners, northern and spotted wolffishes. Microsatellite markers were assessed to determine their ability to differentiate among species in potential forensic cases.

Chapter 5: Genetic variation was analyzed in Atlantic wolffish across the North Atlantic Ocean in order to determine population structure and dispersal patterns of this species based on microsatellite loci. A smaller subset of samples was analyzed with AFLP in order to test for selection using a genome scan approach and to compare to population structure based on microsatellites.

Chapter 6: Microsatellites and AFLP analyses were undertaken in order to estimate genetic diversity, effective population size, and population structure in northern and spotted wolffishes.

Chapter 7: I present a brief concluding chapter in which I summarize the material covered in this thesis.

2 Chapter 2: Mitochondrial and Microsatellite Genetic Diversity is Correlated with Abundance in Fishes

2.1 Abstract

Genetic diversity was surveyed across 165 marine, freshwater, and diadromous fishes in order to assess whether mtDNA and microsatellite diversity was correlated with abundance. Genetic diversity from both marker types was found to be correlated with abundance, and the two marker types were correlated with one other. Microsatellites generally resulted in higher correlations with abundance than did mtDNA data, and within mtDNA analyses, number of haplotypes and haplotype diversity were usually more strongly correlated with abundance than was nucleotide diversity. Correlation analyses suggested that abundance was better approximated by catch data than by body size. Sample size, number of loci, and particular mtDNA region assessed all influenced the strength of correlations. Analyses indicated that both mtDNA and microsatellites generally conformed to neutral expectations.

2.2 Introduction

The study of genetic variation has become a central part of many conservation programs and is of particular concern as species face increasing threats from a changing environment. Genetic variation has been used as a measure of "genetic health" of a species (Frankham et al. 2002), as low diversity has been associated with inbreeding depression, the accumulation of deleterious mutations, and reduced adaptive potential (Frankham et al. 2002; Willi et al. 2006). The finding that species of conservation concern have lower genetic variation than closely related non-threatened species (Spielman et al. 2004) has raised concern about the consequences of low variation. Inherent in assessments of genetic diversity is the assumption that by focusing on a few presumably neutral loci, demographic parameters, such as population size, bottlenecks or population size fluctuations, can be characterized. However, recent studies showing evidence of selection at the molecular level have legitimately raised questions as to whether I can continue to assume marker neutrality in population genetic and

3 conservation genetic studies (Bazin et al. 2006; Grant et al. 2006; Thornton et al. 2007; Meikeljohn et al. 2007; Hahn 2008). Mitochondrial DNA (mtDNA) is one of the most widely studied genetic markers in the fields of conservation, population genetics, and phylogeography. The relatively rapid mutation rate and lack of recombination have made mtDNA particularly useful for tracking speciation, vicariance, and population size fluctuation (Avise 1994). However, the suitability of mtDNA in these contexts has been questioned for several reasons. First, it represents a single non-recombining locus, and therefore may not in itself be a reliable indicator of species relationships or demographic history (Moore 1995; Ballard & Whitlock 2004). Second, and perhaps a more serious criticism is that mtDNA does not conform to neutral expectations (Bazin et al. 2006; Grant et al. 2006). Bazin et al. (2006) surveyed genetic variation across a wide variety of taxa, comparing mtDNA nucleotide diversity, nuclear DNA diversity, and allozyme heterozygosity. Nuclear DNA and allozyme diversity were correlated with one other and generally co-varied with population size across taxa, as expected under neutral genetic theory. MtDNA nucleotide diversity, on the other hand, was nearly uniform across taxa that differed in population size by orders of magnitude (Bazin et al. 2006). They, therefore, proposed that mtDNA is strongly influenced by natural selection, and that genetic draft, a hitch-hiking process which decreases genetic diversity in larger populations (Gillespie 2001), may be the dominant force operating on mtDNA (Bazin et al. 2006).

Given the importance of such a claim to the fields of conservation genetics and phylogeography, my first goal was to re-evaluate the neutrality of mtDNA through its association with population size. Contrasting Bazin et al. (2006), I took the data from the primary literature rather than a database of sequences. The advantage of this approach was that by using the primary literature, I could estimate other measures of mtDNA diversity, such as number of haplotypes and haplotype diversity in addition to nucleotide diversity, all of which are commonly cited measures of mtDNA diversity, to determine if all parameters behaved the same way with respect to population size. Bazin et al (2006) were unable to estimate haplotype diversity due to their reliance on a database of unique sequences, and the nucleotide diversity measurement they produced was not the same as

4 that typically reported in the literature, which is based on all haplotypes in a given sample rather than only unique sequences. The second goal of this work was to examine whether or not microsatellites conform to neutral expectations. Although now a standard tool in assessments of genetic diversity and population structure, particularly in marine fishes, it is not unusual to find evidence of selection in microsatellites (Nielsen et al. 2006; Larsson et al. 2007). Furthermore, the extent to which microsatellites reflect genome-wide diversity has recently been questioned (Vali et al. 2008). The length of the microsatellite repeat array has been shown to be associated with mutation rate (and therefore diversity), presumably because the likelihood of slippage causing mutation is higher in loci with longer arrays (Ellegren 2000). As researchers are more likely to choose longer, more variable loci over shorter, less variable loci, a bias in diversity estimates may result (Vali et al. 2008). Despite the widespread use of both mtDNA and microsatellites, the interpretation of diversity in both marker types can be challenging and may depend on the number of base pairs/loci surveyed, sample sizes, as well as the choice of which mtDNA region or microsatellite loci to include. Mutation rates among mtDNA regions and microsatellite loci are known to vary, sometimes by orders of magnitude, as do mutation rates among species (Faber & Stepien 1997; Ellegren 2004; Galtier et al. 2006). Thus, researchers face two challenges with respect to these markers, the question of neutrality, and the accuracy of estimates due to slightly imprecise and possibly biased markers. I decided to focus this study on fishes for several reasons. I was particularly intrigued by the finding by Bazin et al. (2006) that marine fishes had lower nucleotide diversity than freshwater fishes, and I wanted to explore this further. Secondly, fishes represent an important natural resource, one which is facing increasing conservation risk (Roberts & Hawkins 1999; Ricciardi & Rasmussen 1999; Hutchings 2000). Genetic markers are widely used to assess population structure, historical population size changes, as well as vulnerability in fishes (Moritz 1994; Shaklee & Bentzen 1998; Avise 1998; Grant & Waples 2000; Hauser et al. 2002), and an assessment of the selective neutrality of these markers is warranted. Thirdly, given the large population sizes of many fish species, particularly marine fishes, if genetic draft is a dominant force affecting genetic variation, it may be detectable in these species. Finally, because fishes are

5 commercially exploited, catch data provide an estimate of abundance that is not available for many taxa. Although not a perfect measure of abundance, catch data are likely a better index than what is available for many non-exploited species. Specifically, I asked the following questions: 1) Is genetic variation from mtDNA and microsatellites correlated with abundance? 2) Is mtDNA genetic variation correlated with microsatellite genetic variation? 3) Do freshwater fishes have higher genetic diversity than marine fishes? 4) Which measures of diversity (if any) most accurately reflect population size?

2.3 Materials & Methods

2.3.1 Data Collection I assessed abundance in two ways for this analysis. Abundance was first estimated using global capture production data from the Food and Agriculture Organization of the United Nations (FAO) (http://www.fao.org/fishery/figis), over the period from 1950 to 2006. The fishery catch data, reported in tonnes, was converted to number offish by multiplying by 1000 and then dividing by the maximum weight (kg) for each species. I obtained maximum weight (kg) per individual for each species from Fishbase (http://fishbase.sinica.edu.tw/home.htm), either from directly reported values, or by calculation using the maximum length and the length-weight relationship provided. This assessment of abundance should be robust for several reasons. Catch data, however crude a measure, should still reflect differences in species abundance that vary by orders of magnitude. Furthermore, FAO statistics represent global values whereas survey data do not always include the entirety of species' ranges. Finally, the FAO database covers multiple years and is therefore probably more robust than any single survey I may have been able to access. I evaluated the catch data both with and without zero values, as zeros may reflect minimal fishing effort or reporting rather than low abundance. However, whether I included zeros or not made little difference to overall results, and I chose to present results based on the entire dataset (i.e. with zeros). In order to increase the number of species, I also used body size as a proxy for abundance, given the expected negative relationship between body size and abundance. Indeed, a significant (albeit weak) negative relationship between body size and genetic variation was found in mammals based on allozymes (Wooten & Smith 1985). Data on

6 maximum body size from Fishbase (as described earlier) were used for this purpose. Analyses based on body size were performed separately from those based on catch data. To collect genetic data, I surveyed the Web of Science for fish species that had both mtDNA and microsatellite data (see Apps. 1, 2 for list of species). I collected data on number of haplotypes (k), haplotype diversity (H), and nucleotide diversity (n) for mtDNA, as well as number of alleles (NA), observed and expected heterozygosity (H0,

He), and effective number of alleles (A) for microsatellite loci. The effective number of alleles (A) was derived from a simple transformation of heterozygosity (1/(1-He)), which, it has been argued, is a more appropriate estimate of diversity than He (Jost 2008). Other information such as sample size, number of microsatellite loci, mtDNA region, and the number of mtDNA base pairs examined was also collected. I collected data at the population level for both mtDNA and microsatellite diversity. Preference was given to studies that assessed mtDNA and microsatellite loci on the same populations, or at least the same geographic region, and I avoided studies on hatchery or non-native populations. Rather than attempt to match a particular population in question for which I had genetic data with a particular regional catch, I used global catch in all cases (averaged within each region from 1996-2006 and summed across regions). Although this method was imperfect because it matched population-level genetic data with species-level abundance data, I avoided the subjectivity inherent in deciding which populations (genetic data) should be compared to which regions (abundance data). My expectation was that in species with relatively high gene flow among populations, within population diversity would likely be representative of species- level diversity. However, in species with limited gene flow, this approach may not work as well. In other words, marine fishes were expected to be better suited to this approach than freshwater or anadromous species.

2.3.2 Analysis Simple correlation analyses were performed to assess relationships between diversity parameters and abundance as well as between mtDNA and microsatellite diversity. The log of abundance (for both catch and body size) was taken to account for some very large values that had the potential to strongly influence results (see Frankham, 1996). All analyses were performed using Systat v. 11 (Systat Inc, 2004).

7 I initially created three primary datasets: one for marine fishes, one for freshwater/diadromous fishes (as neither group had enough species to analyze separately), and a combined dataset. Datasets included a single data point per species (i.e. a single mtDNA-microsatellite pair). If a robust estimate from a single study (average value over a wide geographic area) was available for both markers, this was used as a single estimate. In most cases, however, averages across multiple studies for a given species were taken. Although microsatellite data were easily averaged across studies, mtDNA variation has been shown to be influenced both by the region analyzed and the number of base pairs surveyed (Ferguson & Danzmann 1998), which made taking an overall average more difficult. Therefore, I created primary datasets by using the most robust estimate of mtDNA diversity (largest number of base pairs, most samples, largest geographic area, and most information regarding methodology), rather than averaging across dissimilar studies. This way I could account for the number of base pairs analyzed in each study, as well as the mtDNA region analyzed. As sample size is known to affect various parameters, particularly the number of alleles and number of haplotypes, I assessed each variable in each dataset for associations with sample size. Visual assessments showed that relationships between diversity estimates and sample size were approximately linear in most cases, particularly if sample size outliers (n>100) were removed (App. 3). Linearity seemed to improve with transformations in certain cases, particularly for k (log), NA (log), JI (arcsin sqrt), and A (log). To remove the effect of sample size, residuals from a linear regression on sample size (n<100) were taken for number of haplotypes and number of alleles, both of which were consistently significantly associated with sample size. These residual values were then used in correlation analyses with abundance. However, for other parameters, both the original and the residual value were analyzed for correlations with abundance, and the higher correlation was reported. This was to account for a possible, but not theoretically necessary, relationship between sample size and diversity values for these parameters. Correlation analyses were performed on transformed and untransformed data (for the four parameters mentioned), and the higher correlation of the two was reported. The primary datasets were analyzed for correlations between diversity and abundance as well as diversity between mtDNA (k, H, JI) and microsatellite (NA, He)

8 diversity. After analyzing primary datasets, I addressed whether or not sample sizes in the various studies influenced overall results by removing data based on less than 40 individuals for microsatellites and less than 20 individuals for mtDNA. A sample size of at least 50 individuals has been recommended for microsatellite analysis (Ruzzante 1998), but I expected that a cut-off of 40 would allow us to assess variation across a large number of species while removing those data based on small sample sizes. Graphs of heterozygosity and sample size in freshwater and marine fishes highlighted both that microsatellite sample sizes for freshwater fishes were lower than for marine fishes and that data points based on sample sizes less than 40 appeared to be particularly variable (App. 3). The cut-off of 20 for mtDNA samples was not chosen based on pre-conceived notions of appropriate sample sizes. I simply wanted to determine if higher correlations might be found if I restricted the survey to diversity estimates based on larger sample sizes. To examine the effect of the number of loci, the primary dataset of all fishes was divided into those studies based on six or fewer loci and those based on more than six loci. Six was an arbitrary number and was chosen because approximately half the studies fell into each group. Increasing the number of loci was expected to improve precision of diversity estimates (Ruzzante 1998), and I expected higher correlations with abundance in the group with more loci. I re-analyzed the dataset with more than six loci after removing sample sizes less than 40 to see if correlation coefficients increased. In order to examine the role of mtDNA methods in determining levels of diversity, I created three groups based on mtDNA region analyzed. As the control region (CR) has been shown to have an unusually high mutation rate in many fishes (Faber & Stepien 1997), the first dataset included studies based solely on the control region ('CR'). The second dataset was based on regions other than the CR, which included mainly cytochrome b and NADH genes, but also ATPase and COl ('non-CR'). The third dataset was based on a combination of regions, including the CR ('mixture'). These datasets were compiled irrespective of the analytical methods used (SSCP, RFLP, sequences), but the third dataset happened to have the highest proportion of RFLP studies, often based on the whole genome. As with the number of loci, data were re-analyzed after removing sample sizes less than 20 to determine if the correlation coefficients substantially

9 increased. Graphs of haplotype diversity and nucleotide diversity in these mtDNA datasets suggested the presence of nucleotide diversity outliers in that certain species had very high nucleotide diversity relative to haplotype diversity. Although the reasons for the unusually high nucleotide diversity in these outliers were likely varied, I determined what the correlation would be without the outliers in order to gauge the effect of a few unusual data points. I divided nucleotide diversity by haplotype diversity across all samples in each dataset, and outliers were identified as having values greater than two standard deviations from the mean. The number of outliers typically represented about 10% of the data. The datasets were all compiled at the species level (one value per species), however, to account for possible phylogenetic constraint, and therefore pseudo- replication within families, I also analyzed the data at the family level. I did this in two ways, first by averaging across all species within each family (excluding species with incomplete data), and second, by selecting one species to represent each family based on a set of pre-defined criteria. In the 'one species per family' approach, the selected species was chosen based on sample size (large, but < 100 so residuals could be taken), number of loci, and if these were comparable, sequence data were chosen over RFLP data. Finally, I compared average diversity in marine fishes to that of FW/diadromous fishes from the primary datasets in order to determine if freshwater fishes had higher diversity than marine fishes, as was found by Bazin et al (2006).

2.4 Results

In total, I collected data on 88 marine fishes and 77 freshwater or diadromous fishes, 107 of which had catch data available. Most parameters exhibited positive relationships with sample size (App. 4). For number of haplotypes and number of alleles, the correlations with sample size were almost always highly significant. In the primary dataset using 'all fishes', all parameters were significantly correlated with catch, in both the original format and when data based on small sample sizes were removed (Table 1). However, differences in the strengths of correlation were apparent among parameters, with weaker correlations for nucleotide diversity compared to number of haplotypes and

10 haplotype diversity for mtDNA and weaker correlations for observed heterozygosity compared to other parameters for microsatellite data (Table 1). Marine fishes and freshwater/diadromous fishes, analyzed separately, showed the same general patterns as the combined dataset (Table 1). Higher correlation coefficients and more significant associations with genetic diversity were found with catch data compared to body size (Tables 1, 2). Nevertheless, most parameters showed the expected negative correlations with body size, and a clear improvement in correlations with microsatellite diversity in particular was seen when data based on small sample sizes were removed (Table 2). Despite the highly significant correlation between body size and catch (r=-0.824 for marine fishes, -0.575 for freshwater fishes, and -0.695 in total dataset, all with p<0.001), overall results suggested that catch was better than body size at describing abundance in fishes. As with correlations with catch, the weakest correlations with body size were seen with nucleotide diversity and observed heterozygosity for mtDNA and microsatellites, respectively (Table 2). This pattern was also evident when marine and freshwater fishes were analyzed independently (Table 2). Significant correlations were also found between mtDNA diversity (k, H, 71) and microsatellite diversity (NA, He) across all fishes, with nucleotide diversity again showing the weakest correlations of the three (Table 3). Correlations between the two marker types were stronger for marine fishes than for freshwater/diadromous fishes (Table 3), although sample sizes, particularly for microsatellites, were smaller for freshwater/diadromous fishes (App. 3). The number of loci examined had a strong effect on results. Higher correlations with abundance were found when more than six loci were used compared to six loci or fewer (Table 4). When six loci or fewer were used, observed heterozygosity showed a negative association with catch, the inverse of what was expected, although the correlation was non-significant (Table 4). When more than six loci were used, all four microsatellite parameters were significantly associated with both catch data and body size (Table 4), and higher correlation coefficients were found with catch than in any other dataset. When small sample sizes (<40) were removed from the dataset with more than six loci, correlation coefficients were largely unchanged for correlations with catch,

11 although they increased markedly for correlations between body size and diversity (Table 4). Nevertheless, correlations with body size were still substantially lower than for catch data.

LOG (CATCH)

H NA Ho He marine n 53 fc 57 54 ; p-value 0.007 I 0.008 0.077 f^Plpij 0.058 r 0.368 0.350 0.242 &fl3K?3 0.275

FW-diadromous n 37 38 35 p-value 0.004 0.037 0.482 r 0.464 0.340 0.123

: all fishes n 95..J;:'-'.:'--. 89-; p-value <:0.001 #<6.0SFi: 6,03Q: r 0.439 -T OJJ&'S 0.23$

LOG (CATCH)

H NA Ho He

marine n 42 4rt 43 p-value 0.020 I 0.008 0.086 ~'<0j0J^ 0.092 j : r 0.357 j 0.386 • 0.265 ' OttKJ. 0.289 1

FW-diadromous n 27 \X p-value 0.005 : 'i.l.;: 0.232 i^oj)iSll^5l r 0.522 ! n:''l 0.316

all fishes ,<69 .^2 i.hn-,.. ::rf'>I&* -J'-mt.t p-value <0.0ai^ 0.474! 0.252- -•ffiP7- ^0315* -'0:62ffF:- 0.3 iu> •

12 Table 2. Correlation statistics between genetic diversity and body size for the original dataset (top) and for those data with sample sizes >20 for mtDNA and >40 for microsatellites. Significant correlations (p<0.05) are highlighted in gray. LOG (BODY SIZE)

H 71 NA Ho He marine n 75 79 75 75 68 ..£*7ft-o '»»70' . p-value 0.822 0.779 0.624 <0.001 0.422 ;.:v:0.01^^0,005i •fij r -0.026 -0.032 0.058 -0.400 -0.099 \^42V$F^3$4T

FW-diadromous n 64 65 65 66 75 75 p-value 0.122 0.911 0.201 0.919 0.526 0.069 r -0.195 -0.014 -0.161 -0.013 -0.074 -0.211

all fishes n 139 147 135 140 128 137 ?,137,(.' p-value 0.387 0.204 0.741 0.001 0.739 0.132 t*W»Sv r -0.074 -0.105 0.029 -0.266 • -0.030 -0.129 y -0.236* LOG (BODY SIZE)

H NA Ho He marine n 61 66 61 46 p-value 0.700 0.470 0.758 0.245 "o.o^^p).o^|j r -0.050 -0.090 0.040 ..^0.583^1 -0.175

FW-diadromous n 42 52 44 21 26 p-value 0.150 0.062 0.778 0.086 0.097 r -0.226 -0.261 -0.044 -0.384 -0.333 '-0.44Tfe:0.517r;

all fishes n 103 111 100 :• ;.71£$ 66 6&v * 69^ p-value 0.314 0.148 0.991 0.229 i4®l'V'<0,001 ' 5 r -0.100 -0.138 0.001 V0;500'.-: -0.150 :'|To5386% ^-0:457^

13 Table 3. Correlation statistics between mtDNA and microsatellite diversity for marine, FW-anadromous species, and for the combined data set. Significant correlations (p<0.05) are highlighted in gray.

NA He k H n Jk H n

marine n 64 A8 65 • Jrv'6p;s® " ;:'6?%|? 61 p-value 0.020 • 0.008 0.067 'j8$^S'^ffi& °-057 r 0.2W) 0.321 0.229 !"|oS?7^^K&S?3 0.245

FW-diadromous n 56 61 55 .! 58#v 63 56 p-value 0.166 0.203 0.874 IftDJf;:,. 0.070 0.605 r 0.188 0.165 0.022 {'}J$Z13:.'4 0.230 0.071

all fishes

When data for mtDNA were split according to the parts of the genome analyzed, the results varied depending on which mtDNA regions were considered. Compared to the primary dataset, correlation coefficients usually increased when the 'non-CR' and 'mixture' groups were analyzed separately, whereas the 'CR' group produced lower correlations (Table 5 a). Contrary to expectations, considering the putative neutrality of the control region, the 'CR' showed the lowest correlations of the three groups, with no significant correlations (Table 5a). Removing sample sizes less than 20 had little overall effect on correlation coefficients (Table 5b). Correlation coefficients did increase when nucleotide diversity outliers were removed, particularly in the case of the 'non-CR' group (Tables 5a,b). In fact, for the 'non-CR' and 'mixture' groups, correlation coefficients for nucleotide diversity after removing outliers were comparable to those for haplotype diversity (Tables 5a,b). In no case was the number of base pairs or restriction enzymes analyzed significantly associated with any of the mtDNA parameters whether or not the effect of sample size had been removed.

Family-level analyses were designed to assess whether or not trends found at the species level were also apparent at the family-level. Initial analyses of the 'all fishes' dataset suggested that patterns were maintained at the family level, but that correlations were almost always lower when values were averaged among species within a family

14 compared to when one species was chosen per family (Table 6a). Family-level results, when one species per family was chosen, were clearly as strong as the species-level results for microsatellites, but the mtDNA results were more equivocal (Table 6a). However, a separate analysis for the three mtDNA groups found that family-level results were comparable to species-level results (Table 6b).

LOG (C ATCH)

NA Ho He A < 6 loci n 43':.'.- • 39 43 43 p-value : 0.001 0.533 0.216 0.083 r 0.497 -0.103 0.193 0.267 50 > 6 loci n 48 I .54 .-: 54- p-value • 0.001 --jp.ooi ' -o.op;'. •^a.001 r 0.667 6.670 " 0.772.'' V&.762

> 6 loci n 27 *32. ..'34 \, -M. (n>40) p-value <0.00l 4.001 •:'°-°°x,'; •feo-QP1 r 0.658 0.588 0.775 ' :^0/?47

LOG (BODY SIZE)

NA Ho He < 6 loci n 66 67 65 65 p-value 0.252 0.095 0.477 0.287 r -0.143 0.206 0.090 -0.134

...-3.V "•=•• > 6 loci n 73. ,..i68. ,.;;- ••••'"i: >vv-'• p-value 0.003 • '•,. ojorV r -0.343; :: > 6 loci n 37 • |44 ,,\ 44:;r::J " §S--^ (n>40) p-value 0.001 ;.^£o.530 i> £

15 Table 5a. Results for three different classifications of mtDNA: CR, non-CR, and mixture. Significant correlations (p<0.05) are highlighted in gray.

LOG (CATCH) LOG (BODY SIZE)

H 71 7C-OUt H 7I-OUt CR n 37 42 40 36 63 67 67 63 p-value 0.098 0.199 0.203 0.155 0.282 0.289 0.228 0.368 r 0.276 0.202 0.206 0.242 0.138 0.131 0.149 0.115

non-CR n 32 .5 / 2" 25 V.45: -£v 32 ;:-**28, -t p-value -'0.001 0.006 i'.|" 0.010 ;o;M.J &0ifl 0.746 iMMoM r 0.622 0.443 n.l'S 0.506 -0^508-' *$• -0.060 m38Q ^

mixture n 44 -'9 I . 35 56 p-value •'0.001 0.001 0.024j 0.005 ite* r 0.537 0.502 0.361? 0.463 M032M

LOG(CATCH) LOG(BODY SIZE)

#haps H 71 7t-OUt #haps H 7t 7C-OUt CR n 24 29 27 23 47 53 47 43 (n>20) p-value 0.136 0.236 0.102 0.131 0.540 0.556 0.423 0.614 r 0.313 0.227 0.322 0.324 0.092 0.083 0.120 0.079

non-CR n 29 30 26 34 " 36 29 25 (n>20) p-value • 0.001 0.006 0.743 0.032 ••0.001, 0.005 0.828 0.133 r 0.692' 0.490 0.068 0.458 ' -0.649 -0.460 0.042 -0.309

mixture n 31 32 28 26 39 41 35 32 (n>20) p-value 0.003 0.00.5 0.066 0.034 0.011 0.009>' 0.086 0.106 r 0.512 0.503 0.352 0.401 -0.101 -0.401 -0.295 -0.291

16 Table 6a. Family-level relationships between genetic diversity and abundance for all fishes. Family-level values were derived by averaging among all species within a family and using one species per family (see text for details). Significant correlations (p<0.05) are highlighted in gray.

LOG (CATCH)

H NA Ho He Average n 47 47 43 44 45 ••.'•:. 43 ^ p-value 0.104 0.081 0.456 - 0.001 0.431 # r 0.240 0.257 0.117 l).^> 0.120 0.386'-.' ^404

One n lf> 46 42 43 45 -•.v42 .-WV. 42-'.*•*< p-value 0.010 0.037 0.732 -o.ooi 0.129 ^o.oor r 0.378 0.309 0.055 0.634 0.229

Average One species per family LOG (CATCH) LOG (CATCH)

k H 71 k H 71 CR n 24 24 22 24 26 22 p-value 0.279 0.074 0.224 0.074 0.070 0.450 r 0.230 0.372 0.270 0.371 0.361 0.170

non-CR n 19 21 20 •' i'fer. 21 20 p-value 0.007 0.115 0.859 :-,vo:ob1p 0.078 0.834 r 0.5V5 0.355 -0.042 0.393 0.050

mixture n 28 28 24 • ...'24*- -.-^28 . 24 p-value 0.007 0.010 0.058 :i :* 6:w3HSe? 6-022.-rJ 0.299 r : 0.495 0.479 -. 0.392 ifX-€iM69fr" 0.432T-' 0.221

Finally, I found that at the population level, marine fishes had higher diversity than FW-diadromous fishes for every diversity measure (k: 20.8 vs. 8.5; H: 0.75 vs. 0.59,

7i: 0.015 vs. 0.008, NA: 14.1 vs. 7.3; H0: 0.69 vs. 0.54 He: 0.74 vs. 0.59), although sample sizes were also slightly higher for marine than freshwater fishes on average (49 vs. 40 for mtDNA; 69 vs. 48 for microsatellites).

17 2.5 Discussion

The main goal in this study was to determine whether or not mtDNA and microsatellite diversity was correlated with abundance in fishes. In most cases, results showed that they were, and that mtDNA and microsatellite diversity was also correlated with one another. Despite the inexact nature of using catch data to approximate abundance, correlations were all significant in the primary dataset with all fishes, both in the original dataset and when smaller sample sizes were removed (Table 1). With the exception of observed heterozygosity, correlation coefficients for microsatellite markers were slightly higher than those for mtDNA in the primary dataset, when small sample sizes were removed (Table 1), and the highest correlation coefficients overall were found for microsatellites (Table 4; see App. 5 for scatter plots of highest correlations). The poor performance of observed heterozygosity compared to expected heterozygosity was not too surprising, but interesting nonetheless. It suggested higher variance associated with observed heterozygosity, possibly reflecting widespread but sporadic null alleles, genotyping error, or population substructure. Overall, correlation coefficients for genetic diversity and catch were as high as 0.692 for mtDNA, and 0.775 for microsatellites (Tables 4, 5). Thus, (converting r to r2) up to 60% of variation in microsatellite data and 48% of variation in mtDNA data could be accounted for by differences in catch sizes. Interestingly, results for both markers were comparable to that found for allozymes and population size by Soule (correlation of 0.7) (Frankham 1996). Given that the data were based on range-wide catch statistics and not population abundance per se, the true correlation for these markers could be even higher, although this is speculative. The discrepancy between the two marker types in strength of correlations can at least partially be accounted for by the fact that microsatellite data represented averages across multiple loci, higher sample sizes, and often multiple studies, whereas the mtDNA typically represented a single gene (mtDNA region) from a single study. Secondly, although difficult to gauge, the number of base pairs analyzed may also have influenced mtDNA diversity estimates and reduced correlations. The influence of the number of base pairs assessed was not easy to discern across fishes, probably due to low power. Its effect may have been overshadowed by the influence of other factors (i.e. differences in diversity among species or the effect of mtDNA region) and power may have been low

18 because the number of base pairs assessed did not vary widely across studies (average: 585 bp, stdev: 390 bp). Nevertheless, the number of base pairs (or restriction enzymes) surveyed most likely influenced diversity estimates, particularly number of haplotypes and haplotype diversity, and therefore, overall results. Visual assessments pointed to another potential difficulty in estimating diversity in mtDNA, wide variation in nucleotide diversity. One possibility is that data were occasionally mis-reported, with decimals rather than percentages. However, a large proportion of the 'outlier' values were based on RFLP. Although in theory diversity estimates based on RFLP should not differ from those based on sequences (Ferguson & Danzmann 1998), if enzymes were chosen based on the level of polymorphism detected, RFLP studies could result in higher diversity estimates compared to sequence-based studies which can not simply remove portions of the sequence that are not polymorphic. Nevertheless, it remains possible that these so-called 'outliers' were valid data, and simply reflected the large amount of variation of nucleotide diversity in nature. The improvement in correlations with larger sample sizes, more loci, and after separating mtDNA data by region analyzed was not surprising and to some extent validated the approach of using both catch data and body size as proxies for abundance. While the advantages of increasing sample sizes and number of loci are fairly straightforward, these analyses reinforced the notion that mutation patterns and/or rates vary among mtDNA regions to such a large extent that assessments of diversity in mtDNA should take into account the region analyzed. Interestingly, the effect of sample size was apparently stronger for microsatellites than mtDNA and stronger in correlations with body size than with catch (Tables 1, 2). The first result was not necessarily surprising, as microsatellites are highly diverse, and larger sample sizes are required to estimate diversity. However, the second finding was curious. More species were included in correlation analyses with body size than with catch, and the improvement in correlations after data based on small sample sizes were removed may have been due to the particular species involved. Another possibility is that correlations were already high with catch, and they may have reflected the strongest relationships obtainable with data of this type.

19 One unexpected outcome was the lack of significant results in the 'CR' dataset, particularly given the putative neutrality of this region and the widespread use of the control region in population studies. However, a visual assessment of the data suggested that this may have been due to very high diversity in this group (Figure 1). A large number of species in the 'CR' group had very high haplotype diversity, and presumably a large number of haplotypes, probably due to the high mutation rate in the control region, which may have decreased the power to detect differences in abundance across species. This points to the difficulty of assessing diversity with mtDNA alluded to earlier, particularly with number of haplotypes and haplotype diversity. Number of haplotypes and haplotype diversity will only correlate with abundance if the number of base pairs analyzed and mutation rates are such that not all haplotypes are unique, and diversity differences among species can be detected. Nucleotide diversity, in contrast, should not be affected by number of base pairs analyzed, yet correlation coefficients were still relatively low (Table 5). This may be related to the large variation in mutation rate across the control region as well as among species (Faber & Stepien 1997; Galtier et al. 2006; Bowen et al. 2006). Although a visual assessment of the data did not lead us to believe that phylogenetic constraint existed in the dataset, I nevertheless attempted to account for the pseudo-replication that would result if phylogenetic constraint did occur. Species within families showed substantial variation in diversity and catch statistics which, I would argue, lead to loss of signal when averages were taken across these dissimilar species. When one species per family was chosen, however, correlation coefficients were largely comparable to species-level analyses (Table 6,a,b). On the whole, I found higher diversity in marine fishes than in freshwater species, as expected. Although I can not discount the possibility that I simply surveyed different species from Bazin et al. (2006), I consider another hypothesis that may account for their result of greater diversity in freshwater fishes compared to marine fishes. Given barriers to gene flow in the freshwater environment, freshwater species are likely to maintain distinct haplotypes in separate populations throughout the range more easily than marine fishes. When examined on a species level (as was done by Bazin et al., 2006), these distinct haplotypes may produce higher nucleotide diversity in freshwater fishes than

20 marine fishes, even if population level diversity is lower. To examine this possibility, I compared average within-population variation to total variation where these data were reported in the literature. Total variation in this case was not necessarily across the whole range of a species, but rather total variation in the study. Within-population diversity was found to be similar to total diversity in marine fishes (n=21), whereas in freshwater/diadromous fishes (n=7), they were quite different, possibly reflecting this phenomenon of maintaining divergent haplotypes in isolated populations (Figure 2). Both haplotype and nucleotide diversity reflected this difference between freshwater and marine species (Figure 2). Although genetic diversity is theoretically expected to reflect abundance, there are several reasons why this correlation may be weak or non-existent, even under selective neutrality. One reason is that mtDNA and microsatellites may reflect historical population size rather than the current one. Although microsatellites are expected to reflect more recent population size due to their faster mutation rate compared to mtDNA, correlations with both markers may suffer if significant historical population size change has occurred. This would likely affect both the correlation between diversity and current population size, as well as the correlation between the two markers. Secondly, under neutrality, genetic diversity is expected to reflect effective population size (Ne) rather than census size (N). Thus the degree to which Ne:N differs among species, as well as between FW and marine species, will also negatively affect correlations. Perhaps the most intriguing result in this study was the varying degrees to which different diversity parameters were associated with abundance. I was particularly interested in the fact that nucleotide diversity showed lower correlation coefficients compared to number of haplotypes and haplotype diversity for mtDNA. Bazin et al. (2006) found that nucleotide diversity did not vary with population size as expected for a neutral marker and they concluded that natural selection is a major force shaping mtDNA diversity. I would argue that the present study provides evidence that mtDNA diversity does vary with population size, as expected for a neutral marker. Nevertheless, despite significant associations between mtDNA diversity and catch, the reason for the weaker correlations with nucleotide diversity compared to the other mtDNA diversity parameters remains unclear. Nucleotide diversity exhibited far more variation than any other

21 parameter within species, suggesting a high degree of sampling noise in the estimation of this parameter. The striking improvement in correlation coefficients for some mtDNA groups when 'outliers' were removed attests to the influence of this variation on correlations (Table 5). Thus, the weaker correlations between nucleotide diversity and abundance may simply be the product of sampling noise, both in terms of individuals sampled and methodology used. However, it is also possible that other factors are at play. Nucleotide diversity is a complex parameter. It reflects both the frequency of various haplotypes as well as the evolutionary divergence among them. Due to the lack of recombination in mtDNA, this evolutionary divergence is slow to break down following historical isolation or introgression, and is also slow to increase following periods of small population size. Therefore, nucleotide diversity is more strongly affected by historical events such as mixing of glacial races, introgression, and fluctuations in population size. Both the mixing of glacial races and introgression can have a strong effect on nucleotide diversity while leaving number of haplotypes and haplotype diversity relatively unchanged. Although I would argue that these results for mtDNA are largely consistent with neutral expectations, the poor performance of nucleotide diversity relative to other parameters allows for some speculation. Previous studies have shown marine fishes to have perplexingly low nucleotide diversity, and explanations range from historical fluctuations in population size to selective sweeps (Graves 1998; Grant & Bowen 1998). One intriguing possibility is, therefore, that the varying strengths of correlations found in this study reflect the relative speed with which the different parameters can increase in value following an episode of low diversity. Microsatellite diversity in general responds faster than mtDNA diversity, and for mtDNA, number of haplotypes and haplotype diversity respond faster than nucleotide diversity. Certainly I found no direct evidence for selection in this study, but the data do not allow us to completely rule out selection either. Whether these results still leave room for the possibility of selective sweeps depends on whether or not the data suggest a history of low diversity in these species, and whether that low diversity was due to population size fluctuations or to selection.

22 I acknowledge that this paper does not constitute evidence of selective neutrality for either mtDNA or microsatellites, and neither marker appears to be influenced by population size alone. However, this is likely also true for the allozymes and nuclear DNA used by Bazin et al. (2006). I acknowledge the inherent problems associated with measuring diversity in both marker types, and I acknowledge that hitchhiking in regions that do not recombine (e.g. mtDNA) is more likely than in loci that do (Charlesworth et al. 2003). Natural selection, however, is only one of many evolutionary forces affecting variation in mitochondrial and nuclear DNA, and it is not necessarily the dominant one. Although selective neutrality is an ideal that genetic markers may not always meet, they may come close enough to still serve a purpose in conservation and phylogeographic studies. This is not the first study to reexamine the findings of Bazin et al. (2006) and to come to a different conclusion (Mulligan et al. 2006; Nabholz et al. 2008; Atkinson et al. 2008). However, this is the first (I know of) to reexamine these questions in fishes and these findings do not support the theory that selection is the dominant force shaping genetic diversity in either microsatellites or mtDNA.

23 0.10 • CR n non-CR 0.08 i o mixture

0.06

i A O 0.04 H A A^ A a

0.02 o

I JB

0.0 0.2 0.4 0.6 0.8 1.0 H Figure 1. Haplotype and nucleotide diversity for three mtDNA datasets: 'CR', 'non-CR', and 'mixture'.

24 1,0 FW/Diad Marine 0.8 I 0.6

0.4

0.2 i

0.0 """T""""""" "'—""""I" Within Total Within Total

U.U1X ' CZZ? FW/Diail

0.008

0.004- T ; i i 0.000 Within Total Within Total

Figure 2. Within population diversity versus total diversity (per study) for freshwater and marine species with error bars representing 95% standard deviation. Both haplotype (upper) and nucleotide (lower) diversity show that total diversity is higher than population- level diversity in freshwater species whereas they are comparable in marine species.

25 Chapter 3: Phylogeography of Three North Atlantic Wolffish Species (Anarhichas spp.) with Phylogenetic Relationships within the Family Anarhichadidae

Status: submitted, Journal of Heredity

3.1 Abstract

Phylogeographic analyses of Atlantic, spotted, and northern wolffishes, as revealed by the D-loop and ND1 mtDNA sequences, suggest that all three species have experienced significant population expansion following the last glaciation. Higher haplotype and nucleotide diversity in the eastern Atlantic Ocean is consistent with an eastern glacial refuge for wolffishes with subsequent colonization of the western Atlantic. Uncertainty regarding mtDNA mutation rates, however, makes the inference of a single refuge somewhat tenuous. Phylogenetic relationships within the family Anarhichadidae were evaluated with both mtDNA and AFLP. Results indicated that the three North Atlantic wolffishes are monophyletic with Atlantic and spotted wolffishes as sister species. Based on estimated mutation rates, wolf-eel and Bering wolffish, in the Pacific Ocean, diverged between four and six and a half million years ago, whereas the three species of wolffishes in the Atlantic Ocean diverged within the past two million years.

3.2 Introduction

Pleistocene glaciations are known to have had a profound effect on species distributions as well as on patterns of intraspecific genetic variation (Bernatchez & Wilson 1998; Hewitt 2000). Although the impact of glaciation on freshwater and terrestrial organisms has been widely studied, the impact on marine organisms has received far less attention. During the most recent glaciation, ice reached its maximum extent approximately 20 kya (CLIMAP 1976; Mix et al. 2001). Glacial ice reached as far south as 40° N in the western Atlantic and 50° N in the eastern Atlantic, with ice sheets extending well into the marine environment on both coasts (CLIMAP 1976; Peltier 1994). The combination of the greater extent of ice sheets and more compressed isotherms in the western Atlantic is thought to have made this region less hospitable than the eastern Atlantic to many marine species during the last glaciation (Wares &

26 Cunningham 2001). Not surprisingly, a variety of species that occur in the western Atlantic, particularly rocky intertidal invertebrates, are known to have colonized post- glacially from the eastern Atlantic (Ingolfsson 1992; Wares & Cunningham 2001; Henzler & Ingolfsson 2008), perhaps facilitated by ocean currents from Europe to the North American coast (Ingolfsson 1992). However, not all North American coastal populations originated from post-glacial colonization from Europe. A growing consensus is emerging that parts of the North American coast remained ice-free during glaciation (Peltier 1994; Shaw et al. 2006), and that suitable habitat existed for anadromous and marine fishes (Bernatchez & Dodson 1991; Brunner etal. 2001; Hickerson & Cunningham 2006; Dodson et al. 2007; Bigg et al. 2008; Makinen & Merila 2008). I examined the effects of glaciation on North Atlantic wolffishes, a group of sedentary demersal marine fishes with limited dispersal potential (Templeman 1984; Riget 1986; Scott & Scott 1988). The family Anarhichadidae comprises two genera containing a total of five species. The Atlantic, spotted, and northern wolffishes are found in the Atlantic and Arctic oceans (Scott & Scott 1988), while the Bering wolffish and wolf-eel are found in the Pacific. The current distribution of Atlantic wolffish extends from Massachusetts, north to Baffin Island in the western Atlantic, across to the Barents and White seas, and as far south as northern France in the eastern Atlantic (COSEWIC 2000). Spotted and northern wolffishes have similar ranges, although they do not extend quite as far south on either coast (COSEWIC 2001a; COSEWIC 2001b). The northern wolffish extends the furthest northwest of the three species, and has been documented in Mould Bay, North West Territories, Canada. Wolffishes are solitary species and do not form large schools (Scott & Scott 1988). They build nests on the hard substrate of continental shelves and they feed mainly on benthic invertebrates. In Atlantic wolffish, the best studied of the three North Atlantic species, males guard the eggs for up to nine months and larvae emerge at roughly 20 mm, able to feed exogenously (Moksness & Pavlov 1996). Fecundity was measured in the laboratory setting as 1,000-7,000 eggs per female (Moksness & Pavlov 1996). Tagging studies of adults from all three species have shown that most migrations tend to occur over short distances, with only occasional long distance migrations (100s of kms) (Ostvedt 1963; Jonsson 1982; Templeman 1984; Riget 1986; Riget & Messtorff 1988).

27 Wolffishes are not of major commercial importance, but they have been affected by commercial fisheries directed towards other species. Their sedentary nature and nest building behavior have made them vulnerable to habitat degradation by bottom trawlers as well as by-catch (Collie et al. 2000; COSEWIC 2000). The Committee on the Status of Endangered Wildlife in Canada (COSEWIC) has assessed these species as either "threatened" (northern and spotted wolffish) or "special concern" (Atlantic wolffish), and all three species are currently listed under the Canadian Species at Risk Act (SARA) (COSEWIC 2000; COSEWIC 2001a; COSEWIC 2001b). The first goal of this study was to assess the phylogeography of North Atlantic wolffishes to determine if they were affected by glaciation and where they likely survived. The second goal was to identify any deep genetic divergences within these species that might be important for future conservation. The final goal was to clarify phylogenetic relationships and mutation rates among species. Based on whole mitochondrial genome sequencing, Johnstone et al. (2007) found that the Atlantic and spotted wolffishes are sister species (97% bootstrap support) within North Atlantic wolffishes. They reported relative mutation rates across genomic regions, but not absolute rates. To this end, I included samples of Bering wolffish and wolf-eel both to assess phylogenetic relationships within the family Anarhichadidae and to estimate mutation rates based on divergence between the North Atlantic (Atlantic, spotted, and northern wolffishes) and Pacific lineages (Bering wolffish) dating to the Trans-Arctic Interchange. The D-loop and the ND1 gene were chosen as genetic markers because the D-loop has been shown to be highly variable in a variety offish species (Brown et al. 1993; Faber & Stepien 1997), and ND1 was found to be a reliable indicator of mtDNA phylogenetic relationships in gadids (Coulson et al. 2006). Maternal inheritance and lack of recombination make mtDNA an ideal marker for phylogenetic analysis, but mtDNA still only represents a single locus. Therefore, phylogenetic relationships were also assessed using amplified fragment length polymorphism (AFLP) to determine whether nuclear DNA corroborated relationships based on mtDNA.

28 3.3 Materials & Methods

Wolffish samples were collected using trawl nets or long-lines from the North Atlantic, Arctic, and Pacific oceans (Figure 3; Online Map Creation) from 2002 to 2006 (Table 7). DNA was extracted using either a glassmilk extraction protocol (Elphinstone et al. 2003) or QIA DNeasy extraction kits. The D-loop and ND1 regions were amplified using a standard polymerase chain reaction (PCR) with primers developed from conserved regions in other fish species (eelpout, gunnel and tuna sequences from GenBank) (App. 6). PCRs of 10)0,1 volumes contained lx ThermoPol buffer (lOmM Tris-

HC1, pH 8,3; 50 mM KC1), 20-100 ng DNA, 2.0 mM MgCl2, 50 uM each dNTP, 0.5 U Taq DNA polymerase (New England Biolabs), and 0.3-0.5 uM each primer. Thermal cycling conditions for the D-loop were as follows: 96°C 1 min; 30 cycles of 95°C 30 s, 60°C 30 s, and 72°C 1 min; 72°C 5 min, and then held at 4°C. Thermal cycling conditions for the ND1 were identical to that for D-loop except that a 58°C annealing temperature was used instead of 60°C.

Table 7. Sample locations and sample sizes of each species in the family Anarhichadidae.

NAFO/ Location T^CO Atlantic spotted northern Bering wolf-eel A Scotian Shelf 4VX 18 8 B Gulf of St. Lawrence 4TVn 15 C Newfoundland 3KLMNO 15 21 16 D Mid-Atlantic Ridge Xllal 14 E Iceland Va 20 16 19 F North Sea IVb 23 G Barents Sea IIa2 16 17 16 Bering Sea Pacific Ocean 2 Total 107 54 73 1 2

PCR products were visualized on agarose gels stained with EtBr, purified using Omega 10 filter plates, and then re-examined on agarose gels with 100 bp Gene Ruler ladder (Fermentas Life Sciences) to quantify the PCR product. DTCS kits (Beckman- Coulter) were used for sequencing PCRs with 2 ul DTCS, 1.6 uM primer, 50-100 fmol of PCR product and ddLbO for a total of 10 ul. Thermal cycling conditions for the

29 sequencing reaction were as follows: 96°C 20 s, 50°C 20 s, 60°C 4 min, for 30 cycles followed by holding at 4°C. A solution of 3M sodium acetate (NaAOAc), lOOmM, NaiEDTA, and 20mg ml"1 glycogen was used to stop the reaction, followed by an ethanol precipitation. Samples were resuspended in 40 ul deionized formamide and sequenced with a Beckman-Coulter CEQ 8000. Sequencing PCRs were performed using a combination of the original primers and internal primers (App. 6) which were developed after examining initial sequencing results. For the majority of individuals, both sense and anti-sense sequences were produced in order to verify results. Sequences were compiled using Sequencher v 4.2 (Gene Codes Corporation) and all unique D-loop and ND1 sequences were deposited in Genbank (Accession numbers: EU095868-EU095896 for D- loop, EU095897-EU095934 forNDl sequences). AFLP analysis was performed according to Vos et al. (1995), as modified by Agresti et al. (2000), using XbaX rather than EcoRl as the rare cutter. Selective amplification was performed with all combinations of £coRl-AGA, -AGC, -ATA, -ATC and Xba-GGA, -GGC, -GT, -GC, resulting in 16 primer combinations. PCR products were imaged using 6% Sequagel on a LI-COR system. Gels were run for approximately six hours under standard conditions (1500v, 35-45 mA, 45 watt), and bands were scored up to approximately 300 bp using Saga2 software (LI-COR Biosciences, Lincoln, Nebraska). Two individuals were chosen from each of Atlantic, spotted, and northern wolffishes, along with one Bering wolffish individual and one wolf-eel individual for AFLP analysis. Scoring errors were evaluated with a blind duplicate of the Bering wolffish sample.

3.3.1 Phylogenetic Relationships and Speciation Timing MtDNA sequences from all five species were aligned in Clustal X 1.83 (Thompson et al. 1997), using the slow-accurate setting, with a 15 point penalty for opening a gap and a 6.66 point penalty for extending a gap by one residue. Phylogenetic analysis of the five species in the family Anarhichadidae was performed with PAUP* 4.0 (Swofford 2002) based on maximum likelihood using the HKY model with gamma distribution shape parameter = 0.8377, as chosen by the Hierarchical Likelihood Ratio Test in Modeltest (Posada & Crandall 1998) for the D-loop-NDl composite haplotypes. Phylogenetic analysis was also performed based on distance using the Neighbour-Joining

30 algorithm (using the HKY85 mutation model with Gamma distribution shape parameter = 0.8377) with 1000 bootstrap replicates. D-loop and ND1 regions were analyzed separately as well as together in a composite haplotype, in case mutation patterns and rates varied between the two regions, with wolf-eel as an outgroup. Percent mtDNA sequence divergence among species was determined using DXY with the Jukes and Cantor correction in DNAsp 4.10.9 (Rozas et al. 2003). Lineage- specific mutation rates were then calculated for each of Atlantic, spotted, and northern wolffishes based on divergence from the Bering wolffish (presumably the closest relative to the three North Atlantic wolffishes in the Pacific Ocean given existing taxonomy, (Scott & Scott 1988)) using the equation u=(l/2)d/t (where u=mutation rate in years, d=percent sequence divergence, t=divergence time between the Pacific and Atlantic lineages). A faunal migration from the Pacific to the Atlantic is estimated to have occurred ~3.5 mya (3.1-4.1 mya), associated with the opening of the Bering Strait (Vermeij 1991; Marincovich & Gladenkov 1999). This event was likely associated with change in ocean currents from a southward flow to a northward flow through the Bering Strait, presumably facilitating migration from the Pacific to the Atlantic (Marincovich & Gladenkov 1999). Calibrating divergence time between species using the Trans-Arctic Interchange, however, is challenging as the Bering Strait has opened multiple times throughout history. For example, an earlier opening 4.8-7.4 mya was estimated based on fossil calibration (Marincovich & Gladenkov 1999), and evidence from urchins, Strongylocentrotus pallidus, suggests much more recent trans-oceanic migration -90 kya-150 kya (Palumbi & Kessing 1991). Lineage-specific mutation rates will be calculated based on the 3.5 mya estimate, but considerable discussion will be devoted to possible error around that estimate.

Speciation timing was also evaluated with Bayesian MCMC phylogenetics using the program BEAST vl .4.7 (Drummond & Rambaut 2007). A single composite D-loop-NDl haplotype (haplotype 1) was chosen from each species (as recommended by the authors), and speciation events were estimated with the Yule model, considered appropriate for divergence at the species level. The HKY mutation model was used which was selected by the Hierarchical Likelihood Ratio Test in Modeltest for the composite haplotype, with a strict molecular clock. The mean tMRCA parameter was converted to

31 years by dividing by an average of species-specific mutation rates (per year). Two independent runs of at least 10 chains were compared and then combined using LogCombiner. For the AFLP analysis, presence/absence data were evaluated in PHYLIP (Felsenstein 2005) based on both Wagner parsimony and genetic distance (Nei & Li 1979), with distance-based trees constructed using the neighbour-joining algorithm. Bootstrap values were based on 1000 permutations, initially using wolf-eel as the outgroup, and then with Bering wolffish as the outgroup (see Results).

3.3.2 Intra-specific Variation I evaluated diversity (gene diversity and nucleotide diversity) within samples for each species with Arlequin v.2.0 (Schneider et al. 2000), and geographic trends were tested by regressing diversity measures on longitude with a least squares linear regression in SYSTAT (v.l 1). Intra-specific patterns of variation were based on D-loop-NDl composite haplotypes. Haplotype networks based on maximum parsimony were created to illustrate relationships among haplotypes within species using Network v.4.2.0.1 (http://www.fluxus-technology.com). Pair-wise FST values among samples were calculated with Arlequin v.2.0 (Schneider et al. 2000). Tajima's D was used to assess deviations from a neutral, stable population model in Arlequin v.2.0 (Schneider et al. 2000). As significant departures from expectations can also be due to non-neutrality, natural selection was evaluated (for ND1) by comparing synonymous and non-synonymous substitutions within and among North Atlantic species using a McDonald-Kreitman test in DNAsp (Rozas et al. 2003). I also tested for deviations from a population expansion model with the mismatch distribution to determine whether or not the timing of population expansion corresponded with the end of glaciation (-20 kya). Time since expansion (x=2ut) as well as effective population sizes before and after expansion were determined (0i=2Nei|J., and 02=2Ne2|x, where u=mutation rate per generation per locus). Population trajectories for each species as well as effective population sizes were also evaluated with Bayesian Skyline plots using the program BEAST vl .4.7 (Drummond & Rambaut 2007). A chain length of at least 107 was used following a burn- in of 106 assuming a linear model of population size change. ESS values were assessed

32 to ensure they were well above the minimum criterion of 100. The HKY mutation model was used which was selected by the Hierarchical Likelihood Ratio Test in Modeltest for the composite haplotype, with a strict molecular clock. Two independent runs were compared and combined in LogCombiner. Skyline plots were visualized in Tracer v. 1.4 (Rambaut & Drummond 2007) and model parameters were converted to demographic parameters (years and Ne) by dividing by the mutation rate per year (for time estimates) and by the mutation rate per generation (for Ne estimates) using a per bp mutation rate. Species-specific estimated mutation rates were used in all cases. Finally, I used the program Isolation with Migration, IM (Hey & Nielsen 2004), to evaluate divergence times between eastern and western Atlantic samples. An estimate within the last 20 kya would be considered consistent with a single refuge hypothesis, whereas a substantially earlier divergence would be considered consistent with multiple refugia. I used IM as it allows for migration to occur post-divergence and does not assume equilibrium conditions. Stationarity and convergence of parameters were evaluated by assessing ESS values, mixing (according to output graphs), and whether posterior probability density produced a smooth curve after a minimum of a 105 burn-in period and 106 iterations. A geometric heating scheme was used with a minimum of 20 chains and 20 swap attempts per step, and consistency across multiple runs was evaluated. Demographic parameters were converted from model parameters using species specific mutation rates (q=4Neu, where \i is the mutation rate per generation per locus; t=T|a in years, where u is the mutation rate per year per locus; Nemi= qimi/2, as indicated in the program manual). As my objective was to assess whether or not wolffishes survived glaciation on both sides of the Atlantic, samples were divided into those from the western Atlantic (Scotian Shelf, Gulf of St. Lawrence, Newfoundland) and those from the eastern Atlantic (Iceland, Barents Sea, North Sea). Mid-Atlantic Ridge (MAR) samples did not clearly belong to either group, and analyses were therefore performed in three ways: including MAR with western samples, including MAR with eastern samples, and leaving MAR samples out of the analyses. Part of this work was carried out by using the resources of the Computational Biology Service Unit from Cornell University which is partially funded by Microsoft Corporation.

33 3.4 Results

In total, 855 bp were sequenced from the D-loop region (including indels) and 975 bp from the ND1 region, producing a composite haplotype of 1830 bp (see Table 8 for number of haplotypes per species). Nine indels were found in the D-loop within the family Anarhichadidae, five of which differentiated wolf-eel from the four wolffish species (genus Anarhichas) and seven of which differentiated wolf-eel from the three species of wolffishes in the North Atlantic Ocean. Variation in the ND1 region included 11 amino acid changes across the five species, six of which differentiated wolf-eel from the four wolffish species (App. 7). The pattern of amino acid changes suggests convergent evolution at amino acid 75 (App. 7). Despite changes to the amino acid sequence within and among wolffish species, the McDonald-Kreitman test revealed no evidence of selection for ND1. Moreover, selection was not inferred from comparisons of non-synonymous to synonymous changes (d^-ds) across wolffish mitochondrial genomes (Johnstone et al. 2007).

Table 8. Number of haplotypes and ratio of transversions to transitions (tv/ti) for D-loop, ND1, and composites of the two regions (D-loop indels are in parentheses) within three species of wolffishes, genus Anarhichas, and the family Anarhichadidae. n D-loop tv/ti ND1 DL-ND1 tv/ti haplotypes (indels) haplotypes haplotypes Atlantic 107 15 0.09 (1) 20 0.06 33 spotted 54 6 0.25 (1) 10 0.09 13 northern 73 6 0.25 (0) 6 0.20 12 North Atlantic wolffishes 234 27 0.20 (2) 36 0.10 58 Genus Anarhichas 235 28 0.37 (4) 37 0.13 59 Family Anarhichadidae 237 29 0.46 (9) 38 0.16 60

3.4.1 Phvlogenetic Relationships All phylogenetic trees produced identical topographies with respect to species relationships whether they were based on D-loop, ND1, or the composite haplotype, using distance or maximum likelihood, with one exception. The likelihood model for ND1 placed the Bering wolffish haplotype with northern wolffish haplotypes, although it had an exceptionally long branch length when clustered this way. Bootstrap analysis of distance data for ND1, however, separated the three Atlantic species from the Bering

34 wolffish with a bootstrap value of 94%. As all other analyses had identical topographies with respect to species-level clustering, a single phylogenetic tree based on distance with the composite haplotype was produced to illustrate relationships (Figure 4). Each species formed a monophyletic group with 100% bootstrap support, and Atlantic and spotted wolffishes clustered together with 93% bootstrap support (Figure 4). Atlantic, spotted, and northern wolffishes also formed a monophyletic group within the genus Anarhichas with 100% bootstrap support (Figure 4). Two strikingly different ND1 haplotypes were found in spotted wolffish, one in the Newfoundland sample and one in the Barents Sea sample. Each differed from the most common haplotype by five mutations when every other haplotype in the species differed from the most common haplotype by only one mutation (in ND1), and each was only found in a single individual. However, these two haplotypes still clearly clustered with spotted wolffish in a phylogenetic analysis (spotted wolffish haplotypes 4 and 11, Figure 4) and the translated amino acid sequence was identical to other spotted wolffish haplotypes. Re-extracting the individuals and re-sequencing the region did not change the result, therefore these haplotypes remained in all analyses unless otherwise indicated. In the AFLP analysis, 1157 polymorphisms were scored among the five species, 819 of which occurred among the four wolffish species, and 528 of which were present among the North Atlantic wolffishes. Scores for the duplicated Bering wolffish sample differed at 3.2% of bands, which is in the range typically found for AFLP studies (Bonin et al. 2007). The two samples from each species clustered together 100% of the time in all analyses. With wolf-eel as outgroup, distance analysis resulted in the identical topology to that found with mtDNA. The three North Atlantic species were monophyletic (100% bootstrap support), and northern wolffish was found to be ancestral among the three North Atlantic species (65% bootstrap support) (Figure 5). The parsimony-based tree also found the three North Atlantic wolffishes to be monophyletic (100% bootstrap support), however Atlantic wolffish was found to be ancestral to northern and spotted wolffishes (62% bootstrap support). Interestingly, if wolf-eel was removed from the analysis, both distance and parsimony produced identical trees to that found with mtDNA, with northern wolffish ancestral to Atlantic and spotted wolffishes (65% bootstrap support).

35 3.4.2 Mutation Rates and Speciation Timing Based on percent sequence divergence among species (Table 9), mutation rates for the three North Atlantic species were estimated to be between 5.6-5.9 x 10"9 for D-

Q loop and 0.9-1.2x10" for ND1. Average mutation rates for the composite haplotype were 9.1 x 10"9 per bp per year for Atlantic wolffish, 8.7 x 10"9 per bp per year for spotted wolffish, and 7.7 x 10"9 per bp per year for northern wolffish, with an overall average of 8.5 x 10"9. Maturity is reached at eight years in Atlantic wolffish (COSEWIC 2000), seven years in spotted wolffish (COSEWIC 2001b), and five years in northern wolffish (COSEWIC 2001a), and these values were used as the generation time. Per generation mutation rates for the composite haplotypes were therefore estimated as 7.3 x 10"8 for Q Q Atlantic wolffish, 6.1 x 10" for spotted wolffish, and 3.8 x 10" for northern wolffish.

Any error associated with the generation time would affect estimates of Ne but not divergence time, as conversion of theta to Ne is based on u in generations, whereas divergence time is estimated in years. Based on percent sequence divergence, the D-loop results indicated that Atlantic and spotted wolffishes diverged most recently, whereas ND1 results showed divergence times among the three species to be almost identical (Table 10). The timing of speciation events based on the Yule process from BEAST was similar to those based on pairwise sequence divergence for D-loop and ND1 (Tables 10, 11). Table 9. Percent sequence divergence (pairwise differences) among species based on D-loop (below diagonal) and ND1 (above diagonal) with standard deviation. Atlantic spotted northern Bering wolf-eel Atlantic 2.79 ± 0.05% 2.55 ± 0.04% 8.29 ± 0.80% 12.30 ±1.03% spotted 1.51 ±0.03% 2.52 ± 0.06% 7.89 ±1.06% 12.25 ±1.44% northern 2.09 ± 0.04% 2.10 ±0.05% 6.65 ± 0.77% 11.62 ±1.17% Bering 4.16 ±0.40% 4.07 ± 0.55% 3.91 ± 0.46% 14.06 ± 7.03% wolf-eel 5.04 ±0.42% 5.68 ± 0.67% 5.66 ± 0.57% 4.63 ±2.31%

36 Table 10. Divergence time estimates among species (yrs) based on D-loop (below diagonal) and ND1 (above diagonal) with standard deviation. Estimates of 3.5 mya between North Atlantic species and Bering wolffish were by definition (see text). Atlantic spotted northern Bering wolfeel Atlantic 1,205,000 1,194,000 3,500,000 5,659,000 ± 23,000 ± 20,000 ± 337,000 ± 472,000 spotted 1,281,000 1,214,000 3,500,000 5,637,000 ± 26,000 ± 27,000 ± 472,000 ± 660,000 northern 1,814,000 1,839,000 3,500,000 5,347,000 ± 30,000 ± 42,000 ± 407,000 ± 540,000 Bering 3,500,000 3,500,000 3,500,000 6,468,000 ± 337,000 ± 472,000 ± 407,000 ± 3,234,000 wolf-eel 4,362,000 4,914,000 4,899,000 4,002,000 ± 364,000 ± 575,000 ± 494,000 ±2,001,000

Table 11. Speciation timing as indicated by time to most recent common ancestor (tMRCA) based on D-loop-NDl composite haplotypes from BEAST. Parameter estimates from BEAST (top) were converted to years (below) by dividing by the average estimated mutation rate (8.5 x 10"9 per bp per year). tMRCA mean lower CI upper CI Atlantic-spotted 1,057,000 759,000 1,369,000 North Atlantic species 1,549,000 1,176,000 1,929,000 wolffishes 3,707,000 3,073,000 4,367,000 wolffishes and wolfeel 5,677,000 4,881,000 6,487,000

3.4.3 Intra-specific Variation Intra-specific diversity measures based on individual sample sites showed that Atlantic wolffish had the highest diversity of the three North Atlantic species with the exception of Scotian Shelf and Gulf of St. Lawrence samples for Atlantic wolffish (Figure 6). In all three species, a subtle trend emerged of decreasing diversity from the eastern Atlantic to the western Atlantic, particularly for nucleotide diversity in Atlantic wolffish (Figure 6). Nucleotide diversity was significant when regressed on longitude in Atlantic wolffish (p=0.015), although gene diversity was not (p=0.064). In spotted wolffish, although lower diversity was found in Newfoundland compared to either Iceland or Barents Sea, the similar diversity levels in Iceland and the Barents Sea resulted in no significant relationship with longitude overall. In northern wolffish, nucleotide diversity was significant when regressed on longitude (p=0.01) when Mid-Atlantic Ridge (MAR) samples were removed from the analysis. With MAR samples included, however, no associations were significant, presumably because MAR had such low

37 diversity (Figure 6). In all three species, more haplotypes were found to be unique to the eastern Atlantic than the western Atlantic (Figure 6; Apps. 8-10). Whether MAR samples were grouped with eastern or western samples was irrelevant as only two haplotypes were found there and they both occurred on both sides of the ocean. FST values were highest (0.085-0.139) and significant (p=0.001- 0.026) between the two western Atlantic samples (Scotian Shelf and Gulf of St. Lawrence) and eastern Atlantic samples in Atlantic wolffish. Only one other comparison within Atlantic wolfish, Iceland vs. North Sea, was significant (FST=0.068; p=O.005). In spotted wolfish, FST values were significant between Newfoundland and both Iceland and Barents Sea (Fsx=0.085-0.133; p=0.014-0.05). No significant differences were found for northern wolffish.

3.4.4 Population Expansion and Intra-specific Divergence Haplotype networks revealed star-like phylogenies for all three species (Figure 7), suggestive of population expansion, which was generally borne out in statistical tests. Tajima's D values were negative and significant for Atlantic, spotted, and northern wolffishes (-2.045, p<0.004; -1.934, p<0.007; -1.602, p<0.019, respectively). The McDonald-Kreitman test revealed no evidence of selection, so significant negative values from Tajima's D were considered consistent with population expansion. Mismatch distribution results were also generally consistent with population expansion (Figure 8). Despite its unimodal mismatch distribution (Figure 8), Atlantic wolffish samples could not combined as parameters did not converge (which occurs when the mean is larger than the variance), however when samples were subdivided between east and west, both groups conformed to a model of sudden expansion. The time since expansion was estimated at 37 kya (95% CI: 0-53 kya) for western samples, and 73 kya (95% CI: 30-88 kya) for eastern samples. Northern wolffish also resulted in a unimodal distribution (Figure 8), and did not reject the model of sudden expansion. Time since expansion for the species as a whole was estimated at 36 kya (95% CI: 14 kya-47 kya). Spotted wolffish samples showed a bimodal distribution due to the two unusual haplotypes (Figure 8), which marginally rejected the model of sudden expansion (p=0.05) using Harpending's Raggedness index, and which did reject the model of sudden

38 expansion (p=0.03) using the sum of squared deviation. Nevertheless, the model parameters (albeit with a suspect model in this case) indicated a comparable increase in population size over time to the other two species, with time since expansion estimated at 37 kya (95% CI: 11.6-49.2 kya). Population size estimates for all groups were estimated as zero for Nei (prior to expansion), and produced estimates in the millions for Ne2 (post- expansion). According to Bayesian Skyline plots, all three species clearly experienced population size increases post-glacially, with the entire period of expansion in spotted and northern wolffishes well within a post-glacial time frame (Figure 9). Results indicated that Atlantic wolffish population expansion (~30 kya) started slightly before that of spotted and northern wolffishes. Spotted wolffish analyses performed both with and without the two unusual haplotypes were virtually identical, with the only difference being the time to the most recent common ancestor (tMRCA) (or the length of the tail on the x-axis). The program IM was run multiple times for all species, and only those runs that met the requirements for stationarity and convergence were evaluated. Priors of 20 (recommended for most datasets) was sufficient for all datasets except for ql and q2 (population size parameters) in Atlantic wolffish. Atlantic wolffish was therefore analyzed twice with much higher priors (ql and q2=500). Although larger priors were clearly sufficient to encompass the values for ql and q2, the two runs produced very different estimates for population size (23,000 to 101,000 for Nei and from 128,000 to

659,000 for Ne2). Although population size estimates clearly varied, particularly for Atlantic wolffish, divergence time estimates remained remarkably consistent, which was the main parameter of interest. In total, I took averages of four runs for Atlantic wolffish (with ql and q2 estimates from only two of the four runs), five runs for spotted wolffish, and six runs for northern wolffish. All four runs presented for Atlantic wolffish were based on 1 million iterations. As runs with large priors took three to four weeks to run, increasing the number of iterations would have been very time-consuming, and as already mentioned, the main parameter of interest, divergence time, was similar across runs. Spotted and northern wolffish runs, on the other hand, both included one run of 10 million iterations,

39 in order to verify results over a longer run. As results were consistent with previous runs, all runs were averaged to produce the final estimates (Table 12). When the two divergent spotted wolffish haplotypes (found in one individual each) were included in the IM analysis, the divergence time was difficult to define. The posterior probability curve was broad, with multiple peaks, the dominant one usually being very large (>500 kya), and ESS values never reached acceptable levels. Without these two samples, posterior probability curves were sharp and consistent and clearly showed a divergence time of less than 20,000 years ago (Figure 10; Table 12). This suggests a complex history with possible evidence of ancient isolation still lingering in current genetic variation. I chose to present the results without the two individual outliers, reasoning that the outliers obscured an otherwise clear signal of recent divergence in this species. Northern wolffish was estimated with and without MAR, and the placement of MAR did not strongly affect results. Results were presented without MAR samples. Results for all species suggested divergence time estimates close to if not clearly within a post-glacial period of <20 kya. Although the time parameter (t) for northern wolffish was smaller than for Atlantic wolffish, the slower estimated mutation rate in this species made the divergence time slightly older (Table 12). The very large 95% confidence intervals produced by IM were somewhat misleading in this analysis, and they did not substantially decrease with increasing numbers of iterations. Although confidence limits were large, dating back hundreds of thousands if not millions of years, more detailed depictions of likelihood curves showed that the probability density of pre- glacial divergence (100 kya or earlier) was low in all species (Figure 10). Ancient population sizes, which were easily defined and estimated in all species, were smaller for spotted and northern wolffishes than Atlantic wolffish (Table 12). Migration rate values for all species indicated very little trans-oceanic migration (m<0.005). As this parameter reflects migration after divergence, it does not necessarily bear on the initial direction of colonization. The parameter 's' is better suited to assess the direction of colonization as it estimates the proportion of the ancestral population which founded each of the two current populations. Neither spotted nor northern wolffish showed a strong signal for 's', indicating insufficient data to accurately estimate

40 this parameter. However, Atlantic wolffish consistently showed a low value of s' (<0.01) suggesting that a very small portion of the ancestral population may have founded the western Atlantic population. Table 12. EM estimates (above) and converted parameters (below) from EM with 95% confidence intervals. Nel and Ne2 are current effective population sizes of western and eastern Atlantic populations, respectively. NeA is the ancestral population size, and 2Nimi, 2N2m2 are the number of effective migrants per generation into western and eastern populations, respectively.

qi q2 qA t mi m2 Atlantic wolffish 33 209 3 0.48 0.5 1 lower 95% CI 32 109 1 0.28 0.16 0.28 upper 95% CI 2,677 2,413 1,001 8.61 13.72 15.08

spotted wolffish 2 7 0.03 0.25 0.1 3 lower 95% CI 0.95 2.96 0.23 0.17 0.16 0.40 upper 95% CI 37.77 38.49 38.06 19.22 19.12 19.29

northern wolffish 7.17 10.58 0.13 0.47 12 10 lower 95% CI 3.21 3.76 0.24 0.22 0.47 0.38 upper 95% CI 49.87 49.34 47.68 19.17 19.64 19.62

Nei Ne2 NeA Div Time 2Nimi 2N2m2 Atlantic wolffish 62,000 393,000 4,700 29,000 54 122 lower 95% CI 60,000 206,000 1,500 17,000 2 7 upper 95% CI 5,041,000 4,544,000 1,884,000 518,000 11,000 10,000

spotted wolffish 5,000 16,000 70 15,000 0.1 11 lower 95% CI 2,000 7.000 500 11,000 0 1 upper 95% CI 85,000 86,000 85,000 1,207,000 400 400

northern wolffish 26,000 38,000 460 34,000 40 60 lower 95% CI 12,000 13,000 900 17,000 1 1 upper 95% CI 197,000 195,000 188,000 1,368,000 500 500

3.5 Discussion

3.5.1 Origins of Family Anarhichadidae The opening of the Bering Strait and the subsequent migration of fauna from the Pacific to the Atlantic Ocean is widely thought to have occurred approximately 3.5 mya (Vermeij 1991). The two methods of assessing speciation timing with mtDNA (percent sequence divergence and the Yule process) provided similar results, and both were

41 consistent with wolffish colonization from the Pacific to the Atlantic Ocean during the trans-Arctic interchange. Interestingly the Yule process may have accounted for homoplasy better than percent sequence divergence, as recent speciation events were estimated to be more recent and ancient divergence events were estimated to slightly older than with sequence divergence (Tables 10, 11). Although its maternal inheritance and haploid nature makes mtDNA ideally suited to phylogenetic reconstruction (Zink & Barrowclough 2008), its neutrality and reliability (as a single locus) have come into question (Moore 1995; Ballard & Whitlock 2004; Bazin et al. 2006). The congruence between AFLP and mtDNA data provides strong support for the phylogeny presented in this study. Although only two individuals per species were used, it is not uncommon to assess phylogeny with only a few individuals, even for AFLP studies (Buntjer et al. 2002; Despres et al. 2003). It is worth noting, however, that AFLP data, like mtDNA data, are also subject to homoplasy (Vekemans et al. 2002), and homoplasy becomes more likely at higher taxonomic levels (Bonin et al. 2005). Furthermore, reconstruction of recent speciation events may be more difficult with nuclear DNA due to slower lineage sorting for nuclear DNA compared to mtDNA. These factors may help to explain the difference between results when wolf-eel was included versus when it was not as well as the relatively low bootstrap support for relationships within the North Atlantic wolffishes (62-65%). Results from this study revealed an interesting contrast between the two oceans. The Pacific Ocean is characterized by the deep lineages of the wolfeel and the Bering wolffish, with no recent speciation events, whereas the Atlantic Ocean has three species that diverged relatively recently and over a short period of time. Despite the apparent similarity in prey, all three species can be distinguished by their arrangement of teeth (COSEWIC 2000; COSEWIC 2001a; COSEWIC 2001b), suggesting that some degree of trophic specialization may have occurred.

3.5.2 Post-glacial Population Expansion Estimates of population expansion and intra-specific divergence for spotted and northern wolffishes generally fit within a post-glacial time frame, and are consistent with a single refuge during glaciation. All three species of wolffishes appear to have experienced striking population expansion during the post-glacial era. These species are

42 characterized by low diversity, and limited geographic heterogeneity across their range, which both appear to be due to a very low ancestral population size, dating back to the end of the last glaciation. Although all analyses point to very low ancestral population sizes, this is consistent with either low historical abundance or, alternatively, variable survival during the post-glacial period. The limited dispersal potential of wolffishes may have made it difficult for them to move to more suitable habitat during the warming period, and this may have been even more difficult for spotted and northern wolffishes which have more northerly distributions. Range shifts associated with both climate cooling and climate warming may have been more gradual for Atlantic wolffish, resulting in higher survival in this species compared to spotted and northern wolffishes (Table 12). Whatever the exact mechanism, results clearly show that wolffishes were affected by the profound changes that occurred in the past 20 thousand years, and that the two more northerly species, due to either low abundance or limited survival during range shifts associated with the last glaciation, seem to have experienced more severe bottlenecks than Atlantic wolffish (Table 12).

3.5.3 Mutation Rate Estimate Repeated glaciation events during the Pleistocene are thought to have occurred on an approximate 100,000 year cycle (Peltier 1994). Although intraspecific divergence times estimated in this study do not fit strictly within a post-glacial time frame of less than 20 kya for all species in all analyses, nor did they fit a model of isolation during the last interglacial (135-115 kya) (Hewitt 1996). Certainly warmer and cooler periods occurred during glaciation, however, the maximum extent of glaciation is widely considered to be -20 kya, and population expansion leading up to this coldest period is somewhat unlikely. One possible reason for the discrepancy in the timing between these results and a strict post-glacial time frame lies with the mutation rate. Although estimating the mutation rate based on the Trans-Arctic Interchange 3-4 mya seems a plausible approach, several potential sources of error are worth mentioning. The foremost is the question of timing as the opening of the Bering Strait has occurred on multiple occasions. The first opening of the Berring Strait may date back as far as 4.8-7.4 mya (Marincovich & Gladenkov 1999), although this likely would have been associated with a southward flow through the Berring Sea. On the other hand, other

43 studies have suggested much more recent migration events (90-150 kya) (Palumbi & Kessing 1991). If wolffish migrated across the Arctic as early as 4.8-7.4 mya, mutation rate estimates would be on the order of 1.4-2.2 times slower than estimated here. This would directly affect divergence times estimated between western and eastern Atlantic samples, and would make the hypothesis of two refugia more likely. On the other hand, if trans-Arctic migration occurred more recently, mutation rates are likely underestimated, making the single refuge hypothesis more likely. Thus, considerable uncertainty remains in this analysis based on the timing of the trans-oceanic migration of wolffishes. Given that trends in diversity across the Atlantic Ocean are subtle for all three wolffishes, and that haplotype networks do not provide conclusive evidence that wolffishes survived the last glaciation exclusively in the eastern Atlantic, the alternative explanation of two refugia remains a distinct possibility. In addition to the ambiguity surrounding the timing of trans-oceanic migration, I consider other aspects of uncertainty in the mutation rate estimate. One is homoplasy in the mtDNA, which would have the effect of underestimating the mutation rate. Homoplasious mutations are expected in the mtDNA at about 5% divergence, increasing rapidly thereafter (Broughton et al. 2000). The percent divergence between Bering wolffish and North Atlantic wolffishes (3-4% in the D-loop and 6-7% in ND1) was, therefore, in the range where homoplasy may be an issue. Furthermore, the presence of homoplasious mutations in the data was suggested both by mutational networks (Figure 7) and the ratio of transversions to transitions (Table 8). As the taxonomic level increases from within North Atlantic fishes to within the family Anarhichadidae, the ratio of transversions to transitions also increases, particularly for the D-loop (Table 8). Another potential source of error is that the "rate of change" may not be constant over time, as suggested by a recent study on mtDNA (Ho et al. 2005). Ho et al. (2005) argue that purifying selection effectively slows down the rate of change over long time frames (> two million years), such that a single rate of change should not be used for estimating both ancient and recent divergence times. Given that the mutation rate estimated in this study was calibrated on an event that occurred 3-4 mya, and was then used to estimate population divergence in the last 100 kya, their results would suggest caution.

44 As an aside, it is also worth noting that although the mutation rate estimated in this study is typical for mtDNA ~1% per million years, the D-loop had an unusually slow mutation rate. Indeed, Johnstone et al. (2007) found the D-loop had lower variation than nine of 13 gene regions in the mtDNA genome, whereas the ND1 region was one of the more variable regions. The mutation rate for D-loop estimated in this study was slow compared to many other fishes by at least a factor of two, sometimes by an order of magnitude (Faber & Stepien 1997; Bowen et al. 2006). In the absence of further refinement of the mutation rate estimate used in this study, I suggest that an underestimate of the mutation rate can adequately account for the apparent expansion and divergence of wolffishes during glaciation (Table 12, Figures 9, 10), and that results are more consistent with a post-glacial time frame. However, there remains a subtle signal of different population expansion times among the three wolffish species. Spotted wolffish, in particular, consistently show later expansion times than Atlantic wolffish (Figures 9, 10; Table 12). Although speculative, this may suggest that population expansion did not occur in spotted wolffish, and possibly northern wolffish, until more northern habitat became available.

3.5.4 Glacial Refugia Although post-glacial population expansion has been documented in numerous species (Hewitt 1996; Nesb0 et al. 1998; Wilson 2006; Hickerson & Cunningham 2006; Makinen & Merila 2008) and was to some degree expected, the number and location of refugia used by wolffishes during the last glaciation were unknown. Despite the likelihood of harsher conditions and less habitat available in the western Atlantic compared to the eastern Atlantic, many species are thought to have survived on both coasts (Hickerson & Cunningham 2006; Bigg et al. 2008; Makinen & Merila 2008). Results from this study, however, suggest a single refuge was used by all three species of wolffishes. Nevertheless, given large confidence intervals surrounding these estimates, the alternative hypothesis of multiple refugia should be considered. Relatively high FST values between Scotian Shelf and Gulf of St. Lawrence samples in Atlantic wolffish, at least at the outset, seem to suggest deep intraspecific divergence in this species. However, if Atlantic wolffish did survive in an Acadian refuge, it would have been a very small population given the low diversity found in these

45 samples, and relatively recently founded, given the lack of novel haplotypes. Atlantic wolffish also shows the most obvious trend of decreasing diversity from east to west of the three species, with diversity (haplotype and nucleotide diversity as well as number of private haplotypes, App.8) being highest in the North and Barents seas. Therefore, an alternative explanation for the high FST values between Scotian Shelf/ Gulf of St. Lawrence samples and eastern Atlantic may be very low diversity in Atlantic Canada rather than different haplotypes (App. 8). Indeed the majority of haplotypes present in Atlantic Canada were the most common haplotypes found in the rest of the range (App. 8), suggesting post-glacial expansion into this region by a relatively small number of individuals. Genetic variation in wolffishes differs from that seen in other species thought to have survived on both coasts. The divergence between eastern and western populations of Atlantic cod {Gadus morhua), for example, was estimated at well over 100 kya with an abundance of private haplotypes on both coasts (Bigg et al. 2008). Another species thought to have survived in multiple refugia, Pholis gunnellus exhibited reciprocal monophyly on the two coasts (Hickerson & Cunningham 2006). The southern coast of the North Sea was thought to have been ice-free at the height of the last glaciation (Peltier 1994), and therefore that presents one obvious potential refuge for Atlantic wolffish. Higher diversity in the eastern samples in spotted and northern wolffishes is also suggestive of persistence on the European coast, although the exact location is not clear.

3.5.5 Conservation Implications Conservation efforts should focus on conserving distinct populations across the range. Evidence for significant population structure was found in this study using FST- Significant population structure was also detected in spotted wolffish in the only other study (I know of) to assess genetic variation across the range of this species, although species mis-identification was mentioned as a possible concern (Imsland et al. 2008). Further work on population structure using loci with greater power to detect fine-scale differences is warranted for all three species. Estimates of current population sizes of these species varied to a large extent. The mismatch distribution estimated them in the millions for all species, assuming a model of sudden expansion. Estimates from BEAST were in the hundreds of thousands

46 for all three species (Figure 9), whereas estimates from IM were in the tens of thousands for spotted and northern wolffishes, and slightly larger for Atlantic wolffish (Table 12). Effective population size estimates have notoriously large errors and depend on the model of expansion to some degree. However what was apparent from all analyses was the historically small population sizes of all three species at the end of the last glaciation, evidenced by the low nucleotide diversity in all three species. In a survey of Pacific marine fish mtDNA, Grant and Bowen (1998) identified four categories of marine fishes according to their haplotype and nucleotide diversity. Wolffishes not surprisingly, fit the second category characterized by low nucleotide diversity and high haplotype diversity, typical of species that have experienced population expansion after a period of low effective population size. Although their study was probably not meant to be used to compare diversity among species* the results suggest that compared to other marine species, wolffishes have low nucleotide diversity (Figure 11). The relevance of historical bottlenecks to current vulnerability is not clear, but it is at least plausible that low historical sizes have reduced the adaptive potential and resilience in these species. Species with low diversity and small population sizes are known to face increased risk from inbreeding depression and reduced adaptive potential (Frankham 2005; Willi et al. 2006). This study adds to a growing literature documenting the influence of Pleistocene climate change on the evolutionary histories of marine fishes. These data also suggest that population bottlenecks that occurred many thousands of years ago may still have repercussions for the adaptive potential and the conservation risk of species today.

47 -30° Figure 3. Sample sites for wolffishes across the North Atlantic (see Table 7 for location names).

48 Aflaxticwrfffisk I,— AC33 HA* 32

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49 Wolf-eel

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VI Figure 6. Gene diversity and nucleotide diversity across the range of three wolffish species (Atlantic wolffish, left; spotted wolffish, middle, northern wolffish, right).

51 Figure 7. Haplotype networks for Atlantic (upper), spotted (middle), and northern (lower) wolffishes based on composite D-loop-NDl haplotypes. Circle sizes are proportional to haplotype frequency. White haplotypes were only found in the western Atlantic, black haplotypes were only found in the eastern Atlantic, and gray haplotypes were found on both sides of the Atlantic Ocean. Asterisks represent single missing haplotypes. Haplotype numbers correspond to Apps. 8-10 and the phylogenetic tree (Figure 4).

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Figure 10. Atlantic (upper), spotted (middle), and northern (lower) posterior probability distributions of divergence time (in 1,000 years) from EM. Probabilities are based on 10 million iterations for spotted and northern wolffishes, and one million iterations for Atlantic wolffish. Estimates are based on species-specific mutation rates (see text).

55 D.J -

• Category 1 • 3.0 - o Category 2 • Category 4 A Atlantic wolffish

7 S< - • spotted wolffish

it y a northern wolffish S 20 - a> •8 1.5 - ••0 o H 1.0 - 9b 0.5 - o ° °%p°9 t • oo <^> o 0.0 - •• ° D -

-i— r •••— • —i— —r i ™ 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Haplotype Diversity Figure 11. Haplotype and nucleotide diversity in mtDNA from marine fish (taken from Grant and Bowen, 1998). Wolffish data from this study were included (see text for descriptions of different categories).

56 Chapter 4: Microsatellite Markers Discriminate Three Species of North Atlantic Wolffishes (Anarhichas spp.)

Status: published, Journal of Fish Biology

4.1 Abstract

Sixteen tetranucleotide and dinucleotide microsatellite markers were isolated from Atlantic wolffish, Anarhichas lupus, following a microsatellite enrichment procedure using probe-labelled magnetic beads. These microsatellites were intended for use in Atlantic wolffish as well as in two closely related species, spotted wolffish, Anarhichas minor, and northern wolffish, Anarhichas denticulatus. As all three species are of conservation concern in Canadian waters. As forensic wildlife cases may arise for this genus, microsatellite markers were assessed to determine how well they differentiate these species from one another and to estimate probability values that could be expected for identification of individuals to species.

4.2 Introduction

Three species of wolffishes inhabit the North Atlantic Ocean: the Atlantic spotted and northern wolffishes (Scott & Scott, 1988). In Canadian waters, wolffishes have suffered severe population decline over the past 30^10 years, probably due to substantial habitat degradation by bottom trawlers and by-catch (Collie et al. 2000; O'Dea & Haedrich 2000). Off Newfoundland, where Atlantic, spotted and northern wolffishes reach their highest densities in Canadian waters, Canadian Department of Fisheries and Oceans (DFO) scientific surveys indicate population decline of 91% for Atlantic wolffish, 96% for spotted wolffish and 98% for northern wolffish from c. 1978 to 1994 (O'Dea & Haedrich 2000; O'Dea & Haedrich 2001a; O'Dea & Haedrich 2001b). All three species are currently listed in the Canadian Species at Risk Act (SARA), with the Atlantic wolffish listed as 'special concern' and spotted and northern wolffishes listed as 'threatened'. The current designation of 'special concern' given for Atlantic wolffish is much more lenient than the 'threatened' status of spotted and northern wolffishes. Atlantic wolffish can still be caught without a permit, whereas it is illegal to possess spotted or northern wolffishes without one. The various levels of protection afforded

57 Atlantic, spotted and northern wolffishes under the Canadian SARA, which took effect in 2004, highlight the need for accurate species-level identification. Wolffishes are demersal, sedentary fishes on continental shelves or continental slopes and have unusual life histories for marine species, which makes population genetic questions of particular interest. Unlike many marine fishes with extensive pelagic egg and larval stages, Atlantic wolffish produce large demersal eggs guarded by the male until hatch (Keats et al. 1985). Larvae are large for marine species, c. 20 mm for Atlantic wolffish, and emerge with near-juvenile morphology (Moksness & Pavlov 1996). Eggs and larvae are large for spotted and northern wolffishes as well, although these species are not well studied (O'Dea & Haedrich 2001a; Foss et al. 2004). The ranges of Atlantic, spotted and northern wolffishes overlap extensively across the North Atlantic Ocean, with the Atlantic wolffish being the most abundant of the three. The Atlantic wolffish is distributed from as far south as the Gulf of Maine, with occasional strays off New Jersey, to western Greenland in the western Atlantic Ocean. In the eastern Atlantic Ocean, it extends as far east as the White Sea and Murman Coast, and as far south as the British Isles and western coast of France (Scott & Scott 1988). Spotted and northern wolffishes have similar ranges but do not extend as far south as in either the eastern or western Atlantic (Scott & Scott 1988). Microsatellite markers were developed to address the conservation concerns surrounding wolffishes in Canadian waters. Given that wolffish flesh is considered to be of excellent quality (particularly that of Atlantic and spotted wolffishes), genetic-based identification may be essential in fish markets and restaurants where gutted and headed fish may be morphologically unrecognizable. Molecular markers will also be useful for species identification at the egg and larval stages, when species identification is particularly difficult. Several analyses were undertaken to demonstrate the feasibility of identifying wolffish individuals to the species level using microsatellite markers. As assignment probability levels of p>0.999 have been set as a standard in forensic cases (Manel et al. 2002), this probability level was the focus in the present analyses. These markers may also find use in population genetics studies as well as in parentage studies, given the increasing interest in aquaculture for Atlantic and spotted wolffishes.

58 4.3 Materials & Methods

4.3.1 Sample Collection Atlantic, spotted and northern wolffish muscle or fin clip samples were taken in Newfoundland waters 2001-2003 and again in 2006 by the Canadian DFO (Table 13). Samples were pooled across Northwest Atlantic Fisheries Organisation (NAFO) regions and across years to comprise a total of 167 Atlantic wolffish, 55 spotted wolffish and 68 northern wolffish samples from the Grand Banks and Labrador Shelf. In addition, 10 samples (for each species at each site) were collected from various locations across the North Atlantic Ocean (Table 13). Trawl nets or long lines were used to collect samples, and collections were made (or overseen) by government or university personnel who identified the species of each fish sampled. North Sea samples, however, were collected from a fish market in Aberdeen, Scotland.

4.3.2 Genetic Analysis Genomic DNA was extracted from fin or muscle tissue following Elphinstone et al. (2003), modified to work with a 96 well filter plate. DNA from one Atlantic wolffish individual was used to create microsatellite-enriched libraries for AAAG, CATC and GACA repeats, following Hamilton et al. (1999). The microsatellite libraries were cloned using pDrive cloning vector (QIAGEN, Mississauga, ON, Canada), transformed into MAX Efficiency DH5 alpha Competent Cells (Invitrogen, Burlington, ON, Canada) and plated on imMedia Amp Blue agar (Invitrogen). Positive colonies were screened for suitably sized inserts (400-1000 bp) by direct polymerase chain reaction (PCR) amplification of colony picks using Ml 3 primers under standard PCR conditions and imaged with agarose electrophoresis. Plasmid DNAs were isolated using QIAprep Miniprep Kit (QIAGEN) and sequenced on CEQ 8000 (Beckman Coulter, Mississauga, ON, Canada). Primer3 software (Rozen & Skaletsky 2000) was used for primer design. Unique sequences were deposited in GenBank (Accession numbers EU095852- EU095867). Individuals were genotyped using PCR amplifications of 5 or 10 ml volume containing20-100 ng DNA, 2.0 mM MgC12, 50 mM each dNTP, 0.5 U Taq DNA polymerase (New England Biolabs, Pickering, ON, Canada), 0.3-0.5 mM each primer

59 (forward primers were 59 end labelled with IR700 or IR800 dye) and IX PCR buffer (10 mM Tris-HCl, pH 8.3; 50 mM KC1).

Table 13. General locations, NAFO/ICES regions, years collected, and sample sizes of wolffish sample collections. Reference population NAFO/ ICES No. of Species Location Year Collected Region samples Atlantic Newfoundland 3LNO 2001-2003 167 spotted Newfoundland 2J, 3KLN 2001-2003,2006 55 northern Newfoundland 2J, 3KLMNOP 2001-2003,2006 68 Sample populations to be assigned NAFO/ ICES No. of Species Location Year Collected Region samples Atlantic Scotian Shelf 4VX 2002 10 Atlantic West Greenland 1ABCDE 2004 10 Atlantic Iceland Va 2002, 2004 10 Atlantic Rockall VIb2 2006 10 Atlantic North Sea IVb 2002 10 Atlantic Barents Sea IIa2 2005 10

spotted West Greenland 1ABCDE 2004 10 spotted Iceland Va 2004 10 spotted Barents Sea IIa2 2004 10

northern Mid-Atlantic Ridge Xllal 2004 10 northern Iceland Va 2004 10 northern Barents Sea IIa2 2004 10

For the first set of primers (Alul, Alu9, AlulO, Alull,Alu\4), PCR conditions were as follows: denaturing at 95° C for 3 min; 5 cycles of the following: denaturing at 95° C for 20 s, annealing at 60° C (-1° C per cycle) for 20 s and extension at 72° C for 20 s; then 25 cycles of the following: denaturing at 95° C for20 s, annealing at 55° C for 20 s and extension at 72° C for 20 s; followed by an extension at 72° C for 3-5 min (Table 14). Reactions were run in either MJ Research or Eppendorf thermocyclers and imaged on IR2 DNA Analysers (LI-COR Biosciences, Lincoln, NE, U.S.A.). For the second set of primers (Alu2l,Alu22, Alu23, Alu24, Alu25, Alu26, Alu21, Alu2S, Alu29, Alu30, Alu3l), PCR conditions were as follows: denaturing at 95° C for 3 min; 30 cycle

60 of the following: denaturing at 95° C for 20 s, annealing at 50° C for 20 s and extension at 72° C for 20 s; followed by an extension at 72° C for 3-5 min. Reactions were run in either MJ Research or Eppendorf thermocyclers and imaged on an FMBioII scanner (MiraiBio, San Francisco, CA, U.S.A.).

Table 14. Repeat motif, Genbank number, and primer sequences of 16 microsatellite loci for amplification in Anarhichas spp. Repeat motif/ Locus Genbank No Primer sequence (5'->3') Alul (AAAG)3GAAGAAAGGAA(GAAA)7 GGAGCCTGATCTGCATCTGT EU095852 CCCCCTCTCAGTCTTTATGG Alu9 (CTAT)9 GCTGAAACACCCAAAGCAAT EU095853 ACATCAACATAGATAGATAGAAAGAAA AlulO (TAGA)ll TGTTGCAGCTGAGCCTTCTA EU095854 CAGCCAAGGACAGACAGATG AMI (TATC)13 CCTATCTGCTATAACTGCCTGTAAG EU095855 AACTGGAGCTGCCTCATTGT Alul A (CCAT)6 CTGGCAGGTGCACTGAATAA EU095856 TGCTGATCTCCTCATCACTTTT Alu21 (CATC)8 CGAGCAAAAGACTGACACCA EU095857 CAAAGCTATGGCACTGCAAA Alu22 (GT)14 ACTTCTTTATGCGGCAGTCC EU095858 TAAAGTGTCTGGGACCATGC Alu23 (GACA)5(GA)4(CAGA)5(GACA)2-(CA)6 CAGTAGAGGCATTTCACACATTG EU095859 GACATCGCCCTGATAGTTCC Alu24 (CG)5(CA)16 CAAGACAGCACTGGCACATT EU095860 GCATAGCCTGTCAGGAGCAT Alu25 (TGTC)IO CGGTTCTGCAAATGAACCTC EU095861 GGCAGAGACAGCAGACAGC Alu26 (CA)12(CC)(CA)6 TGGGTCTTACATGGCTAGGAA EU095862 GAGTTTTTGGCTTCGTTTGG Alu27 (TATQ16 CCGACAGCAGATTTAAGTGC EU095863 ATGCGAAACACCGATTATCC Alu28 (TCTA)21(TC)4 TCTCTTCAGCACCCACCTTT EU095864 GAATTCAAAACATGTCCCAACA Alu29 (GT)10 TGGCTGATGTGTTAGCCTTG EU095865 GGAAATGGTGTGGGAATCAC Alu30 (GA)15 AGGTGATGGAAACAATGTGG EU095866 TTCTCCGCTTCCTGTTTCAT AMI (CATC) 11 AGCCGATTGAGAAAAGCAAA EU095867 GTGTGGTGGCTAGCACTGTC

61 4.3.3 Statistical Methods Microsatellite markers were characterized with samples of Atlantic, spotted and northern wolffish from Newfoundland waters. Potential problems with null alleles, stutter and large allele dropout were examined with Microchecker (Van Oosterhout et al. 2004a). Probabilities associated with deviations from linkage equilibrium and Hardy- Weinberg expectation (HWE) were calculated with Genepop 3.4 (Raymond & Rousset 1995). Number of alleles, allele range size and observed and expected heterozygosities were assessed with Microsatellite Toolkit (Park 2001). As a gauge of how well assignment tests were likely to perform, FST between the three species from Newfoundland waters was calculated with Genepop 3.4 (Raymond & Rousset 1995). A two-dimensional factorial correspondence analysis was performed on these data in Genetix 4.05 (Belkhir et al. 2004), which also provided an indication as to how easily species could be discriminated and how well assignment tests were likely to perform. STRUCTURE (Pritchard et al. 2000) and GeneClass2 (Piry et al. 2004) were used to assess probabilities of an individual belonging to a given species. All individuals were grouped into a single file for the STRUCTURE analysis, as it was not possible to discriminate between reference and sample groups. A burn-in period of 50,000 was selected, followed by 200,000 runs, which should provide good estimates. The 'no admixture' model was chosen, as hybridization was not expected between species, and k (the number of populations) was set to three. The program calculated the probability of individuals belonging to one of the three populations (in this case, species) with no prior species information. In GeneClass2, an assignment test was performed with Newfoundland samples as the reference population, and all individuals from elsewhere in the range as the 'sample' population. The purpose of this test was to determine if accurate species identification across the range was possible based on Newfoundland samples alone. Individuals were classified based on the Bayesian method of computation, and exclusion probabilities were calculated following 1000 simulations of a Monte-Carlo re-sampling algorithm, unless otherwise specified (Paetkau et al. 2004). Unlike STRUCTURE, GeneClass2 does not require all possible source populations to be sampled. Instead, it computes an exclusionary probability if a potential source population is not the true source.

62 Individuals were assigned to a population in GeneClass2, if they were excluded from other populations (p < 0.001) but not from the population of putative origin (p > 0.001), following the evaluation procedure of Manel et al. (2002) in keeping with wildlife forensic standards.

4.4 Results

4.4.1 Microsatellite Characteristics Allele number, size range and heterozygosity levels varied among loci and among species (Table 15). Samples of Atlantic wolffish generally had more alleles and higher heterozygosities than those of spotted and northern wolffishes, and spotted wolffish generally had the lowest values. The range of alleles for a given locus, however, overlapped considerably among the species. No significant deviations from HWE were found in the 16 loci amplified in Atlantic wolffish, although some evidence of null alleles was found mAlu2l. Alul4 did not amplify in either spotted or northern wolffish. Of the 15 loci that did amplify in spotted wolffish, no significant deviations from HWE or indications of null alleles were found, although four loci (Alu\\,Alu24, Alu27, Alu28) were monomorphic in this species. In addition to Alu\4, Alu2% was not amplified in northern wolfish, as scoring was difficult due to large allelic sizes (>300 bp) and a high degree of apparent single base pair stutter. This locus was sequenced in northern wolffish with the hope of designing different primers resulting in a smaller fragment, but sequence analysis revealed a highly complex microsatellite, (GA)2o(GGGA)3(TGGA)9(TAGA)29(TGGA)8. As Alu2% was likely to be difficult to score, given the complexity of the microsatellite, it was not analysed further in northern wolffish. Of the 14 loci amplified in northern wolffish, one locus, Alul, deviated significantly from HWE and showed evidence of null alleles. After correcting for multiple comparisons with a Bonferroni correction (Rice 1989), no significant cases of linkage disequilibrium were found between these 16 loci in either species. Several additional points are worth noting. First, although the original sequence of Alul revealed a fairly complex repeat motif, it functioned as a tetranucleotide marker in all three species. Similarly, Alu2S had a di-tetra repeat motif but also functioned as a tetranucleotide marker in Atlantic wolffish. Alu23, however, functioned as a di-tetra

63 complex with many alleles 4 bp apart but with some 2 bp apart. Therefore, 10 loci appeared to consist of tetranucleotide markers, five of dinucleotide markers (Alu22, AlulA, Alu26, Alu29, Alu30) and one of a di-tetra complex (Alu23). However, occasional mutations appeared that were unpredictable from repeat motif. Some loci, Alu2A and Alu25 in Atlantic wolffish andAlu30 in spotted wolffish, had rare alleles lbp different from a more common allele. These alleles were binned with the closest allele that fitted a stepwise mutation pattern. Curiously, Alu25 had a distinctly non-stepwise mutation pattern in northern wolffish (alleles 119,123, 127, 131,135, 139, 142, 146 and 150). Although no evidence of null alleles or deviations from HWE appeared in the sample, this may signal a problem with this locus for future studies. Finally, a large gap (c. 40 bp) between most alleles and a larger allele appeared in Alu26 in northern wolfish, which suggested a non-stepwise mutation pattern for this species.

Table 15. Characteristics of 16 microsatellite loci in Atlantic, spotted, and northern wolffishes (HE= expected heterozygosity; Ho = observed heterozygosity). Estimates are based on 167 Atlantic, 55 spotted, and 68 northern wolffish individuals.

Atlantic wolffish spotted wolffish northern wolffish

Size range Size range Size range

Locus NA (bp) HE Ho NA (bp) HE Ho NA (bp) HE Ho Alul 6 232-252 0.70 0.71 2 236-240 0.50 0.40 6 236-256 0.67 0.50 Alu9 8 178-234 0.66 0.64 6 186-242 0.57 0.53 6 158-190 0.57 0.64 AMO 7 164-192 0.67 0.70 3 156-172 0.28 0.25 5 164-184 0.59 0.52 AMI 19 148-236 0.82 0.81 1 132 0.00 0.00 11 128-180 0.85 0.91 AluU 13 209-265 0.75 0.72 NA NA Alu21 11 202-246 0.76 0.65 2 202-206 0.38 0.36 8 198-230 0.71 0.70 Alu22 6 174-190 0.32 0.30 3 176-180 0.45 0.49 3 172-176 0.30 0.26 Alu23 10 193-235 0.62 0.65 6 215-277 0.42 0.45 2 211-231 0.21 0.21 Alu24 4 237-249 0.66 0.65 1 241 0.00 0.00 5 237-245 0.63 0.68 Alu25 7 117-149 0.66 0.62 9 149-185 0.79 0.80 6 119-150 0.58 0.47 Alu26 15 211-249 0.81 0.82 4 227-241 0.30 0.35 8 213-263 0.63 0.54 Alu27 18 207-283 0.86 0.84 1 171 0.00 0.00 17 235-307 0.93 0.92 Alu28 16 193-253 0.90 0.87 1 123 0.00 0.00 NA Alu29 6 163-179 0.55 0.53 3 177-181 0.45 0.38 2 161-165 0.09 0.09 Alu30 3 225-229 0.06 0.06 5 223-237 0.75 0.71 12 213-245 0.86 0.84 Alu31 13 130-178 0.85 0.83 5 118-142 0.47 0.40 4 118-134 0.17 0.18

64 4.4.2 Species-Level Identification To assess the application of these microsatellite loci to species-level identification, Alu\4 and Alu28 were removed, as they were not amplified in all three species. The remaining 14 loci resulted in highly significant FST values among species, with FST estimates of 0.43 between Atlantic and spotted wolffishes, 0.47 between spotted and northern wolffishes and 0.35 between Atlantic and northern wolffishes. The high FST values differentiating spotted wolffish from the other two species was probably a reflection of the low variation in spotted wolffish, accentuated by four monomorphic loci. A factorial correspondence analysis (FCA) plot showed strong differentiation among species (Figure 12), indicating that assignments to species would probably be accurate. STRUCTURE resulted in high posterior probabilities for samples belonging to either Atlantic, spotted or northern wolffishes. To determine whether fewer than 14 loci could be used to produce reliable results with the precision required, this program was run with eight loci (removing monomorphic loci in spotted and Alul) and again with five loci (Alu9, Alul 1, AluTl, Alu23, Alub 1). In all cases, correct assignments were made with 100% accuracy (P > 0.999). GeneClass2 also produced 100% accurate assignments of individuals taken throughout the range (with Newfoundland samples used as the reference population) with 14, eight and five loci. However, for eight loci, 100,000 simulations were required to meet the forensic criterion of certainty (Manel et al.,2002). Although the probability of belonging to a given population (species) was rarely as high as 0.999, the onus was placed on the reverse probability, that of being excluded. Probabilities of belonging to the wrong species were zero or O.001 for all individuals, and the probability of correct assignment was generally high, though with several Atlantic wolffish individuals from the eastern Atlantic, the probabilities of correct assignment fell below p < 0.05 (but were still well above the criterion of p > 0.001).

4.5 Discussion

Microsatellite markers reported in this study will be useful in studies of population structure in all three species, but additionally, the results indicated that they can be used to discriminate species with a high degree of confidence. As few as five microsatellite loci and baseline data from a single population for each species were

65 sufficient to identify individuals anywhere in the species' ranges to species with 100% confidence. Identifications of wide-ranging species with a single reference sample may be possible only in marine species, which inhabit a fluid environment with few barriers to dispersal and which have large population sizes and less population structure than freshwater or anadromous species. These results corroborate those in a study of microsatellites in rockfishes (genus Sebastes), which also indicated that individuals from distant populations were equally likely to be correctly assigned to species as individuals taken from the reference population (Pearse et al. 2007). These results also indicated a relatively low level of differentiation among wolffish species in the North Atlantic (FST ranged from 0.35 to 0.47). Higher FST values are not uncommon among freshwater fish populations using microsatellites (Hanfling et al. 2002; Triantafyllidis et al. 2002; Jacobsen et al. 2005). The ability to score 14 of 16 markers in all three species further indicates a close genetic relationship among these species. Nevertheless, the level of differentiation provided adequate resolution for species identifications. Results from STRUCTURE and GeneClass2 varied slightly depending on the number of loci used as well as on the number of simulations performed. Though probabilities associated with 'belonging' to a certain group in Gene-Class2 were not as high as for STRUCTURE, the strength of GeneClass2 lies in its calculations of exclusionary probabilities when all potential source populations probably unsampled. With this as the criterion, GeneClass2 performed just as well as STRUCTURE, with likelihoods associated with any of these individuals assigned to the wrong species well below a probability threshold associated with forensics (p < 0.001). GeneClass2 results based on five loci performed slightly better than with eight loci, suggesting that not all loci were equally useful in assignment tests. As previously mentioned, not all combinations of five or eight loci were tested in wolffish, and those loci chosen for analysis with five loci had a high probability of distinguishing among species. Nevertheless, the finding that fewer microsatellites can provide higher accuracy is not unique to this study. Fewer microsatellite loci produced higher assignment success at the population level in three species of redfish (genus Sebastes), though the same was not found at the species level (Roques et al. 1999). Furthermore, in red drum (Sciaenops

66 ocellatus L.), assignment success of wild fish to population of origin using GeneClass2 decreased after some point with the addition of more loci (Renshaw et al. 2006). Theoretical results suggest that a modest number of loci work best for assignment tests, each with a small number of alleles at intermediate frequency (Smouse & Chevillon 1998). Genetic data are likely to become an essential tool in forensic cases where illegal possession of species at risk is suspected. In fact, one case of genetic based forensic identification in wolffishes has already occurred. In 2005, the Canadian DFO charged an angler with illegal possession of northern wolffish specimens in Scotian Shelf. The specimens had been gutted and headed, making morphology-based identification difficult. The seven individuals were identified to species with mtDNA sequences at the Marine Gene Probe Laboratory (Dalhousie University). Microsatellites offer an alternative to the now widely accepted barcoding approach to identify species (Hebert et al. 2003). Although mtDNA has been demonstrated as an effective means of identifying species of marine fish (Ward et al. 2005), microsatellites offer a potentially lower cost alternative that maybe preferable for high throughput analyses. An economical and efficient analysis will be useful when separations of wolffish species in fisheries are of interest. Microsatellites also have the advantage of allowing detection of hybrids, which may not be identifiable with maternally inherited mtDNA. Further advantages over barcoding include the ability to calculate explicit P values and the potential directly to integrate results into stock identification. The results in this study demonstrate that species identification among wolffishes is possible with microsatellite markers, and that associated probabilities meet the standards necessary for forensic identifications.

67 89

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v> Chapter 5: Historical Influences Dominate the Population Genetic Structure of a Sedentary Marine Fish, Atlantic wolffian (Anarhichas lupus), Across the North Atlantic Ocean

5.1 Abstract

Genetic variation was assessed in Atlantic wolffish across the North Atlantic Ocean using microsatellite and AFLP markers. Analysis of population structure revealed the presence of four distinct groups: two within Atlantic Canada, one across the North Atlantic, and Rockall Bank. Range-wide FSJ values were low (<0.035), despite life history attributes such as large benthic eggs, large larvae, a limited pelagic stage, and relatively sedentary adults that suggested potential for strong population structure. Nonetheless, significant genetic differentiation and isolation by distance were observed both within and among these main groups, suggesting limited dispersal in this species.

AFLP loci were evaluated on a subset of samples which revealed higher FST and assignment values, but similar IBD estimates, compared to microsatellites. The genetic structure of Atlantic wolffish populations appears to have been shaped by their post glacial history of recolonization, life history features, downstream current distance, continuity of habitat on continental shelves, and population size.

5.2 Introduction

The marine environment is often perceived to be a relatively uniform ecosystem with few barriers to dispersal across wide geographic areas. The apparent passive dispersal of planktonic eggs and larvae of many marine species suggests high dispersal potential and "open" populations in the marine environment. However, there is a growing recognition that marine populations do not necessarily adhere to this model, and that gene flow is often limited, even in the absence of obvious geographic barriers (Ruzzante et al. 1999; Hellberg et al. 2002; Swearer et al. 2002; Palumbi 2003). The degree to which populations are connected by gene flow has become an important focus of research, both from the standpoint of designing marine reserves (Palumbi 2003; Botsford et al. 2003; Palumbi 2004) and in understanding the dynamics of recruitment in the marine environment (Levin 2006). The need to understand underlying population

69 dynamics has become particularly acute in recent years given problems of overfishing and habitat destruction (Levin 2006). Although planktonic larval duration has been shown to be associated with dispersal distance in a variety of marine organisms (Shanks et al. 2003; Siegel et al. 2003), and dispersal ability is generally thought to be linked to gene flow (Shaklee & Bentzen 1998; Bohonak 1999), population connectivity cannot be reliably predicted from life history attributes alone. Paradoxical patterns have been found, such as species with long planktonic stages showing limited gene flow even over very short geographical distances (Hellberg et al. 2002; Swearer et al. 2002). Oceanic currents and conditions, larval behaviour (as well as that at other life history stages), and historical extinction and recolonization patterns also play a role in determining patterns of population structure (Cunningham & Collins 1998; Hellberg et al. 2002; Palumbi 2003; Gaines et al. 2003; Levin 2006). Nevertheless, it is still reasonable to expect that those life history characteristics likely to reduce dispersal, such as demersal eggs, a short planktonic larval stage, and large larvae, should decrease the probability of gene flow and decrease connectivity among populations. The Atlantic wolffish is a demersal, sedentary species, typically found alone or in pairs, with a range extending from the Gulf of Maine to Baffin Bay in the western Atlantic Ocean, across to the White Sea and Murman coast and the western coast of France in the eastern Atlantic. The eggs, unlike those of many marine fishes, are benthic, and deposited in nests that are guarded by the male parent until hatch (800-1000 degree days, which can be up to nine months; (Pavlov & Novikov 1993). The eggs are among the largest of any marine teleost fishes, up to 6 mm in diameter (O'Dea & Haedrich 2000), and larvae emerge at -20 mm (Scott & Scott 1988; Moksness & Pavlov 1996). Wolffish larvae are thought to stay close to the nest after hatch (Bigelow & Schroeder 1953). In captivity, the larvae have been observed swimming to the surface, but they are negatively buoyant and sink to the bottom when they stop moving (Moksness & Pavlov 1996). Although never captured in large numbers, larvae and juveniles have been found throughout the water column at depths of 0-100s of meters deep (Bigelow & Schroeder 1953; Keats et al. 1986; Templeman 1986; Pavlov et al. 1987; Ortova et al. 1990; Shevelev & Kuz'michev 1990; Falk-Petersen et al. 1990). Nevertheless, due to

70 their negative buoyancy, large size, and relative rarity in surface waters, they are expected to be less subject to ocean currents than many marine species are (Bigelow & Schroeder 1953). At ~50mm, juveniles no longer react positively to light and begin to move to the ocean bottom; at 100mm, they become almost exclusively bottom dwelling (Moksness & Pavlov 1996). Tagging studies off Newfoundland and West Greenland have indicated that most adult movements occur over short distances, although occasional long distance migrations (over 200 km) have been recorded (Templeman 1984; Riget & Messtorff 1988b). Behavioural variation documented throughout the range in spawning time and depth (Barsukov 1959; Powles 1967; Keats et al. 1985; Pavlov & Novikov 1993; O'Dea & Haedrich 2000), as well as limited movement at all life history stages, suggest that significant population structure may exist. The first objective in assessing population structure was, therefore, to evaluate dispersal in a potentially highly structured species. I expected dispersal to be limited in Atlantic wolffish due to large benthic eggs, large size at hatch, a brief larval stage, and relatively sedentary adults. Atlantic wolffish are of conservation concern in Canada because of severe population declines over the past 30-40 years that are likely due to substantial habitat degradation by bottom trawlers and by-catch (Collie et al. 2000; O'Dea & Haedrich 2000). Off Newfoundland, where Atlantic wolffish reach their highest density in Canadian waters, population declines of 91% have been documented from approximately 1978 to 1994 (O'Dea & Haedrich 2000). Atlantic wolffish have been assessed as "special concern" by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC 2000) and listed under the Canadian Species at Risk Act. Given recent declines in Atlantic wolffish, genetic data were used to test for evidence of a recent genetic bottleneck as well as to assess effective population size (Ne), which has implications for inbreeding and the future adaptive potential of species (Frankham 2005; Willi et al.

2006). Effective population size to census size ratios (Ne:N) are of particular interest in marine fish populations as Ne and N have been shown to be orders of magnitude apart, suggesting that even species with large census sizes may still be vulnerable to depletion of genetic resources (Turner et al. 2002; Hauser et al. 2002). Finally, population structure is often influenced by both contemporary and historical factors, and one potential historical influence is glaciation. During the most

71 recent glaciation, ice reached its maximum extent approximately 17-18,000 years ago. Ice sheets reached as far south as 40° N (the mid-Atlantic region of the U.S.) in the western Atlantic and 50° N in the eastern Atlantic, extending well into the marine environment (CLIMAP 1976). The combination of the greater extent of ice sheets in the western Atlantic compared to the eastern Atlantic, as well as more compressed isotherms in this region (CLIMAP 1976), likely made the western Atlantic a much less hospitable environment for many marine species (Wares & Cunningham 2001). The third objective was, therefore, to evaluate whether or not wolffish were affected by the most recent glaciation by assessing historical population size change across the range. I evaluated genetic variation using both microsatellites and amplified fragment length polymorphism (AFLP) (Vos et al. 1995). Microsatellites are commonly used to assess population structure and are generally thought to be a reliable and neutral indicator of genetic variation. However, the coverage of the genome tends to be limited for nonmodel organisms. Potential bias exists in the selection of microsatellite loci (Ellegren 2000), and a recent study suggested that microsatellites may not be representative of genome-wide diversity (Vali et al. 2008). AFLP analysis surveys a larger number of loci, is not likely to be subject to bias, and has the advantage of being more suited to a genome scan which has been used successfully to identify candidate loci under selection (or linked to loci under selection) (Wilding et al. 2001; Campbell & Bernatchez 2004). I focused mainly on microsatellite analyses in this study, and assessed AFLP on a subset of samples, both to compare to microsatellite results and to potentially identify candidate loci under selection. I was mainly interested in evaluating population structure with neutral loci, but the identification of loci under selection is also important both because it can help to identify population structure not otherwise detectable, and it can illuminate genetic variation and adaptive processes worth conserving.

5.3 Materials & Methods

5.3.1 Sample and Data Collection Samples of fin clips or muscle were collected from across the range of Atlantic wolffish in the North Atlantic Ocean from 2002-2006 (Table 16a, Figure 13). Samples were collected with trawl nets or long-lines during research surveys, with the exception

72 of the North Sea sample, which was collected from a fish market in Aberdeen, Scotland (Arran McPherson, DFO, personal communication). DNA was extracted following either a glassmilk extraction protocol (Elphinstone et al. 2003) modified for a 96-well plate or with QIA DNeasy extraction kits (QIAGEN). A subset of randomly chosen individuals from five populations was assessed with AFLP (Table 16b).

Table 16a. Sample sizes and locations for microsatellite analysis. Note that three samples have temporal replicates. Location NAFO/ ICES Years n A Scotian Shelf 4VWX 2002 75 4VWX 2004 79 B Southern Gulf of St. Lawrence 4TVn 2002, 2004 64 C Northern Gulf of St. Lawrence 4RS 2004 63 D Southern Newfoundland 30P 2002,2003 74 E SE Grand Banks 3N 2001-2003 64 F NE Grand Banks 3L 2001-2003 68 G West Greenland 1ABCDE 2004 83 H East Greenland XlVb 2004 44 I Iceland Va 2002 96 Va 2004 94 J Spitsbergen IIa2 2004 34 K Barents Sea IIa2 2004,2005 111 L North Sea IVb 2002, 2004 66 M Rockall Bank VIb2 2005 34 VIb2 2006 75 Total 1,124

A total of 16 microsatellite loci were assayed using PCR protocols given in Chapter 4. Microsatellite alleles were visualized using two DNA imaging systems, LI- COR DNA analyzers (LI-COR Biosciences) and an FMBIO II (MiraiBio) scanner. AFLP analysis was performed according to Vos et al. (1995), as modified by Agresti et al. (2000), using Xba\ rather than EcoRX as the rare cutter. Selective amplification was performed with all combinations of EcoRl-AGA, -AGC, -ATA, -ATC and Xba-GGA, - GGC, -GT, -GC, resulting in 16 primer combinations. PCR products were imaged using 6% Sequagel on a LI-COR system. Gels were run for approximately six hours under standard conditions (1500v, 35-45 mA, 45 watt), and bands were scored up to approximately 300 b using Saga2 software (LI-COR Biosciences). For both

73 microsatellites and AFLP, negative controls and duplicates were run on each gel to ensure that samples were scored appropriately.

Table 16b. Sample sizes and locations for AFLP analysis. AFLP Locations NAFO/ ICES Years A Scotian Shelf 4VWX 2002,2004 33 E,F Eastern Grand Banks 3LN 2001-2003 34 I Iceland Va 2002, 2004 54 J Spitsbergen IIa2 2004 33 M Rockall Bank VIb2 2006 33 187

5.3.2 Analytical Methods

S.3.2J Microsatellites All loci were assessed for null alleles, amplification stutter, or large allele drop out using Microchecker v 2.2.1 (Van Oosterhout et al. 2004b). Natural selection was evaluated for microsatellites with FDist2, which infers selection from outliers in FST distributions (Beaumont & Nichols 1996). Observed and expected heterozygosity, and number of alleles were evaluated using Microsatellite Toolkit (Park 2001). Hardy- Weinberg equilibrium (HWE), linkage equilibrium, and FST were assessed with FSTAT v

2.9.3.2 (GOUDET 2001), and standardized FST values were assessed using Recode (Meirmans 2006). Significance of population differentiation was evaluated with contingency tests on allele frequencies, which may detect departures from panmixia more reliably than assignment tests or 0 p-values (Waples & Gaggiotti 2006), using the program TFPGA (Miller 1997). MDS plots were created in SYSTAT v.l 1 based on FST. An assignment test was performed using the Bayesian method (Rannala & Mountain 1997) in GeneClass2 (Piry et al. 2004), and a Bayesian spatial clustering analysis was performed in BAPS v.5 (Corander et al. 2008). Isolation by distance (IBD) was evaluated with a Mantel test using the program IBD (Bohonak 2002). For the complete microsatellite dataset, IBD was evaluated by comparing FST/(1-FST) to both straight-line distance (the shortest marine distance between two points) and downstream current distance (the shortest distance between two points following downstream currents) (App. 11). The one-dimensional migration model

74 was considered appropriate as the width of habitat was small relative to the scale at which differentiation occurred (Rousset 1997). Following Palumbi et al. (1997), I tested whether or not downstream current distance explained significantly more genetic variation than straight-line distance. Residuals from a regression of downstream current distance on straight-line distance were plotted against residuals from a regression of

FST/(1-^ST) on straight-line distance. A positive relationship would suggest that samples that were further apart by downstream current distance than by straight-line distance also had higher FST values than expected based on straight-line distance. Mean dispersal distance per generation was evaluated using the slope of IBD given by the reduced major axis regression analysis in the program IBD. Specifically, the

IBD slope is equal to effective density, D (Ne/length of habitat) multiplied by the variance in parent-offspring distance, o (Rousset 1997). The mean dispersal distance is estimated by the square root of a 12 (Buonaccorsi et al. 2004). The probability associated with specific dispersal distances was evaluated using the Chebyshev inequality, which states that the probability of migrating a distance of A: standard deviations is no greater than 1/(A?) (Buonaccorsi et al. 2004). Historical demography was evaluated using the program Bottleneck (Cornuet & Luikart 1996) which assesses heterozygosity with respect to the observed number of alleles. Heterozygosity excess suggests that the population is not at mutation-drift equilibrium and that rare alleles have been removed from the population, which is indicative of a bottleneck. Bottlenecks were evaluated using the two-phase model of mutation (TPM), which was shown to be the most appropriate model for microsatellite evolution in a recent review (Ellegren 2004). Historical population size change was evaluated with k-g tests (Reich & Goldstein 1998; Reich et al. 1999), recently developed for use in Excel (Bilgin 2007). The within-locus k test assesses the distribution of allele lengths to determine if it is more typical of a constant or growing population. Populations of constant size are characterized by discrete peaks, whereas growing populations tend to have one smooth peak because most alleles have descended from the time of expansion. The interlocus g test assesses whether or not the alleles across loci coalesce to approximately the same time period, which would further support the expansion hypothesis (Reich & Goldstein 1998).

75 Finally, effective population size, Ne, was evaluated with both a long-term, heterozygosity-based method, ThetaF, (Xu & Fu 2004), and a linkage disequilibrium method, LDNE (Waples & Do 2008). For the first method, Ne was estimated using the equation 0F=4Nen. for diploid loci. LDNE estimates Ne based on both random mating and monogamy, accounting for bias that occurs when sample size is less than Ne (Waples 2006). Mutation rates for microsatellite loci are thought to be between 10"3 to 10"5 per generation although no empirical data are available for mutation rates in teleosts (Bagley 4 et al. 1999). An intermediate mutation rate of 10" was used in this analysis. An Ne:N ratio was calculated where census data were available such as off Newfoundland (Simpson & Kulka 2002) and West Greenland (Riget & Messtorff 1988b; Ratz & Stransky 2005; Storr-Paulsen & Jorgensen 2005). For the Newfoundland estimate, long- term Ne for the eastern Grand Banks was compared to census size from the eastern Grand Banks from 1977-1994 (NAFO Regions 3LN) (Simpson & Kulka 2002). Atlantic wolffish are thought to be reproductively mature at 50 cm (O'Dea & Haedrich 2000), and surveys caught fish roughly from 10-70 cm, although the peak catch was usually approximately 50 cm (sometimes from 40-50 cm). Clearly a substantial portion of the catch was juvenile fish, but the exact proportion of adults was not given. Therefore, adult census sizes were estimated using a harmonic mean of abundance over the study period for each survey (Nunney & Elam 1994), dividing by various factors to account for the adult portion of the catch (see Results).

5.3.2MAFLP To assess data quality for the AFLP analysis, 21 samples (or 11% of total) were run as blind replicates and error rates per locus were determined by dividing the number of mismatches between blind replicates by the number of replicates, as recommended (Bonin et al. 2004; Pompanon et al. 2005). The final error rate calculation was based on data that had been "cleaned" as suggested by Bonin et al. (2004), by eliminating problematic loci and samples. AFLP data were evaluated with genome scans to identify candidate loci under selection using two programs, DFDist_c (Beaumont & Balding 2004), and BayeScan (Foil & Gaggiotti 2008). Outliers were removed before evaluating heterozygosity and

76 FST with AFLP-SURV (Vekemans 2002), using both band-based and allele frequency- based (Bayesian) methods. Microsatellites genotyped on the same individuals were also assessed for heteroyzgosity and FST in order to directly compare the two marker types. Significance of pair-wise FST values for both marker types were evaluated with TFPGA (Miller 1997), which uses marker frequencies rather than allele frequencies for dominant data. IBD analyses were performed as described above with downstream geographic distances. A Bayesian spatial clustering analysis was performed in BAPS v.5 (Corander et al. 2008) on AFLP and the comparable microsatellite dataset to determine if population structure was detectable with either marker. Assignment tests were performed with AFLPOP (Duchesne & Bernatchez 2002) on all AFLP loci, and with GeneClass2 (Piry et al. 2004) for microsatellites.

5.4 RESULTS

5.4.1 Microsatellite Characteristics Of 16 loci amplified in Atlantic wolffish, two loci, Alul and Alu30, showed evidence of departure from HWE as well as null alleles or stutter in multiple populations, and were removed from the data set. There were no significant departures from HWE, after Bonferroni correction for either number of samples or loci (Rice 1989) among the remaining 14 loci. Linkage disequilibrium was significant even after correcting for multiple tests forAlu25 mdAlu27 in the Barents Sea population. However, as main results remained largely unchanged regardless of whether one or both loci were included in analyses, both loci were retained in the analysis. No evidence was found for selection using the program FDist2. The number of alleles and average heterozygosity per locus were 6-25 and 0.08- 0.93, respectively (App. 12). Average heterozygosity per population across 14 loci ranged from 0.66-0.74 (Figure 14, App. 12). When heterozygosity and allelic richness were plotted for all populations using longitude as a proxy for sample location, a trend emerged of high to low genetic diversity from eastern to western Atlantic samples (Figure 14). Atlantic Canadian samples, particularly southern Gulf of St. Lawrence, Scotian Shelf, and southern Newfoundland, showed lower genetic diversity than samples throughout the rest of the range (Figure 14). Scotian Shelf, Gulf of St. Lawrence, and

77 southern Newfoundland samples will be referred to as western Atlantic Canada for the rest of this paper, and SE and NE Grand Banks samples will be referred to as eastern Grand Banks.

5.4.2 Population Structure and Dispersal Significant population structure was found across the range of Atlantic wolffish, which generally reflected four distinct groups: western Atlantic Canada, eastern Grand Banks, North Atlantic, and Rockall (Table 17, Figure 15). Significant differentiation was found among all groups, with the highest FST values found between Rockall and western Atlantic Canada (FST=0.035) (Table 17). The North Atlantic group showed very low within-group differentiation (FST=-0.002-0.004) across thousands of kilometers, with no significant pair-wise differences after a Bonferroni correction (Table 17). MDS plots based on FST clearly separated North Atlantic from western Atlantic Canadian samples, with eastern Grand Banks being intermediate (Figure 15). Rockall samples were also clearly distinct (Figure 15). An assignment test reinforced this pattern of eastern Grand Banks being intermediate between western Atlantic Canada and the North Atlantic, with Rockall again being distinct (Figure 16). A spatial clustering analysis done in BAPS confirmed the Atlantic Canada-North Atlantic division, but was apparently not sensitive enough to detect the distinctness of Rockall (Figure 17). The fact that BAPS grouped the northeastern Grand Banks with the North Atlantic group and the southeastern Grand Banks with Atlantic Canada highlighted the transitional nature of the eastern Grand Banks.

Although the predominant pattern was of regional groups, some structure within both Atlantic Canada and the North Atlantic was apparent. In Atlantic Canada, apart from eastern Grand Banks, northern Gulf and southern Gulf samples were significantly different from one another (Table 17), with the northern Gulf samples showing a greater affinity for the eastern Grand Banks than other Atlantic Canadian samples in an MDS plot (Figure 18). However, southern Gulf of St. Lawrence, Scotian Shelf, and southern Newfoundland were not significantly different from one another (Table 17). MDS plots of North Atlantic samples without Rockall revealed regional clustering (Figure 18), despite the lack of significant differences after Bonferroni corrections (Table 17).

78 wolffis h s ; Tab l A-02 A-04 B C D E F G H 1-02 1-04 J K L M-05 M-06

A-02 0.0589 0.1066 0.0438 0.7387 0.0017 <.0001 <0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 A-04 0.003 0.1682 0.0001 0.5374 <0001 <.0001 <.0001 <.0001 <0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 i 3 B 0.001 0.002 <.0001 0.4519 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <0001 <.0001 <.0001 <.0001 g 1 8 C 0.004 0.006 0.010 0.0001 0.0009 <.0001 <.0001 <.0001 <.0001 <.0001 <0001 <.0001 <.0001 <.0001 <.0001 M ^ D -0.002 0.000 0.001 0.005 0.0026 <.0001 <.0001 <.0001 <0001 <.0001 <0001 <.0001 <.0001 <.0001 <.0001 1" I r- E 0.003 0.009 0.011 0.007 0.004 0.1926 <.0001 <.0OOl <.0001 <0001 0.001 <.0001 <0001 <.0001 <0001 i F 0.007 0.010 0.018 0.008 0.007 0.000 0.0001 <.0001 <.0001 <0001 <.0001 <.0001 <.0001 <.0001 <.0001 I 0.022 0.024 0.031 0.016 0.022 0.009 0.006 0.4265 0.1305 0.1929 0.1656 0.0660 0.0012 <.0001 <.0001 G 1 a- 0.019 0.025 0.032 0.019 0.021 0.010 0.007 0.000 0.7143 0.6933 0.4989 0.5500 0.4339 0.0002 <.0001 H 1 0.019 0.023 0.029 0.016 0.021 0.008 0.008 0.000 -0.001 0.9397 0.3340 0.1777 0.0030 <.0001 <0001 1 1-02 0.017 0.022 0.027 0.015 0.020 0.007 0.007 0.001 -0.001 -0.001 0.1455 0.2521 0.0509 0.0004 <.0001 1-04 0.020 0.026 0.031 0.014 0.020 0.008 0.009 0.000 -0.001 0.002 0.004 0.4544 0.3693 0.0030 <.0001 l J 1 0.020 0.024 0.030 0.015 0.021 0.009 0.008 0.001 0.000 0.002 0.001 -0.002 0.0311 <.0001 <.0001 K I 0.018 0.023 0.030 0.012 0.020 0.011 0.010 0.004 0.001 0.002 0.001 0.000 -0.001 <.0001 <0001 L 0.026 0.031 0.035 0.025 0.028 0.016 0.019 0.005 0.005 0.007 0.004 0.005 0.007 0.007 0.6770 M-05 0.028 0.033 0.034 0.027 0.032 0.022 0.022 0.012 0.011 0.012 0.008 0.014 0.014 0.011 0.000 M-06- ? 1£• generation if effective density was 1 fish per km (Table 18). For a given effective density, the average dispersal distance across the North Atlantic was about three to four times higher than in Atlantic Canada (Table 18). However, if effective densities are larger in the North Atlantic, dispersal distances may be more similar between the two regions. The Chebyshev inequality distances were calculated for Atlantic wolffish downstream current distance across the whole range (as that was intermediate between Atlantic Canada and North Atlantic dispersal estimates) based on an effective density of 10 individuals per km. Results indicated that -75% of the population would disperse <180 km per generation, and -90% would disperse <275 km.

Table 18. Mean dispersal distance (MDD) estimates for Atlantic wolffish based on Rousset's estimates of FST values. AFLP outliers were removed before analysis. Dispersal estimates were not calculated when IBD was not significant. MDD (km) r p-value slope D=\ £>=10 D=100 Across range ds curr distance 0.71 O.001 2.95 x 10"6 206 65 21 straight distance 0.61 <0.001 6.97 xlO"6 134 42 14

Atlantic Canada ds curr distance (with Grand Banks) 0.70 <0.004 1.07 xlO"5 108 34 11 straight distance (with Grand Banks) 0.67 <0.003 1.60 xlO"5 88 28 9 straight distance (without Grand Banks) 0.05 <0.471 2.08 x 10"5

North Atlantic ds curr distance 0.67 <0.001 7.51 x 10"7 408 129 41 straight distance 0.43 <0.017 1.39 xlO"6 300 95 30

AFLP versus micro-satellites Across range using downstream current distance AFLP (allele frequency) 0.48 <0.072 2.78 x 10"6 AFLP (band based) 0.68 O.012 5.63 x 10"6 149 47 15 microsatellites (of AFLP samples) 0.82 <0.007 2.02 x 10"6 248 79 25

5.4.3 Effective Population Size and Historical Demography No evidence of a bottleneck was found, but evidence for a population expansion was found in the k test. Though the g test was not significant, it is known to have

80 reduced power when mutation rates vary greatly across loci (Reich & Goldstein 1998). This may have been the case for these data which included di-, di-tetra, and tetranucleotide loci.

Ne estimates across significantly different regional groups ranged from 7,900 for western Atlantic Canada and 12,300 for the North Atlantic (Table 19). Ne estimates based on linkage disequilibrium for the North Atlantic were far greater than those calculated for any other region (not surprisingly given its large area), although estimates for the eastern Grand Banks were negative or infinity, indicating that any disequilibrium found could be explained by sampling error (Table 19). Results were reported for random mating, but if monogamy is more appropriate, they should essentially be doubled. Ne estimates varied substantially for the North Atlantic group depending on whether minimum allele frequencies of 0.05 or 0.01 were used (Table 19), possibly due to an upward bias when more alleles were used (Waples & Do 2008), although other locations were not similarly affected. Despite the apparent similarity between ThetaF and LDNE results, the upper bound of infinity (with the LDNE method) makes these estimates difficult to interpret in any meaningful way. The assumptions of LDNE, which may not have been met in wolffish, further complicated the interpretation of results. LDNE was designed for closed populations, and IBD patterns found in wolffish make clear-cut population identification difficult. Secondly, the assumption of discrete generations was not met in wolffish, although results can be interpreted as Nb rather than

Ne (Waples 2006; Waples & Do 2008). A harmonic mean of census size in Newfoundland waters was estimated at 11.9 million, with eastern Grand Banks accounting for 1.7 million (Simpson & Kulka 2002). This estimate was divided by 2, 10, and 100 to account for the adult portion of the populations (depending on distribution of the size offish caught), which resulted in Ne:N estimates ranging from 1.2 x 10"2 to 6.0 x 10"1 (Table 20). For West Greenland, the long- term Ne for West Greenland was compared to census size estimates ranging from 2-15 million fish (Riget & Messtorff 1988a; Ratz & Stransky 2005; Storr-Paulsen & Jorgensen 2005). These surveys typically caught smaller size classes than the Newfoundland surveys, therefore dividing by 10 is probably more appropriate than dividing by two. 3 1 Nevertheless, Ne:N estimates ranged from 1.5 x 10" to 5.6 x 10" (Table 20).

81 Table 19. Effective population sizes on a regional and local scale across the range of Atlantic wolffish based on ThetaF (left) and LDNE (right). LDNe estimates were based on the random mating model with minimum allele frequencies of 0.05 and 0.01, and the jacknife approach was used for confidence intervals. 0.05 0.01 Regions n Ne lower upper Ne lower upper Ne lower upper western Atlantic Canada 346 7,886 5,599 11,527 3,141 914 OO 4,002 1,326 00 eastern Grand Banks 132 10,282 7,613 14,332 CO 536 co oo 1,198 oo North Atlantic 528 12,261 8,878 17,625 5,736 1,485 co 61,274 4,391 co Rockall Bank 109 11,797 8,746 16,447 oo 445 oo 6,417 736 co Local-scale

Scotian Shelf-2002 75 7,425 5,115 11,248 623 172 CO 477 206 00

Scotian Shelf-2004 79 7,599 5,610 10,585 397 149 CO CO 497 00 S Gulf of St. Lawrence 64 6,461 4,516 9,594 oo 249 co oo 2,441 oo

N Gulf of St. Lawrence 63 8,491 5,998 12,510 691 171 00 1,799 298 CO Southern Newfoundland 74 7,822 5,510 11,542 oo 346 oo 602 272 co SE Grand Banks 64 9,695 7,153 13,569 4,565 181 oo 823 248 oo NE Grand Banks 68 10,383 7,703 14,433 oo 331 co oo 1,552 oo

West Greenland 83 11,324 8,298 16,029 2,179 259 00 00 782 CO

East Greenland 44 11,479 8,101 17,023 180 87 00 CO 365 00 Iceland-2002 96 11,660 8,712 16,100 oo 425 oo oo 821 oo Iceland-2004 94 11,542 8,400 16,448 1,187 252 co oo 907 oo Spitsbergen 34 10,998 7,742 16,372 oo 268 co co 221 co Barents Sea 111 12,465 8,882 18,280 13,462 403 oo co 1,238 co North Sea 66 12,574 8,985 18,385 694 191 oo 402 204 4,155 Rockall Bank-2005 34 9,859 7,145 14,124 61 39 120 145 83 448

Rockall Bank-2006 75 12,205 9,111 16,882 00 358 00 00 605 00

5.4.4 AFLP Characteristics An initial error rate was estimated at 5.8%, which dropped to 3% after removing problematic loci and samples. Of the 172 loci scored, both genome scans (DFDist, BayeScan) identified two candidate loci for selection (locus 118 and 160) with high probability (p>0.99). No scoring errors were identified for either of these two candidate loci. One locus indicated an unusual band at high frequency on the Scotian Shelf and another indicated an unusual band in both Iceland and Rockall samples. In both cases, the unusual band was virtually absent from other samples, particularly those from the opposite coast.

82 Table 20. Ne:N ratios based on harmonic means of census size in Newfoundland and West Greenland waters. proportion Location Abundance data N N Ne:N e of adults XT e JI J Simpsor n and Kulka, , _ , , 2 Newfoundland -___ 10.3 x 1 0A 3 1.7 xlO6 1/2 1.2 xlO"

1/10 6.0 x 10"2 1/100 6.0 xlO"1 Storr-Paulsen and West Greenland 11.0x10' 2.0 xlO6 1/2 1.1 xlO-2 Jargensen, 2005 1/10 5.6 xlO"2 1/100 5.6x10"' Ratz and Stransky, West Greenland ll.OxlO3 11.6xl06 1/2 2.0 x 10"3 2005 1/10 9.8 x 10"3 1/100 9.8 x 10-2 Ri etand Wes•ar t• Greenlan/- i dA S° Messtorff.„„„ , ..11.0x1. ,„03 14.9 xlO6 1/2 1.5 xlO"3

1/10 7.6 xlO"3 1/100 7.6 xlO-2

Expected heterozygosity was approximately 0.2 across all five populations (App. 13), which was much lower than for microsatellites, and heterozygosity values for the two markers were not correlated. After removal of the two candidate loci, and using those samples for which data were available for both markers, FST and IBD were fairly comparable between AFLP and microsatellites, particularly using the band-based method (Tables 18, 21). Although IBD trends were positive for both band-based and allele- frequency based approaches, IBD was only significant using the band-based approach (Table 18). BAPS (v. 5) was unable to detect structure in this limited number of samples for either marker type. Assignment tests (GeneClass and AFLPOP), however, produced higher self-assignment values for AFLP than for microsatellites (Figure 20). Self- assignment probabilities were particularly high for Nova Scotia and Rockall with both microsatellites and AFLP (Figure 20).

83 Table 21. FST values (below diagonal) for AFLP data using band-based (upper) and allele frequence-based (middle) methods, as well as for microsatellite data (lower) for the same individuals. P-values (above diagonal) are based on contingeny tests using TFPGA. Significant results from permutation tests for AFLP data using AFLP-SURV are also presented (p<0.05 in bold). AFLP analyses were performed without the two outlier loci. AFLP (band based) A E,F I J M Scotian Shelf 0.999 0.002 0.251 <0.001 Eastern Grand Banks 0.028 0.146 0.994 0.614 Iceland 0.047 0.025 0.007 0.034 Spitsbergen 0.050 0.017 0.024 0.873 Rockall Bank 0.076 0.032 0.031 0.021

AFLP (allele frequency) A E,F I J M Scotian Shelf Eastern Grand Banks 0.017 Iceland 0.024 0.022 Spitsbergen 0.027 0.011 0.022 Rockall Bank 0.046 0.018 0.024 0.025

Microsatellites A E,F I J M Scotian Shelf 0.002 O.001 O.001 <0.001 Eastern Grand Banks 0.005 O.001 0.007 <0.001 Iceland 0.014 0.006 0.641 <0.001 Spitsbergen 0.021 0.008 0.003 O.001 Rockall Bank 0.027 0.026 0.009 0.014

5.5 DISCUSSION

5.5.1 Population Structure Across the Range of Atlantic Wolffish Contrary to expectations based on life history attributes, FST values for Atlantic wolffish evaluated with microsatellites were not unusually high for a marine fish. For example, Atlantic cod (Gadus morhud), a well-studied demersal species with pelagic eggs and larvae, has ocean-wide levels of differentiation comparable to those in Atlantic wolffish (up to 0.037) (Bentzen et al. 1996; Pogson et al. 2001; Hutchinson et al. 2001; O'Leary et al. 2007) and similar if not higher levels on a bay scale (Ruzzante et al. 1998). Likewise, Atlantic herring (Clupea harengus), with demersal eggs and pelagic larvae also have similar FST values (0.029) across the North Atlantic (McPherson et al. 2004).

84 Despite lower than expected FST values, however, regional structure was apparent across the range of Atlantic wolffish. One of the most striking findings was the presence of four genetically distinguishable population groups, western Atlantic Canada, eastern Grand Banks, North Atlantic, and Rockall, occurring over widely varying spatial scales. Notably, the FST values between western Atlantic Canada and the eastern Grand Banks demonstrated greater differentiation over less than 500 km than was found across 4,000 km in the North Atlantic (Table 17). The genetic pattern found in Atlantic wolffish was surprisingly similar to that found in several other species in the North Atlantic Ocean. The Atlantic cod Scotian Shelf population was found to be distinct from a relatively homogeneous North Atlantic group (from West Greenland to the Barents Sea) as well as from the Baltic Sea population (O'Leary et al. 2007). The western Atlantic may have provided a refuge for Atlantic cod during the last glaciation (Bigg et al. 2008; Carr & Marshall 2008), which may have contributed to the genetic distinctness of this region. Redfish (Sebastes mentella), interestingly, also showed a similar pattern to Atlantic wolffish in that samples from the Gulf of St. Lawrence and south of Newfoundland (comparable to western Atlantic Canada samples) formed one group, distinct from a Panoceanic group that included the Grand Banks (comparable to eastern Grand Banks) (Roques et al. 2002). However, Roques et al. (2002) attributed this pattern to introgression with S. fasciatus, which had previously been described in those same waters, as well as circulation patterns within the Gulf of St. Lawrence (Roques et al. 2001). Roques et al. (2002) also pointed to the role of ocean currents in the North Atlantic to explain the relative homogeneity across the Panoceanic group. Finally, the phylogeographic pattern found in capelin (Mallotus villosus) also revealed a genetic break marked by the Labrador Sea (Dodson et al. 2007). In the case of capelin, monophyletic groups were found on either side suggesting that divergence dated back to a much earlier vicariant event than with wolffish. Nevertheless, these studies point to the Grand Banks and the Labrador Sea as possible historical boundaries for widely distributed populations. More studies of marine fishes with similar trans-Atlantic distributions are needed to determine whether such genetic discontinuities are typical of this region at the extreme eastern limits of the North American continental shelf.

85 Isolation by distance was found both across the species range and within regional groups, indicating limited gene flow. Although IBD was found to be significant across the range of wolffish, the pattern was not typical of a species at equilibrium (Hutchison & Templeton 1999). Equilibrium is characterized by a monotonic increase in genetic distance with geographic distance along with an increase in genetic variance among samples as genetic drift increases in importance relative to gene flow (Hutchison & Templeton 1999). IBD graphs with regional groups highlighted (particularly FST values between Atlantic Canada and the North Atlantic samples, Figure 19) indicated that genetic variation across the range of wolffish was probably better explained by regional structure than an overall IBD. Within regions, IBD patterns revealed some interesting trends. Estimates of IBD slope in Atlantic Canada (both with and without eastern Grand Banks) were substantially steeper than what was found across the North Atlantic, although the trend was not significant without eastern Grand Banks) (Table 18).

Effective population sizes may have influenced these estimates, as Ne is likely larger in the North Atlantic compared to Atlantic Canada (Table 19). The second finding was that, across the North Atlantic, downstream current distance explained variation significantly better than did straight-line distance.

The accuracy of dispersal distance estimates clearly depends on Ne, which raises two questions with regard to Ne estimates in this study. One is the relationship between long-term and current Ne, and the other is the appropriate geographic scale for estimating long-term Ne. Current Ne is typically larger than long-term Ne, particularly with evidence of population expansion, as is the case for wolffish. Thus, long-term Ne should be viewed as a lower bound for current Ne, and is likely a conservative one. Secondly, although estimating a single Ne for the whole of the North Atlantic may be appropriate from the standpoint of evolutionary independence (e.g. Ne of 12,300, Table 20), this is arguably too conservative for dispersal distance estimation. We, therefore, estimated effective density regional groups), which resulted in estimates of-10 individuals per kilometer of continental shelf in a variety of locations (i.e. eastern Grand Banks, West Greenland, Iceland, and Barents Sea). As such, mean dispersal distance estimates were 28-34 km per generation for Atlantic Canada, depending on which populations were included, and 95-129 km for the North Atlantic (depending on whether straight-line or

86 downstream current distance was used) (Table 18). If effective density is actually higher, average dispersal distances would be lower. 5 Estimates of Ne:N ratios for other marine fishes ranging from 10'MO" have been reported based on current or in some cases long-term Ne (Turner et al. 2002; Hauser et al.

2002; Hutchinson et al. 2003; Hoarau et al. 2005; Poulsen et al. 2006). Ne:N ratios from 1 3 wolffish based on long-term Ne were generally in the range of 10" to 10" , which may reflect low fecundity, single pair mating, parental care and general K-strategist attributes of wolffish. Ratios of 10" or lower may be typical for fishes with high fecundity and high variance in individual reproductive success, but wolffish appear to have ratios more similar to salmon (104-10"2) (Bartley et al. 1992; Frankham 1995; Waples 2004).

5.5.2 The Role of Contemporary vs. Historical Factors Dispersal ability should be correlated with gene flow in theory, and case studies have largely found this to be true (Waples 1987; Shaklee & Bentzen 1998; Bohonak 1999). However, when population structure has differed from expectations based on perceived dispersal potential, authors have largely invoked longer-scale processes, such as glaciation and historical sea level changes (Planes et al. 1993; Shulman & Bermingham 1995). A recent history of glaciation, periodic changes in abundance due to short-term climate changes, sweepstakes recruitment, and natural selection have all been put forward as explanations for weak population structure in marine fishes (Grant & Bowen 1998). No evidence for selection was found for microsatellites in this study, and the life history of Atlantic wolffish does not suggest that this species would be particularly susceptible to sweepstakes recruitment. Atlantic wolffish are thought to form monogamous mating pairs so mating success is not likely to be extremely skewed towards a few individuals with a disproportionate number of mates. Furthermore, because the Atlantic wolffish is a long-lived deep-water species, it is not expected to be subject to the same natural fluctuations as more short-lived pelagic species. However, given the recent history of glaciation, the influence of recent colonization on population structure is a possibility. In a recent study of mtDNA, Atlantic wolffish were hypothesized to have survived glaciation in the eastern Atlantic and subsequently (re)colonized the western

87 Atlantic, although the validity of this hypothesis depends on the estimated mutation rate (Chapter 3). The subtle peak in microsatellite diversity at approximately 0° longitude is consistent with the possibility of a single refuge in this general region (Figure 14). Furthermore, evidence of population expansion from k tests suggests genetic variation at microsatellite loci may still show a signal of post-glacial expansion and may not yet be in equilibrium. The evidence of population structure across the Grand Banks is an especially intriguing result as it shows that genetic differentiation occurs on a (relatively) fine geographic scale in the absence of any obvious barriers to dispersal and across an area where wolffish are more or less continuously distributed. Certainly, geographic barriers are not required for restricted dispersal, as homing to natal habitat has been documented in fishes (Jones et al. 1999; Almany et al. 2007). Nonetheless, the apparent restricted dispersal on the Grand Banks presents a conundrum. Either wolffish disperse less on the Grand Banks than anywhere else on the continental shelves (excluding Rockall because of its obvious physical isolation), or wolffish dispersal on the Grand Banks is typical (and low), and historical effects or differences in effective densities across the two regions limit our ability to detect low dispersal elsewhere. As no geographic barriers appear to exist across the Grand Banks, and nor is there reason to suggest homing should be greater in this region, I suggest that the latter scenario is more likely. One possible explanation for the striking genetic distinctness of Atlantic Canada is that the western Atlantic was a refuge for wolffish during the last glaciation. Although the lack of fixed mutational differences across ~2 kbps analyzed between the eastern and western Atlantic (in addition to other analyses) suggests recent divergence, if the mutation rate is very slow in these species, the presence of two refugia during the last glaciation remains possible (see Chapter 3). Alternatively, another possibility is that the estimated mutation rates are accurate, and that a single refuge existed in the eastern Atlantic during the last glaciation. Under this scenario, the strong signal of genetic differentiation in Atlantic Canada may be due to a combination of low historical and contemporary population sizes. Post-glacial colonization of this region may have occurred in two phases, as the far eastern part of the Grand Banks was likely habitable for wolffish a few thousand years earlier than regions

88 to the west and south (Shaw et al. 2006). Ice sheets are thought to have retreated off northeastern Newfoundland by approximately 20,000 ya, followed by a period of rapid retreat just before 14,000 ya in the Laurentian channel (Shaw et al. 2006). If a small number of individuals was involved in the initial colonization of the Grand Banks, and an even smaller number rapidly colonized the remainder of Atlantic Canada, reduced heterozygosity in this region may have resulted. Wolffish are known to reach their highest densities in Atlantic Canada in Newfoundland waters, particularly in areas northeast of Newfoundland (O'Dea & Haedrich 2000; Simpson & Kulka 2002). Smaller population sizes, both historical and contemporary, are more susceptible to genetic drift, and this has likely accentuated genetic differentiation across this region (Figure 15, Table 17). It is worth noting that microsatellite heterozygosity is distinctly lower in western Atlantic Canada compared to the rest of the range (Figure 14), whereas AFLP heterozygosity is not (App. 13). I suggest that this illustrates a greater sensitivity of microsatellites as indicators of genetic diversity compared to AFLP. The interpretation of genetic data can be challenging due to the difficulty of distinguishing historical patterns from contemporary ones (Hauser & Ward 1998). I suggest that a strong historical signal still exists in the data, likely influenced by post glacial colonization patterns and historical population size differences across the range. However, this is undoubtedly overlaid by contemporary signals, such as the influence of current population size and current gene flow. Population structure across Atlantic Canada coincides with certain oceanographic and temperature features of the area, suggesting a role for contemporary factors in this region. For example, a strong temperature change (6-7°C) occurs over a short distance between the southern Grand Banks and northern Grand Banks (Powles et al. 2004). This coincides with genetic differentiation between NE Grand Bank and SE Grand Bank samples shown most dramatically in the BAPS analysis (Figure 17), although it does not account for the strong differentiation found between southern Newfoundland and SE Grand Banks samples (Figures 15-16). Secondly, the northern Gulf of St. Lawrence is characterized by relatively deep water (>400m), in contrast to the shallow waters (<100m) of the southern Gulf, southern Newfoundland, and Scotian Shelf (Powles et al. 2004). This may help to explain the genetic divergence between northern Gulf of St. Lawrence samples and

89 neighboring samples (Table 17, Figures 15, 18). Compared to Atlantic Canada, the relative absence of significant population differentiation across the North Atlantic is intriguing. Whether this is due to historical (recent colonization, relatively large population sizes) or current gene flow across the North Atlantic is unclear. Roques et al. (2002) posited that gene flow across the North Atlantic influenced by contemporary oceanic circulation could explain low levels of differentiation in the deep-water redfish, Sebastes mentella. Although I can not rule out some level of on-going gene flow across the North Atlantic, I suggest gene flow is at most sporadic and infrequent as significant IBD (Table 18) and population clustering (Figure 18) across the North Atlantic still argue for limited dispersal and largely self-sustaining populations. Finally, results from this study suggest that environmental factors such as ocean currents, water temperature, and continuity of habitat on continental shelves, have played a role in genetic differentiation in wolffish. Ocean currents across most of the North Atlantic travel along continental shelves that are presumably reasonable habitat for wolffish. The fact that IBD was significantly better when based on downstream current distances rather than straight-line distances (at a range-wide scale and across North Atlantic samples) suggests that wolffish are more likely to disperse along continental shelves rather than open ocean. The dominant current from West Greenland to Newfoundland travels north through very cold, possibly inhospitable regions, before heading south again (App. 11), which may help to explain the sharp genetic break in this region. The fact that Rockall Bank is surrounded by water greater than 1000 m deep and more than 200 km wide (Shaw et al. 1999) suggests that hydrographic isolation plays a role in the genetic differentiation found in wolffish in this region as well. The veined squid, Loligo forbesi, also shows evidence of an isolated Rockall population, as well as isolation of other oceanic island populations (Shaw et al. 1999). Continental shelf populations of veined squid from West Scotland, the English Channel and NW Spain were not significantly differentiated from one another, whereas both Rockall and Azores populations were significantly different from the shelf population in this demersal species (Shaw et al. 1999).

90 5.5.3 AFLP vs. Microsatellites The genome scan to detect loci under selection identified two candidate loci (1.2% of loci) in Atlantic wolffish. Similar numbers (-0-3%) have been identified in other AFLP genome scans on species as wide-ranging as lake whitefish, sole, and the common frog (Campbell & Bernatchez 2004; Bonin et al. 2006; Garoia et al. 2007), although 5% of loci were identified in a study of ecotypes in the snail Littorina saxatilis (Wilding et al. 2001). The modest number of loci surveyed may have limited power to detect selection, particularly if genes of small effect underlie selected traits (Storz 2005; Thornton et al. 2007). Error rates cited in this study for AFLP were similar to those reported in other AFLP studies of 2-5% (Bonin et al. 2007). Nevertheless, technical challenges in the AFLP analysis did occur, due to uneven amplification across gels and possible restriction site or PCR inhibition. The latter source of error has been cited as a potential problem with AFLP, although not a common one (Bonin et al. 2004; Bensch & Akesson 2005). As this affected certain samples at multiple loci in a predictable fashion, it was relatively easy to recognize. Nevertheless, I suspect that the microsatellite data were more reliable, although error rates were not reported due to the low number of blind samples included in the dataset. The comparison of AFLP and microsatellites was interesting for two reasons. I was encouraged that the two methods produced comparable FST, IBD, and dispersal estimates (Tables 18, 21). Perhaps more interesting, however, was the higher self- assignment probabilities and lower rates of trans-oceanic migration found with AFLP compared to microsatellites (Figure 20). AFLP has been found to differentiate among populations better than microsatellites in other species as well, notably sole (Garoia et al. 2007), Atlantic salmon (Ryynanen et al. 2007), brown trout (Sonstebo et al. 2007), and plants (Mariette et al. 2002; Gaudeul et al. 2004). An unexpected result was the difference between band-based and allele frequency-based approaches. Although some authors have argued that the allele frequency method, which derives allele frequencies assuming HWE, is more appropriate for diploid organisms (Vekemans 2002), others have simply presented the two approaches as alternatives, acknowledging that many analytical packages use the band-based approach (Bonin et al. 2007). Furthermore, the two

91 approaches have been shown to produce similar results (Bonin et al. 2007). At least one study found that the choice of which loci to include had a greater effect on results than whether band-based or allele frequency-based approaches were used (Mariette et al. 2002). In this study, results using the band-based approach were more similar to microsatellite results. FST values for AFLP using the band-based approach were significantly correlated with those based on microsatellites, whereas values based on the allele frequency-based approach were not. Furthermore, AFLPOP (a band-based assignment test) produced results that were very similar to microsatellites (Figure 20). As most studies have not reported values using both approaches, or compared results to microsatellites, I do not know if this is a typical result or not.

5.5.4 Conservation Implications The field of population genetics has become an important resource for managers of exploited and threatened populations, however it has become increasingly clear that the time scales over which managers are concerned do not always coincide with the time scales relevant to population genetic studies (Hauser & Ward 1998). The degree of genetic isolation among populations required for significant differences at neutral loci tends to be far below what a manager might consider to be a demographically independent unit (which can be as high as m~0.1) (Hauser & Ward 1998). Therefore, results from this work should be understood within an evolutionary context. Lack of significant structure among populations does not necessarily mean that populations are panmictic. I argue that the apparent homogeneity across the North Atlantic is likely at least partly due to historical factors and that FST values across the region overestimate dispersal. Nevertheless, this study has shown significant population structure in Atlantic wolffish, sometimes on a very fine geographic scale, definitively showing limited gene flow in some parts of its range if not in others. The presence of regional structure and IBD suggests that ocean trenches, geographic distance, and current patterns all play a role in limiting dispersal in this species. The other main finding of this work, with respect to conservation, is relatively low genetic diversity in Atlantic wolffish. A recent review found that typical heterozygosity and allele numbers for marine fishes are about 0.77 and 19.9, respectively,

92 0.68 and 10.8 for anadromous fishes, and 0.54 and 9.1 for freshwater fishes (DeWoody & Avise 2000). Therefore, with overall heterozygosity averages across populations of 0.71 and 10.4 alleles, Atlantic wolffish have low diversity for a marine fish, which underscores the vulnerability of this species presently listed in Canada as "special concern". Low genetic diversity in wolffish is likely related to low abundance of this species compared to many marine fishes, as well as to its recent colonization history. Low genetic diversity in Atlantic Canada, in particular, suggests that if population declines continue, Atlantic wolffish may indeed become vulnerable to consequences associated with low genetic diversity, including increased inbreeding and reduced adaptive potential (Frankham 2005; Willi et al. 2006).

93 **.

v o. ^H

M' ^

-30' Figure 13. Sample locations for Atlantic wolffish samples across the North Atlantic (see Table 16 for names of locations).

L2 9.0 0.74 H o K HO j o M Gg 8.5 0.72 H O o E o 8.0 | § 0.70 2

ffl 7.5 ^ 0.68 - A8Do 7.0 0.66 A BO 6.5 -60 -40 -20 20 Longitude

94 Table 16orFigur3fomornsampllocations). Figure 15.MDSplotofsubdividedAtlanticwolffishsamplesbasenFvalue(se ST Axis 2 -0.5 - -1.0 - 0.0 - 0.5 - 1.0 - B« A-04« -1.0 Dm • A-02 -0.5 i • C Axis 1 95 0.0 • I E u M-06« 0.5 i M-05« J H 1-02 K G %l 1-04 L T 1.0 A-02 A-04 B C D E F G H 1-02 1-04 J K L M-05 M-06 Populations Figure 16. Assignment test results for 14 microsatellites across all samples. Samples taken from various locations (x-axis) across the North Atlantic (see Table 16, Figure 13 for more on sample locations). Those individuals assigned to an Atlantic Canadian location are black, those assigned to eastern Grand Banks are medium gray, those assigned to a North Atlantic location are dark gray, and those assigned to Rockall are light gray.

96 ABCDEFGHI JKLM Figure 17. Spatial clustering analysis in BAPS across the range of Atlantic wolffish identified two clusters, shown in red and green. Polygons represent approximate sample locations (see Table 16, Figure 13 for more on sample locations), with x and y axes approximating longitude and latitude, respectively. The size of the polygon is related more to distance from other sample groups than to the size of the sampled area. The bar graph represents the change in log marginal likelihood if samples were to be grouped with the other cluster. 0.4 B*

0.2 - A-04 j A-02 * u

0.0 -

1 •0.2 - C« •0.4

•0.6

i i i -0.2 0.0 0.2 0.4 0.6 Axis 1

-1.0 -0.5 0.0 0.5 1.0 Axis 1 Figure 18. MDS plot of Atlantic Canada (upper) and of North Atlantic samples (lower) based on FST values. Rockall samples were not included (see Table 16, Figure 13 for more on sample locations).

98 fen 0.02

-i 1 1 r 2000 4000 6000 8000 10000 12000 Downstream Current Distance (km) Figure 19. FST/(1- FST) plotted against downstream current distance for all Atlantic wolffish samples. Genetic and geographic distances within and between major groups were indicated as follows: temporal replicates as well as NE Grand Banks v SE Grand Banks (dark circles); within western Atlantic Canada (white triangles); within North Atlantic (white squares), eastern Grand Banks versus all others (except Rockall) (hatched circle); North Atlantic versus Rockall (black squares); western Atlantic Canada versus North Atlantic (white circles), eastern Grand Banks versus Rockall (white stars), western Atlantic Canada versus Rockall (cross).

99 A EF I J M

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100 Chapter 6: Genetic Variation in Northern and Spotted Wolffishes (Anarhichas denticulatus and A minor) Across the North Atlantic Ocean: Low Effective Population Sizes in Two 'Threatened' Species

6.1 Abstract

Northern and spotted wolffishes are demersal, sedentary marine fishes, and both are listed as 'threatened' in Canadian waters. Microsatellites and AFLP analyses were undertaken in order to estimate population structure and effective population size in these North Atlantic species. Significant population structure and isolation by distance were documented in both species, although FST values were generally low. Both species were characterized by low diversity in microsatellite DNA for marine fishes and have low long-term effective population sizes, suggesting that they may be at risk of reduced fitness due to inbreeding and reduced adaptive potential.

6.2 Introduction

Assessing genetic variation of exploited or threatened species has become a central part of management and conservation planning. Knowledge of dispersal patterns and population structure allows managers to determine the scale necessary for monitoring programs and to design appropriate marine protected areas (Botsford et ah 2003). Furthermore, conserving the independent units (populations) that form a species has been shown to be an effective way to improve the long-term sustainability of species (Hilborn et ah 2003). Genetic variation is increasingly being recognized as a factor in the resilience (Hughes & Stachowicz 2004), recovery (Saccheri et ah 1998; Reusch et ah 2005), and adaptive responses (Frankham 2005; Willi et ah 2006) of natural populations.

As such, effective population size (Ne) has become an important parameter in the management of species at risk. The genus Anarhichas is comprised of four species of wolffishes. Atlantic, northern, and spotted wolffishes are found in the North Atlantic and Arctic oceans, and the Bering wolffish is found in the northwestern Pacific Ocean and Bering Sea (Scott & Scott 1988). All four species have become a conservation concern in Canadian waters. Northern and spotted wolffishes have been designated as "threatened" by the Committee on the Status of Endangered Wildlife in Canada, COSEWIC (COSEWIC 2001a;

101 COSEWIC 2001b). The Atlantic wolffish, the most common of the four, has been designated as "special concern"(COSEWIC 2000), and the Bering wolffish was recently changed to "data deficient" after being listed as "special concern" in 1989 (COSEWIC 2002). This paper follows an analysis of Atlantic wolffish across the North Atlantic Ocean (Chapter 5). Here I focus on the two species with the more considerable conservation concern, northern and spotted wolffishes. In Canadian waters, northern and spotted wolffishes have suffered severe population declines over the past 30-40 years, likely due to habitat degradation by bottom trawlers and by-catch (Collie et al. 2000; O'Dea & Haedrich 2001a; O'Dea & Haedrich 2001b). Off Newfoundland, where wolffishes reach their highest densities in Canadian waters, population declines of 98% for northern wolffish and 96% for spotted wolffish were documented from approximately 1978 to 1994 (O'Dea & Haedrich 2001a; O'Dea & Haedrich 2001b). Northern and spotted wolffishes are currently protected under the Canadian Species at Risk Act (SARA). Although incidental by-catch still occurs, permits are required for capture, and both species must be released when caught in Canadian waters. The distribution of northern wolffish extends from Newfoundland to Mould Bay, Northwest Territories, Canada, in the western Atlantic (and Arctic), and as far east as the Murman Coast in the eastern Atlantic (O'Dea & Haedrich 2001a). The northern wolffish is the only wolffish species (to my knowledge) to be found along the Mid-Atlantic Ridge. The distribution of spotted wolffish extends from the Scotian Shelf to West Greenland in the western Atlantic, as far east as the Barents and White seas, and south to Bergen, Norway in the eastern Atlantic (O'Dea & Haedrich 2001b). The ranges of all three species overlap extensively, although the Atlantic wolffish tends to occupy the shallowest habitat and northern wolffish the deepest (Barsukov 1959). Wolffishes are demersal, sedentary fishes that build nests on the hard substrate of continental shelves and slopes (Scott & Scott 1988). Less is known about northern and spotted wolffishes than about Atlantic wolffish, but all three are non-schooling and are typically found either alone or in pairs. Wolffish eggs are some of the largest of any marine species, up to 8 mm in diameter for northern wolffish and 6 mm for spotted wolffish (Scott & Scott 1988). Atlantic wolffish eggs (and probably those of spotted and

102 northern wolffishes) are guarded by males on the ocean bottom until hatch (Pavlov & Novikov 1993). Spotted wolffish larvae hatch at approximately 22 mm. Larvae emerge well developed and able to feed exogenously (Falk-Petersen & Hansen 2003). In artificial environments, larvae have been observed swimming to the surface after hatch, although smaller larvae with larger yolk sacs stayed on the bottom (Falk-Petersen & Hansen 2003). Observations in the wild are rare, but larvae have been observed in surface waters (Scott & Scott 1988; Ortova et al. 1990). Spotted wolffish are thought to have a brief pelagic phase (3-4 weeks) before they settle to the benthic environment at 4-6 cm in length (Foss et al. 2004). Northern wolffish are perhaps the least understood wolffish species in the Atlantic Ocean. Larvae are thought to be pelagic (Scott & Scott 1988), but observations in the natural environment are rare. The large benthic eggs, large size at hatch, and swimming ability of larvae argue against strict passive dispersal for either species. Adult movement has not been well-documented for any of the wolffish species, but it appears to be limited. Tagging studies in Newfoundland, West Greenland, and the Barents Sea have indicated that most adult migrations of northern and spotted wolffishes occur over short distances, possibly associated with spawning, although some long distance migrations were also documented. In the Barents Sea, approximately half of spotted wolffish tagged were found within 93 km of the tagging site, although long distance migrations of up to 555 km were also documented (Ostvedt 1963). Off West Greenland, migration distances were estimated for 41 fish, two-thirds of which were found within 19 km of the tagging sites up to 6.8 years after tagging (Riget & Messtorff 1988b). Only five fish (12% of recaptures) were caught more than 93 km from the tagging site. This was similar to the reports for Atlantic wolffish in Newfoundland, where most migrations were short (as little as 3-8 km after 5-7 years), although three long distance migrations were reported out of 20 recaptures (300-850 km after 3-7 years) (Templeman 1984). In Iceland, Atlantic wolffish have been documented migrating up to 740-925 km, depending on the route chosen, although no evidence of migration between East Greenland and Iceland was found (Jonsson 1982). Spotted and northern wolffishes were also tagged off Newfoundland, but recaptures were too low to be informative (three individuals of each species with precise locations, all showing short distance migrations)

103 (Templeman 1984). Nevertheless, northern wolffish are thought to be the most pelagic of the three Atlantic species as they feed on both benthic and bathypelagic invertebrates, whereas both Atlantic and spotted wolffishes feed mainly on benthic invertebrates (Scott & Scott 1988). The conservation concern facing northern and spotted wolffishes as well as their fairly unusual life history motivated this study of genetic variation. Large size of eggs and larvae and limited adult movement suggest that wolffishes may have limited dispersal and more highly structured populations compared to many marine species. Furthermore, documented declines in Canadian waters of northern and spotted wolffishes warrant a better understanding of population structure and management units. Given the conservation concern of wolffishes, I evaluated effective population sizes (Ne), as well as effective population size to census size ratios (Ne:N) in these species, where possible. 3 5 Unexpectedly small Ne:N ratios (10" to 10" ) have been documented in marine fishes (Bagley et al. 1999; Turner et al. 2002; Hauser et al. 2002), indicating that despite large abundances, marine fishes may still be susceptible to problems associated with low genetic variation. I evaluated genetic variation using two marker types, microsatellites and AFLP.

6.3 Materials & Methods

6.3.1 Sample and Data Collection Samples of fin or muscle tissue were collected from across the range of northern and spotted wolffishes in the North Atlantic Ocean. Samples were taken with trawl nets or long-lines over the period of 2002-2006 (Table 22a, Figure 21). Temporal replicates were analyzed in the Barents Sea for both northern and spotted wolffishes (2004, 2005), and samples were subdivided by area or trawl in other regions to assess potential fine- scale population structure (e.g. Atlantic Canada for northern wolffish, and West Greenland and Iceland for spotted wolffish). DNA was extracted following either a glassmilk extraction protocol (Elphinstone et al. 2003) modified for a 96-well plate or with QIA DNeasy extraction kits (QIAGEN). Fifteen microsatellite markers developed for Atlantic wolffish were used to assess genetic variation in northern and spotted wolffishes (Chapter 4). Standard PCR protocols

104 were used, and microsatellite loci were visualized using two DNA imaging systems, LI- COR DNA analyzers (LI-COR Biosciences) and an FMBIO II (MiraiBio) scanner. Negative controls and duplicates were used to ensure scoring accuracy.

Table 22a. Sample sizes and locations for northern and spotted wolffishes across the North Atlantic Ocean for microsatellite analysis. Sample location Abbreviation He NAFO/ ICES Years n northern wolffish Atlantic Canada-lower AC-L 0.58 3MNOPQ 4VX 2001-2006 35 Atlantic Canada-upper AC-U 0.58 2J3KL 2001-2006 45 East Greenland EG 0.58 XlVb 2004 42 Iceland Ice 0.60 Va 2004 29 Mid-Atlantic Ridge MAR 0.59 XII 2004 26 Barents Sea 2004 Bar-A 0.56 I, Ha, lib 2004 35 Barents Sea 2005 Bar-B 0.58 I, Ha, lib 2005 39 Total 251 spotted wolffish Atlantic Canada AC 0.51 2J 3KLN 4RS 2001-2004,2006 75 West Greenland WG 0.51 1ABCDE 2004 43 Inshore West Greenland IWG 0.49 1A 2004 35 Iceland-spring * Ice-A 0.50 Va, XlVb 2004 39 Iceland-fall-trawl-1 Ice-B 0.47 Va 2004 71 Iceland-fall-trawl-2 Ice-C 0.50 Va 2004 64 Barents Sea 2004 Bar-A 0.49 I, Ha, lib 2004 63 Barents Sea 2005 Bar-B 0.51 I, Ha, lib 2005 75 Total 465 * includes 12 samples from East Greenland

A subset of samples was analyzed with AFLP (Table 22b) according to Vos et al. (1995), as modified by Agresti et al. (2000), using Xba\ rather than EcoRX as the rare cutter. Selective amplification was performed with all combinations of £coRl-AGA, - AGC, -ATA, -ATC and Xba-GGA, -GGC, -GT, -GC, resulting in 16 primer combinations. PCR products were imaged using 6% Sequagel on a LI-COR system. Gels were run for approximately six hours under standard conditions (1500v, 35-45 mA, 45 watt), and bands were scored up to approximately 300 bp using Saga2 software (LI-COR Biosciences, Lincoln, Nebraska). Error rates were calculated for AFLP using

105 blind duplicates, dividing the number of mismatches by the number of duplicate samples, as has been recommended (Bonin et al. 2004; Pompanon et al. 2005).

Table 22b. Sample sizes and locations for northern and spotted wolffishes across the North Atlantic Ocean for AFLP analysis. Sample location Abbreviation NAFO/ ICES Years n northern wolffish Atlantic Canada AC 2J3KL 2001-2006 39 Iceland Ice Va 2004 28 Mid-Atlantic Ridge MAR XII 2004 26 Barents Sea Bar I, Ha, lib 2004 36 Total 129 spotted wolffish Atlantic Canada AC 3KL 4RS 2004,2006 33 West Greenland WG 1ABCDE 2004 36 Iceland Ice Va 2004 38 Barents Sea Bar-A I, Ha, lib 2004 37 Total 144

6.3.2 Analytical Methods Microsatellite loci were assessed for null alleles, amplification stutter, or large allele drop-out using Microchecker v 2.2.1 (Van Oosterhout et al. 2004b). The genome scan approach was used to detect candidate loci under natural selection using FDist2 (Beaumont & Nichols 1996) for microsatellites and DFdist (Beaumont & Balding 2004) for AFLP loci. Hardy-Weinberg equilibrium (HWE) and linkage equilibrium were assessed for microsatellites with FSTAT v 2.9.3.2 (GOUDET 2001). Heterozygosity and number of alleles were evaluated using Microsatellite Toolkit for microsatellites (Park 2001), and heterozygosity was evaluated for AFLP loci using AFLP-SURV (Vekemans

2002). FST was assessed with FSTAT v 2.9.3.2 (GOUDET 2001) for microsatellites, and AFLP-SURV (Vekemans 2002) for AFLP loci using both band-based and allele frequency-based methods. MDS plots were created in SYSTAT v.l 1 based on FST using microsatellites. To account for the influence of heterozygosity on microsatellite markers, standardized FST was calculated using Recode (Meirmans 2006). An additional genetic differentiation measure, D, which is not influenced by heterozygosity (Jost 2008), was estimated in order to compare values to FST- Significance of genetic differentiation was

106 evaluated with exact tests for both marker types using TFPGA (Miller 1997), in addition to the permutation test available in AFLP-SURV for AFLP loci. I also evaluated population structure with STRUCTURE 2.2 (Pritchard et al. 2000; Falush et al. 2007) after 103 burn-in and 104 iterations, and BAPS 5.2 (Corander et al. 2006) for both microsatellites and AFLP. Finally, assignment tests were carried out using GeneClass2 for microsatellites (Piry et al. 2004) using a Bayesian computation criterion (Rannala & Mountain 1997), and AFLPOP (Duchesne & Bernatchez 2002) for AFLP loci, which is specifically designed for dominant data. Assignment tests were performed with regional groupings, leaving the northern wolffish East Greenland sample out, in order to directly compare microsatellites with AFLP. Samples were grouped by region in order to ensure each region was represented only once. My thought was that over-representation of any region might increase assignment to that region simply by chance. Isolation by distance (IBD) was evaluated with a Mantel test using the program IBD (Bohonak 2002). The one-dimensional model was considered appropriate as the width of the habitat was generally much smaller than the geographic distance at which genetic differentiation occurred (Rousset 1997). The shortest marine distance between any two points was used to measure geographic distance. Mean dispersal distance was evaluated based on the slope of FST/O-^ST) over geographic distance with the program IBD for both markers, using FST values for microsatellites. According to Rousset (1997), the IBD slope is equal to \/4Da , where D is effective density (Ne/length of habitat) and CT2 is the variance in parent-offspring distance (Rousset 1997; Buonaccorsi et al. 2004). Mean dispersal distance is equal to the square root of a 12 (Buonaccorsi et al. 2004). Probabilities associated with specific dispersal distances were evaluated using the Chebyshev inequality, which states that the probability of migrating a distance of k standard deviations (CT) is no greater than l/(k2) (Buonaccorsi et al. 2004). Given the recent sharp declines in wolffishes documented in Atlantic Canada, two methods were used to assess the probability of bottlenecks with microsatellites. The program Bottleneck (Cornuet & Luikart 1996) assesses heterozygosity with respect to the observed number of alleles. A heterozygosity excess indicates that the population is not at mutation-drift equilibrium and that rare alleles have been removed from the population, suggesting a bottleneck. The two-phase model of mutation (TPM), was used

107 to evaluate bottlenecks (with 30% of mutations following the infinite allele model, and 70% following the stepwise mutation model), as recommended (Ellegren 2004). M, the ratio of the number of alleles to the range in allele size (Garza & Williamson 2001), was also evaluated for evidence of a bottleneck (Garza & Williamson 2001). Both analyses were performed for each region separately by combining samples within a region.

Long-term effective population size (Ne) was evaluated with microsatellites using the computer program ThetaF based on homozygosity and sample size (Xu & Fu 2004).

0F can be converted to Ne using the equation 0F=4Ne|a for diploid loci. A linkage disequilibrium method, LDNE (Waples & Do 2008), was also used to assess Ne using the equation 9F=4NC|X for diploid loci. LDNE estimates Ne based on both random mating and monogamy, accounting for bias that occurs when sample size is less than Ne (Waples 2006). Mutation rates for microsatellite loci are believed to be between 10"3-10"5 per generation although no empirical data are available for mutation rates in teleosts (Bagley et al. 1999). An intermediate mutation rate of 10"4 was used in this analysis. As with bottleneck analyses, Ne was evaluated across regions. As wolffishes are not of major commercial interest in most parts of their range, populations are not typically closely monitored. However, census data were available for spotted and northern wolffishes in Newfoundland waters from the Canadian Science Advisory Secretariat (CSAS) (Simpson & Kulka 2002), and for spotted wolffish in Greenland waters based on trawl surveys (Riget & Messtorff 1988b; Ratz & Stransky 2005; Storr-Paulsen & Jorgensen 2005). Abundance estimates for Newfoundland waters were summed across NAFO regions 2J3KLNO and a harmonic mean (Nunney & Elam 1994) was taken over the period of approximately 1977-1994 (although data for 3L and 30 only dated back to the early 1990s) (Simpson & Kulka 2002). In 1995, the gear changed and smaller fish were caught in greater abundance, so these data were left out for consistency. In West Greenland, data were collected from 1982-1986 (Riget & Messtorff 1988b), 1992-2004 (Storr-Paulsen & Jorgensen 2005), and 1982-2004 (Ratz & Stransky 2005) and a harmonic mean of abundance was taken for each survey.

Ne:N ratios are typically reported based on adult census sizes, yet surveys did not limit catch to adults. Graphs from various surveys show a large variation in lengths of captured spotted wolffish. If I assume that sexual maturity in spotted wolffish is typically

108 reached at 60-90 cm (Foss et al. 2004), the proportion of mature adults in each catch was approximately half. I divided census size estimates by two for both spotted and northern wolffishes, although no length data were reported for northern wolffish (Simpson & Kulka2002).

Finally, I compared Ne across the three species of wolffishes in the North Atlantic. I included data from Atlantic wolffish (Chapter 5), given that the same loci were used for genotyping. Ne is often subject to large estimation error, and comparisons among species can be compromised by variation in molecular methods and inherent differences among loci (Wang 2005). However, if the same molecular markers and methods have been used, results can usually be taken to be indicative of relative effective population sizes (see Discussion).

6.4 Results

6.4.1 Microsatellite Characteristics HWE and null allele problems were found in both northern and spotted wolffishes for Alul, therefore this locus was removed from further analyses. For northern wolffish, Alu25 and Alu26 also had HWE and null allele problems in multiple populations. The allele size pattern mAlu25 indicated non-stepwise mutation (in this species only), and Alu26 was characterized by a large gap among alleles. In addition, although amplification of Alu2S was possible in northern wolffish, and was polymorphic, scoring was difficult due to stutter. Sequencing revealed a complex microsatellite that was likely to be difficult to score even if new primers were developed, therefore this locus was not further analyzed in northern wolffish. Finally, Alu29 was nearly monomorphic in northern wolffish, and was therefore removed from further analysis. The remaining 10 loci revealed only two cases of HWE problems, neither of which was significant after a Bonferroni correction for the number of loci. No additional indications of null alleles or stuttering were found, no significant cases of linkage disequilibrium were found after correcting for multiple tests, and no cases of natural selection were revealed by FDist2. In northern wolffish, expected heterozygosity varied widely across loci (App. 14), but ranged from 0.58-0.60 across samples based on 10 loci (Table 22a).

109 For spotted wolffish, other thanAlu7, as mentioned above, no other significant deviations from HWE were found after a Bonferroni correction for either number of samples or loci (Rice 1989). However, Alu\ 1, AlulA, Alu27, and Alu2S were all monomorphic after amplification in approximately 100 individuals, therefore these loci were removed from analyses. For the remaining 10 loci, no significant cases of linkage disequilibrium were found after correcting for multiple tests. However, FDist2 identified Alu26 as having a significantly higher global FST value in spotted wolffish compared to other loci, possibly indicating natural selection at this locus or a closely linked locus. Therefore, analyses were performed without this locus. Expected heterozygosity in spotted wolffish varied widely across loci (App. 14), but ranged from 0.47-0.51 across samples based on nine loci (Table 22a).

6.4.2 AFLP Characteristics Based on approximately 15 blind duplicate samples (approximately 11% of 129 samples), the AFLP error rate was approximately 4.4% for northern wolffish. The majority of errors were due to poor amplification of the marker or background amplification that obscured bands. I encountered more amplification problems with spotted wolffish than with northern wolffish, and the overall error was estimated at 5.6% based on 42 blind duplicate samples (approximately 29% of 144 samples). Multiple spotted wolffish samples suffered from missing bands, which seemed to occur in a predictable fashion. These loci were not scored, and should not have influenced the error rate for this species, however the reason for the problem remains unclear. Restriction site or PCR inhibition may have been responsible, which has been cited as a potential problem with AFLP, although not a common one (Bonin et al. 2004; Bensch & Akesson 2005). After repeating the AFLP protocol for these samples, previously missing bands amplified clearly indicating that the problem was associated with AFLP amplification rather than DNA extraction. Nevertheless, these loci were excluded from the dataset in case amplification problems occurred in other samples that were unrecognized. Despite these amplification problems, error rates for both species were close to or within the range of error rates previously reported for AFLP studies (Bonin et al. 2007; Egan et al. 2008).

110 In total, 183 AFLP loci were analyzed in northern wolffish, and 98 AFLP loci were analyzed in spotted wolffish. Genome scans resulted in no significant outliers for either species (for p<0.001). DFdist uses a trimmed mean of FST values that is lower than the true mean, therefore conservative alpha values are recommended (an alpha value of

0.0005 was used in Beaumont and Balding (2004)). Plots of FST values with 99% confidence limits showed no clear outliers for either species (not shown), although four loci in northern wolffish were essentially on the upper confidence limit. Due to the stringency required for this analysis, these were not considered outliers and they remained in the analysis. AFLP heterozygosity was low for both species (0.20-0.28), well below that found for microsatellites (Table 22a, Apps. 15a,b), and heterozygosity values for the two marker types were not significantly correlated. The majority of variance in the AFLP analysis was attributed to differences among loci, and within- population variance among individuals accounted for only 10-20% of total variance (Apps. 15a,b).

6.4.3 Population Structure and Dispersal

6.4.3A Northern Wolffish In northern wolffish, FST values for microsatellites reached a maximum of 0.032 between Atlantic Canada and Barents Sea samples (Table 23 a), with standardized FST values up to 0.075. Significant differences were found between the Barents Sea samples and every other sample as well as between the Mid-Atlantic Ridge and one Atlantic Canadian sample (Table 23a). D produced qualitatively similar results to FST for microsatellites, as did AFLP (Table 23b). Power was greater for microsatellites, which resulted in significant differences among almost all pair-wise comparisons, using the exact test, compared to no significant differences with AFLP (Table 23b), although sample size was also higher for microsatellites. Permutation tests for AFLP, however, revealed multiple significant differences (Barents Sea versus others, and Mid-Atlantic Ridge versus Atlantic Canada). The band-based AFLP approach provided results more consistent with those from microsatellites than the allele frequency-based approach. Band-based FST values were nearly significantly correlated with microsatellite FST values when analyzed for the same samples (r=0.78, p=0.07), whereas a negative relationship

111 was found for the allele frequency-based approach. Differentiation was apparently too subtle for either BAPS or STRUCTURE to detect population structure in northern wolffish for either micro satellites or AFLP data. However, assignment tests for both microsatellites and AFLP indicated that the Barents Sea was the most distinct population (Figure 22).

Table 23a. Fsr (below diagonal) and p-value (above diagonal, based on contingency tests from TFPGA) (upper) and Jost's D (lower) for northern wolffish samples based on 10 microsatellite loci (Bonferroni corrected p-values <0.005 for loci or samples are in gray).

AC-L AC-U EG Ice MAR Bar-2004 Bar-2005 Atlantic Canada-lower 0.570 0.581 Atlantic Canada-upper -0.004 0.484 East Greenland -0.003 -0.003 Iceland 0.001 0.010 0.005 Mid-Atlantic Ridge 0.001 0.002 0.003 Barents-2004 0.021 0.032 0.021 Barents-2005 0.012 0.019 0.008

D AC-L AC-U EG Ice MAR Bar-2004 Bar-2005

AC-U -0.004 EG -0.003 -0.002 Ice -0.001 -0.008 -0.005 MAR -0.002 0.008 0.003 0.008 Bar-2004 0.017 0.031 0.013 0.004 0.018 Bar-2005 0.027 0.044 0.027 0.012 0.016 0.003

112 Table 23b. FST values (below diagonal) for AFLP data using band-based and allele frequency-based approaches for northern wolffish samples. P-values (above diagonal) are based on contingeny tests for allele frequencies using TFPGA. Bonferroni-corrected significant results from permutation tests for AFLP data using AFLP-SURV are also presented in bold.

AFLP band-based AC Ice MAR Bar Atlantic Canada Iceland 0.007 Mid-Atlantic Ridge 0.011 0.001 Barents 0.013 0.014 0.013

AFLP allele frequency-based AC Ice MAR Bar Atlantic Canada 1.000 0.999 0.952 Iceland 0.006 1.000 0.998 Mid-Atlantic Ridge 0.013 0.001 0.987 Barents 0.008 0.000 0.008

Table 24. Isolation by distance statistics including mean dispersal distance (MDD) estimates for northern wolffish.

MDD (km)

r p-value [FST/(l-FST)]/km D=\ D=\Q £>=100

FSr (microsatellites) 0.56 0.026 7.14x10" 132 42 13 D (microsatellites) 0.58 0.028

FST without Barents Sea -0.30 0.826 negative

FST (AFLP, band-based) 0.44 0.190 3.87x10"

FST (AFLP, allele frequency) 0.24 0.354 3.72x10"

IBD relationships for microsatellites (FST and D) were both significant across the range of northern wolffish, indicating limited dispersal (Table 24, Figure 23). For AFLP, the band-based method produced a slightly stronger IBD relationship compared to the allele-frequency method, but neither was significant, possibly due to the small number of sample locations. Average dispersal distances ranged from 13-132 km per generation depending on whether the effective density was 1, 10, or 100 (Table 24). According to Chebyshev inequality distances, based on an effective density of 10 ind km"1, 75% of individuals disperse less than 120 km over their lifetime, and -90% disperse less than 180km(App. 16).

113 6.4.3.ii Spotted Wolffish FST values for spotted wolffish reached a maximum of 0.008 between inshore West Greenland and Iceland samples (Table 25a) with standardized FST values up to 0.015. Several pair-wise comparisons were significantly different (p<0.05), although only five remained significant after a Bonferroni correction for either samples or number of loci (p<0.006) (Table 25a). Interestingly, these all involved the inshore West Greenland sample, suggesting restricted dispersal in this group (Table 25a). Perhaps the most unexpected result was that of negative FST between Atlantic Canada and Barents Sea samples as well as between one West Greenland sample and the Barents Sea (Table 25a). Genetic differentiation calculated using Jost's D was closer to what might be expected based on geographic distances, although differentiation was still low (up to 0.019) (Table 25a). AFLP band-based and allele frequency-based methods produced markedly different FST results, as they did for northern wolffish, although in this case, neither correlated with microsatellite data. As with northern wolffish, no AFLP samples were significantly different using exact tests, but permutation tests found all pair-wise comparisons to be significantly different (Table 25b). FST values were too small for either BAPS or STRUCTURE to detect population structure in spotted wolffish for either microsatellites or AFLP data. However, assignment tests revealed subtle population structure across the range with both marker types (Figure 24). IBD was positive for microsatellites, but not significant using FST (Table 26, Figure 25). However, given the negative FST values between the eastern and western end of the range, a weakly positive IBD was all that could be expected. Mean dispersal distance was estimated for comparison to northern wolffish, however due to the lack of significance of IBD, the dispersal distance should be interpreted with caution. IBD based on Jost's D was also positive and, in contrast to FST, marginally significant (Table 26). However, IBD with AFLP loci was not significant, likely due at least in part to the smaller sample sizes and number of locations analyzed. Interestingly, the band-based analysis produced a similar slope to that found with microsatellites, and as with northern wolffish, stronger IBD patterns compared to the allele-frequency analysis (Tables 24, 26). A general assessment of slopes indicated a shallower slope for spotted wolffish compared

114 to northern wolffish with microsatellites, but a very similar slope between the two species with AFLP (with the band-based method).

Table 25a. FST (below diagonal) and p-value (above diagonal, based on contingency tests from TFPGA) (upper) and Jost's D (lower) for spotted wolffish samples based on nine loci (Bonferroni corrected p-values <0.005 for loci or samples are in gray). AC WG IWG Ice-A Ice-B Ice-C Bar-A Bar-B AC 0.108 MllllI 0.269 0.019 0.501 0.143 0.049 WG 0.004 0.343 0.760 0.040 0.117 0.017 0.750 Inshore WG 0.005 0.000 0.009 0,001.-,^ii&n ..^jkQ&jl " 0.023 Ice-A 0.003 -0.004 0.008 0.391 0.885 0.031 0.909 Ice-B 0.004 0.005 0.006 -0.001 0.274 0.035 0.044 Ice-C -0.002 0.004 0.002 -0.004 0.000 0.158 0.113 Bar-A -0.001 -0.001 0.002 -0.003 0.002 -0.002 0.281 Bar-B -0.002 0.006 0.007 0.006 0.002 0.000 0.000

_D AC WG IWG Ice-A Ice-B Ice-C Bar-A

WG 0.007 IWG 0.019 0.003 Ice-A 0.009 -0.003 0.008 Ice-B 0.005 -0.014 -0.012 -0.020 Ice-C 0.000 0.003 0.008 -0.002 -0.004 Bar-A 0.002 0.001 0.007 0.002 -0.002 0.001 Bar-B 0.000 0.002 0.015 0.003 0.003 -0.001 0.004

6.4.4 Bottlenecks and Effective Population Size Neither BOTTLENECK nor M provided support for a recent bottleneck for either species. One spotted wolffish sample (Barents Sea) showed evidence of a heterozygosity deficit (p=0.04), possibly indicative of population substructure, but not a bottleneck.

Long-term Ne was estimated to be approximately 4,000 for northern wolffish and approximately 2,500 for spotted wolffish both across regions and for each species as a whole using ThetaF (Table 27). Current Ne based on linkage disequilibrium was estimated to be well below those values, though in many cases confidence limits reached infinity (Table 27). Using global Ne estimates based on ThetaF for each species, Ne:N ratios ranged from 4.6 x 10"3 to 1.1 x 10"2 for northern and spotted wolffishes in Atlantic Canada and West Greenland (Table 28).

115 Table 25b. FST values (below diagonal) for AFLP data using band-based and allele frequency-based approaches for spotted wolffish samples. P-values (above diagonal) are based on contingeny tests for allele frequencies using TFPGA. Bonferroni-corrected significant results from permutation tests for AFLP data using AFLP-SURV are also presented bold.

AFLP band-based AC WG Ice Bar Atlantic Canada West Greenland 0.011 Iceland 0.014 0.010 Barents 0.014 0.022 0.019

AFLP allele frequency-based AC WG Ice Bar Atlantic Canada 0.999 0.996 0.952 West Greenland 0.003 1.000 0.790 Iceland 0.004 0.005 0.795 Barents 0.005 0.008 0.012

Table 26. Isolation by distance statistics including mean dispersal distance (MDD) estimates for spotted wolffish.

MDD (km)

r p-value [Fsl/(l- FST)]/km D=\ D=10 £>=100

FST (microsatellites) 0.23 0.151 2.23 xlO"6 237 75 24

D (microsatellites) 0.36 0.048

6 FST without Barents Sea 0.55 0.004 3.02 xlO" 204 64 20

6 FST (AFLP, band-based) 0.18 0.335 4.02 xlO"

FST (AFLP, allele frequency) -0.22 0.623 neg ative

Comparative estimates of global Ne based on ThetaF across Atlantic, spotted, and northern wolffishes were calculated in several ways. In the first analysis, all variable loci (without amplification problems) were used, which included 14 loci for Atlantic wolffish, 10 loci for northern wolffish, and 9 loci for spotted wolffish. For the other analyses, I

compared Ne across species using identical loci. In order to do this, however, I had to determine whether or not species that were monomorphic for a particular locus still had the repeat array, and whether the number of repeats was within the range of that found for the polymorphic species. If the repeat array had disappeared or was significantly shorter in the monomorphic species, comparisons could not be made. Sequencing revealed that

116 fovAlu27 andAlu2S, the microsatellite array was still present in spotted wolffish, but was much reduced in size compared to the other species, therefore these loci were not used. However, the other three loci, Alul 1 and AlulA in spotted wolffish, and Alu29 in northern wolffish, still had the repeat array intact, and within the range of that found in the polymorphic species (App. 17). Therefore, these loci were included in comparative analyses.

0^05 O01 Population He Ne lower upper Ne lower upper Ne lower upper northern wolffish Atlantic Canada 0.58 3,898 2,274 7,035 223 107 3,093 451 172 oo East Greenland 0.59 3,773 2,290 6,504 oo 107 oo oo 101 oo

Mid-Atlantic Ridge 0.58 3,679 2,252 6,291 58 22 00 137 55 00 Iceland 0.60 4,234 2,695 6,956 200 44 oo 184 71 oo

Barents 0.57 3,612 2,250 6,010 OO OO 00 00 00 00 Global 0.59 4,129 2,524 7,062 945 305 oo 540 325 1,313

spotted wolffish

Atlantic Canada 0.51 2,542 1,786 3,651 OO 176 oo oo 342 oo

West Greenland 0.50 2,363 1,545 3,650 163 45 00 153 73 1,228

Iceland 0.49 2,231 1,537 3,256 OO 630 00 oo 1,544 00

Barents 0.50 2,453 1,626 3,738 OO 240 00 00 908 oo

Global 0.50 2,419 1,642 3,590 oo 1,482 oo 00 1,550 00

Comparisons across identical loci were based on three different analyses. In the first analysis, 10 loci were used that amplified in all three species with relatively few problems {Alu9, AlulO, Alu\\,Alu2\,Alu22, Alu23, Alu24, Alu29, Alu30, andAlu31). This included two monomophic loci in spotted wolffish (Alul 1 andAlu24), one nearly monomorphic locus in northern wolffish (Alu29), and one locus with very low polymorphism in Atlantic wolffish (Alu30). In the second combination, Alul 1 and Alu30 were removed, Alul 1 because the repeat array in spotted wolffish was not within the range of sizes found in Atlantic wolffish (although comparable to northern wolffish in size), andAlu30 because this locus showed some evidence of null alleles in Atlantic

117 wolffish. In the third combination, all monomorphic loci were removed and analyses were based on six loci: Alu9, AlulO, Alu2l,Alu22, Alu23, andAlu31. Comparisons of Ne among Atlantic, spotted, and northern wolffishes revealed large, consistent, and often significant differences in Ne among wolffish, regardless of the loci chosen (Figure 26).

Table 28. Census size and effective population sizes of northern and spotted wolffishes in Newfoundland (above) and spotted wolffish in West Greenland (below) waters. Ne:N ratios are based on half of the harmonic mean of census size.

Abundance data species Ne N Ne:N Atlantic Canada Simpson and Kulka, 2002 northern 3.9 xlO3 7.5 x 105 1.0 xlO"2 Simpson and Kulka, 2002 spotted 2.5 x 103 1.1 xlO6 4.6 xlO"3

West Greenland Storr-Paulsen and J0rgensen, 2005 spotted 2.4 xlO3 7.1 xlO5 6.7 x 10"3 Ratz and Stransky, 2005 spotted 2.4 x 103 4.3 x 105 1.1 x 10"2 Riget and Messtorff, 1988 spotted 2.4 x 103 9.8 x 105 4.8 x 10"3

6.5 Discussion

6.5.1 Microsatellite and AFLP Comparisons Microsatellites have a distinct advantage over AFLP in that they are codominant, so each locus is better understood and more informative than AFLP loci. Microsatellites can be explicitly tested for HWE, large allele drop-out, and null alleles, and the mutational mechanism for microsatellites can often be inferred. In contrast, because AFLP produces presence/absence data, the loci can only be assumed to be in HWE, and mutational differences underlying allelic diversity are usually unknown. On the other hand, AFLP does not suffer from ascertainment bias which can affect microsatellites (Ellegren 2000; Vali et ah 2008). Given these advantages and disadvantages, microsatellites and AFLP are complementary to one another. Weaknesses in one marker tend to be strengths in the other, and they, therefore, can lend credibility to one another when similar genetic patterns are found in both marker types. Studies have typically found similar patterns in the two marker types, although AFLP tends to produce lower

118 heterozygosity and often higher population differentiation than microsatellites (Mariette et al. 2002; Garoia et al. 2007; Sonstebo et al. 2007). As AFLP are dominant markers, allele frequencies must be derived using one of two methods (Bonin et al. 2007). The allele-frequency method derives allele frequencies based on an assumption of HWE whereas the band-based method uses the frequencies of 'presence' and 'absence' phenotypes as allele frequencies. Reviews of AFLP studies have found that allele frequency-based and band-based methods yield similar results (Nybom 2004; Bonin et al. 2007), and both methods are commonly used, although the allele frequency-based method is often preferred for large sample sizes (Bonin et al. 2007). However, in this study, FST values using the band-based method were more consistent with results from microsatellites for northern wolffish, and more consistent with expectations from geography for spotted wolffish than the allele frequency-based method. Interestingly, band-based methods also produced results more consistent with those from microsatellites for Atlantic wolffish in a previous study (Chapter 5), although the difference between the two methods was smaller than for northern and spotted wolffishes. Why the band-based approach might be preferable to the allele frequency-based approach is not clear, but I speculate about various circumstances in which it would be. First, if heterozygotes were underrepresented in the data due to population substructure, scoring errors, or sampling error, then the allele frequency-based approach, which assumes HWE, might distort relationships. Northern and spotted wolffish both showed positive Fis values across all populations for microsatellites, although deviations from HWE were not significant overall. The extent to which AFLP loci showed substructure, however, could not be evaluated. However, if heterozygotes were frequently mis- identified as 'absence' phenotypes (which might occur if they produced a fainter signal than homozygous individuals), then the band-based approach might better reflect true frequencies. A slightly different potential explanation is that the allele frequency approach was based on more loci with low frequencies of 'null' alleles compared to the band-based approach (Apps. 15 a,b). Loci with null allele frequencies less than 3/N (N is number of samples) were eliminated from analysis in AFLP-SURV (Vekemans 2002; Bonin et al. 2007), but because the 'null allele' frequency was higher with the allele-

119 frequency method than with the band-based method, more of these loci were included in the allele-frequency analysis. Given that these loci are usually more prone to error (Bonin et al. 2007), this may make the allele-frequency method less accurate. In one study in which both methods were used, results varied more depending on whether or not low polymorphic loci were eliminated than whether the band-based or allele frequency- based method was used (Mariette et al. 2002). Upon closer examination of the study by Mariette et al. (2002), slightly better alignment was found between microsatellites and AFLP with the band-based approach than with the allele frequency-based approach. Overall for northern wolffish, AFLP band-based approaches and microsatellite data produced comparable estimates of differentiation, assignment, and isolation by distance. Significant differentiation was found across the range, most notably between the Barents Sea and other samples, although the Mid-Atlantic Ridge was also distinct in some analyses (Table 24, Figure 22). For spotted wolffish, however, corroboration between markers was weak. AFLP produced markedly higher estimates of differentiation between the western most and eastern most samples than microsatellites (Table 25). In addition, corroboration between FST, D, and assignment tests was weaker for spotted wolffish than for northern wolffish. FST estimates between Atlantic Canada (and West Greenland) and the Barents Sea for spotted wolffish were particularly low with microsatellites, whereas both D and assignment tests showed slightly higher levels of differentiation. FST has recently been criticized for not being a true measure of differentiation as it can be influenced by heterozygosity differences between samples (Jost 2008). Consistent with this, a positive trend between heterozygosity differences between samples and FST was observed (and a negative trend with D), suggesting that FST estimates may have been influenced by heterozygosity. The less robust signal of population structure in spotted wolffish compared to northern wolffish may have been partly due to the fact that the Barents Sea was simply less distinct for spotted wolffish than for northern wolffish. Microsatellites and AFLP both indicated this distinctness in northern wolffish (Figure 22, Table 23), whereas the same was not true for spotted wolffish (Figure 24). Another possible explanation for the weak patterns and seemingly spurious results for spotted wolffish was low heterozygosity in this species. I conducted simulations in Easypop on populations with low and high

120 levels of variability in the initial population, and FST was found to be considerably more variable when level of polymorphism was low (standard deviations of FST up to 50% higher after 50 generations; not shown), even though the number of individuals was the same. Loci with low heterozygosity seem to suffer from two problems. They show greater variance in FST estimates and they have lower power to detect differences compared to more variable loci (Waples & Gaggiotti 2006). Spotted wolffish had low heterozygosity (App. 14). Sixty-seven percent of spotted loci and 30% of northern loci had heterozygosity values less than 0.5, and the two most common alleles had a combined frequency of >90% in 55% of spotted loci compared to 30% of northern loci (not including monomorphic loci which were also more prevalent in spotted wolffish). Interestingly, loci with greater heterozygosity were more likely to show IBD patterns in spotted samples (not shown). I found it interesting that assignment tests produced the most consistent results across marker types, which were also more consistent with geographic proximity than either D or FST. Indeed, Waples and Gaggiotti (2006) have shown that assignment tests may outperform FsT-type analyses in many situations, including when loci have low polymorphism. Finally, the low FST values between Atlantic Canada and Barents Sea for spotted wolffish were not consistent with previous analyses based on mtDNA in which Atlantic Canada was found to be distinct from both Iceland and Barents Sea samples (Chapter 3) I know of only one other range-wide study of spotted wolffish, which curiously, also found an affinity between one Atlantic Canada sample with the Barents Sea in an MDS plot with mtDNA, although the other Atlantic Canada sample was distinct, and both samples were significantly different from the Barents Sea (Imsland et al. 2008). I would argue that the apparent affinity between western Atlantic and Barents Sea samples found with microsatellite FST values in this study probably does not reflect recent shared history between the these regions, and instead reflects a spurious similarity. Verification of this hypothesis can only be determined from futher investigation.

6.5.2 Dispersal and Life History Estimated dispersal distances calculated for northern wolffish were approximately half of what they were for spotted wolffish (Table 24). However, the significance of the

121 IBD in northern wolffish was almost entirely due to the Barents Sea, as a very different trend was found after the Barents Sea samples were removed (Table 24). Thus, these dispersal distances may underestimate dispersal in northern wolffish throughout much of its range. In contrast, spotted wolffish showed significant IBD when the Barents Sea samples were removed (Table 24), arguing for restricted dispersal in this species despite low levels of differentiation across the range. As this IBD estimate was significant, I estimated the probability associated with various dispersal distances using the Chebychev inequality without the Barents Sea (App 16). Accurate dispersal distance estimates clearly depend on knowledge of effective density, yet this parameter can be difficult to gauge. One problem with long-term Ne estimates is that they were roughly the same regardless of geographic scale assessed.

Within region Ne was virtually the same as global Ne for each species, which is not surprising considering that ThetaF determines Ne based on heterozygosity levels, and heterozygosity levels did not change drastically with geographic scale. Therefore, if the whole range was considered an appropriate scale, effective density is low for both species (<1 ind km"1), whereas if the local sampling area is considered appropriate (-200 km), effective density would be much higher (>10 ind km"1). Perhaps a more problematic issue is that long-term Ne is probably a poor gauge of current Ne. To account for uncertainty in this parameter, dispersal distances were calculated for a range of effective densities (Table 24). Nevertheless, I regard an effective density of 10 individuals per kilometer as a reasonable first approximation. Given heterozygosity levels at the local sampling scale, I feel this number can be justified, although a more accurate estimate would require temporal samples, and clear evidence of independent population units. Effective densities of 10 ind km"1 translate to average dispersal distances of 27-64 km, for both species. I found it particularly interesting that the Chebychev values for spotted wolffish were broadly consistent with the limited tagging data available for this species. Wolffishes are thought to be sedentary animals, characterized by short- distance migration, but long-distance migrations (of hundreds of kilometers) do occur (Ostvedt 1963). If both the adult tagging studies and these genetic estimates are valid measures of dispersal, they suggest that adult movement alone can explain the level of

122 connectivity found in wolffishes. This is an intriguing hypothesis, but I stress that it is based on very limited tagging (and genetic) data. Subtle population structure was found for both species, though the clearest evidence of distinctness was the Barents Sea sample for northern wolffish (Figure 24). Although one microsatellite locus, Alu26, was not included due to the possible influence of selection, it did suggest that the Iceland sample for spotted wolffish was also distinct. One possible reason for the differences between regions is variation in spawning behaviour. The Atlantic wolffish is known to have considerable variation across its range in both direction (from shallow to deep water or vice versa) and timing of spawning migrations (Jonsson 1982; Keats et al. 1985; Pavlov & Novikov 1993), and the same may be true of northern and spotted wolffishes. Finally, limited population structure found in both northern and spotted wolffishes (with the exception of the Barents Sea sample in northern wolffish) may be partly due to a history of recent colonization following the last glaciation, as suggested by a recent mtDNA study of wolffishes (Chapter 3). Recent colonization (i.e. < 20 kya) may not be the main factor in determining population patterns, as evidence of population structure was found in both species, however, it may help to explain both low diversity and low levels of differentiation across their ranges.

6.5.3 Effective Population Size

Long-term Ne estimates for northern and spotted wolffishes were both below

5,000 individuals, which has been put forward as the minimum Ne necessary to maintain adaptive variation in natural populations (Lande 1995), although a minimum size in the low thousands was thought to be acceptable by others (Willi et al. 2006). One of the more striking results from this study was the differences in Ne among wolffish species. However, for these differences to be valid, ascertainment bias must be discounted. Ascertainment bias can arise in microsatellite studies for two reasons. One is that if microsatellite loci were chosen based on polymorphism in the species in which they were cloned, such screening is likely to produce higher levels of polymorphism in the target species relative to non-target species (Ellegren et al. 1995). In this study loci were not screened for polymorphism, so this type of ascertainment bias was not expected. The

123 other potential problem is that DNA library screening processes typically target loci with a large number of repeats, which leads to longer repeat arrays (and greater polymorphism) in the target species compared to non-target species (Ellegren et al. 1995). Ascertainment bias is known to occur, although it is typically less of a problem for closely related species (Rico et al. 1996; Harr et al. 1998; Carreras-Carbonell et al. 2008). Average allele sizes in Atlantic, northern, and spotted wolffishes were found to be comparable (189; 178; 195 for six loci, 201; 192; 205 for eight loci, and 200; 196; 201 bps across 10 loci, respectively), although the average minimum size allele across loci (181; 176; 183 bps) was more similar than the maximum size (217; 198; 201 bps, respectively). This may suggest some degree of ascertainment bias or it may simply be a result of greater variation in Atlantic wolffish. As microsatellite mutation is known to occur disproportionately towards larger alleles (Ellegren 2004), it is almost impossible to rule out ascertainment bias if the species in which loci were developed has the most variation. Nevertheless, minimum fragment sizes across loci were highly correlated among species (r=0.963 between Atlantic wolffish and northern wolffishes, and r=0.96 between Atlantic and spotted wolffishes), suggesting conservation of allele size across species. Given that comparisons were made across loci for which allele sizes (or length of repeat array) overlapped across species, I would argue that differences in diversity among species reflect differences in effective sizes rather than simply ascertainment bias. Moreover, comparable differences in diversity were found with mitochondrial DNA (see Chapter 3), with Atlantic wolffish showing higher diversity than northern and spotted wolffishes, providing further support for this hypothesis. Although range-wide abundance of wolffishes is not known with precision, catch statistics taken by the Food and Agriculture organization of the UN indicate a ten-fold difference in biomass of Atlantic wolffish caught compared to northern and spotted wolffishes from 1950-2006 (www.fao.org). Thus, a higher effective size of Atlantic wolffish compared to its congeners is not unexpected.

124 6.5.4 Conclusion Subtle population structure was found in both northern and spotted wolffishes. The Barents Sea and Mid-Atlantic Ridge, in particular, were distinct in northern wolffish. In spotted wolffish, structure was more subtle, but a cautionary approach would argue that management should still occur on a local scale. Perhaps a more striking result of this study was the low level of genetic diversity, and consequently Ne, found in these species. Typical heterozygosity and allele numbers for marine fishes are approximately 0.77 and 19.9, respectively, 0.68 and 10.8 for anadromous fishes, and 0.54 and 9.1 for freshwater fishes (DeWoody & Avise 2000). According to these values, heterozygosity levels of 0.58 and 0.49 and allele numbers of 8.8 and 7.2 per locus for northern and spotted wolffishes, respectively, are more similar to freshwater fishes than marine. Genetic variation at microsatellite loci has revealed an intriguing trend among wolffishes, in that the two species that have shown the greatest declines in abundance (spotted and northern wolffishes) also exhibited lower genetic variation and smaller effective population sizes than Atlantic wolffish. This underscores the vulnerability of spotted and northern wolffishes and suggests that they may be more likely to suffer from problems associated with low genetic variation, such as inbreeding, lower fitness, and reduced adaptive potential (Spielman et ah 2004; Frankham 2005; Willi et al. 2006).

All species of wolffishes, the three Atlantic species and the Bering wolffish, have become a conservation concern in Canada and they have all been assessed by COSEWIC. Together with the Pacific wolf-eel, they comprise the family Anarhichadidae, and thus they represent a distinct and significant component of fish diversity. As marine biodiversity faces increasing threats from habitat damage, exploitation, and climate change, conserving the underlying genetic variation that leads to future adaptation is a critical first step. This study highlights the genetic fragility of species that are already known to have suffered declines, at least in Canadian waters, likely due to exploitation and habitat damage. Wolffishes may be more vulnerable than previously appreciated and their unique evolutionary history warrants significant protection.

125 Figure 21. Sample sites for northern and spotted wolffish samples across the North Atlantic (see Tables 22a,b).

126 I 1 Bar ^H MAR 0.8 - caagg Ice •H AC © ^^H ^^H •a 4) a 0.6- ^^^^^^^^^^H ^^^^^^^^^^1 ass i a © ^^^H f 0.4 - a. H _ 2 in 0.2 - ^^^^^^^B ^^^^^^^H ^^^^^^^H

0.0 - AC Ice MAR Bar Sampled Population

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to : ^^H ^^^ ^^^ o ^ assigne d © ^^^^i Proportio n 0.2 - ^^^^^^^^^H

0.0 - AC Ice MAR Bar Sampled population Figure 22. Assignment results for microsatellites (upper) and AFLP (lower) for northern wolffish. The proportion of individuals from each population assigned to various populations are represented by shades of gray.

127 0.04

0.03 i

^ 0.02 i

t 5mm ^ 0.01

0.00

-0.01 1000 2000 3000 4000 5000 Geographic Distance (km)

0 1000 2000 3000 4000 5000 Geographic Distance (km)

Figure 23. Isolation by distance among northern wolffish samples based on FSj (upper) and Jost's D (lower) using 10 microsatellite loci.

128 0.8 -

a .2° 0.6 - [A « a i o 0.4 - • ;• j o '••'•'•: ' 1 i a. 2 ' ;.* ' i 0.2 - ^^^^H ^^^^^ ^^^v^

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129 0.008 H

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Geographic Distance (km)

Figure 25. Isolation by distance among spotted wolffish samples based on FST (upper) and Jost's D (lower) using nine microsatellite loci.

130 All variable loci 10 loci 8 loci 6 loci

131 Chapter 7: Conclusion

Genetic markers are an important tool for assessing evolutionary history, population connectivity, and effective population size in natural populations. A review of mtDNA and microsatellite diversity in freshwater and marine fishes found that these genetic markers generally do conform to the neutral theory and, therefore, are informative tools for addressing these types of questions. MtDNA, as a single marker, may not be as reliable as multiple microsatellite markers, but diversity was still correlated with abundance, particularly for studies that analyzed regions of the molecule other than the control region. Microsatellites were highly correlated with abundance when more than six loci were used and more than 40 individuals included. It follows that the wolffish dataset presented in this thesis, based on two mtDNA regions and up to 14 microsatellite loci should provide insight into population genetic parameters of wolffishes. Phylogenetic analysis based on mtDNA supported relationships based on traditional taxonomy and provided a time-frame for speciation in the family Anarhichadidae. Wolf-eel and Bering wolffish, both in the Pacific Ocean, likely diverged between four and six and a half million years ago, whereas the three species of wolffishes in the Atlantic Ocean likely diverged within the past two million years. According to mtDNA analyses, all three species experienced low population size (or low survival) roughly coinciding with the end of the last glaciation, likely contributing to the low diversity in these species. Results indicated that low population sizes were followed by a period of population expansion. Given the strong suggestion of population contraction for all three species during the last glaciation, migration-drift equilibrium may not yet exist in wolffishes, which creates difficulties in interpreting population structure and dispersal patterns. Nevertheless, some clear differences were found among wolffish species based on microsatellite and AFLP data. Atlantic wolffish exhibited significant genetic differentiation over short geographical distances in some areas, with very little structure over wide expanses in other parts of the range. Specifically, Atlantic Canada was distinct from other parts of the range, and significant differences were found among populations in this region. Rockall Bank was also distinct, but little differentiation was found across the North Atlantic Ocean. Genetic differentiation between the North Atlantic and

132 Atlantic Canada coincided with differences in genetic diversity, suggesting effective population size differences between these two regions. Two alternative hypotheses exist to explain genetic differentiation between Atlantic Canadian and North Atlantic samples. One is that a separate refuge existed in Atlantic Canada during the last glaciation, and the other is that Atlantic Canada was colonized by a small number of individuals, which contributed both to the genetic distinctness of this region (via genetic drift), as well as to low genetic diversity. The lack of obvious genetic differentiation across the North Atlantic may be related to larger population sizes and larger colonizing population sizes across this region following glaciation, or alternatively, to a recent colonization of the northern extent of the wolffish range from an eastern Atlantic refuge. Genetic differentiation between Rockall Bank and the rest of the range is likely related to its physical separation from the main continental shelf. Slightly less differentiation was found among northern wolffish and spotted wolffish populations compared to Atlantic wolffish populations, suggesting either higher levels of gene flow in these species, or more recent colonization of the range. MtDNA evidence suggests that spotted wolffish may have colonized the North Atlantic more recently than its congeners, which could have contributed to the particularly low population structure in this species. In northern wolffish, the strongest signal of genetic differentiation was between the Barents Sea samples and the rest of the range, a pattern not exhibited by spotted wolffish. The weak population structure found in spotted wolffish may have been partly due to the low genetic diversity in this species. Microsatellite loci were characterized by low polymorphism, which may have reduced power to detect significant differences. Atlantic wolffish, the most common of the three North Atlantic wolffishes, had significantly higher diversity than northern and spotted wolffishes. Although I can not rule out ascertainment bias as a contributing factor, as microsatellites were isolated in Atlantic wolffish, allele sizes were comparable across species. Qualitatively similar differences in diversity among species were also found with mtDNA, suggesting that ascertainment bias is not the most likely explanation. Moreover, concordance between mtDNA and microsatellite diversity suggests long-term differences in abundances among the three species. Northern and spotted wolffishes, with the lowest abundances and the

133 lowest estimated effective population sizes, also have higher levels of conservation concern in Atlantic Canada compared to Atlantic wolffish. Wolffishes are currently the only marine fishes protected under the Canadian Species at Risk Act. This study found lower population structure than expected for this species, suggesting a fairly high level of gene flow over evolutionary time, across the range of wolffishes. However, results indicate that on an ecological timescale, which is more important for managers, dispersal is among populations is likely to be low, indicating that management should occur on a local scale. Low levels of genetic diversity found in wolffishes highlight the potential fragility of these species and suggest that wolffishes, particularly northern and spotted wolffishes, may be at risk of the negative consequences associated with low genetic variation.

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Zardoya, R., R. Castilho, C. Grande, L. Favre-Krey, S. Caetano, S. Marcato, G. Krey and T. Patarnello. 2004. Differential population structuring of two closely related fish species, the mackerel (Scomber scombrus) and the chub mackerel (Scomber japonicus), in the Mediterranean Sea. Molecular Ecology, 13: 1785-1798.

Zatcoff, M. S., A. O. Ball and G. R. Sedberry. 2004. Population genetic analysis of red grouper, Epinephelus morio, and scamp, Mycteroperca phenax, from the southeastern US Atlantic and Gulf of Mexico. Marine Biology, 144: 769-777.

Zelenina, D. A., A. M. Khrustaleva and A. A. Volkov. 2006. Comparative study of the population structure and population assignment of sockeye salmon Oncorhynchus nerka from West Kamchatka based on RAPD-PCR and microsatellite polymorphism. Russian Journal of Genetics, 42: 563-572.

Zhang, J. B., Z. P. Cai and L. M. Huang. 2006. Population genetic structure of crimson snapper Lutjanus erythropterus in East Asia, revealed by analysis of the mitochondrial control region. ICES Journal of Marine Science, 63: 693-704.

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181 Appendix 1. List of marine fishes and citations used. 1. Acanthochromispolyacanthus (Planes et al. 2001; Miller-Sims et al. 2005) 2. Acanthopagrus butcheri, Southern black bream (Yap et al. 2000; Burridge et al. 2004) 3. Albula vulpes, bonefish (Colborn et al. 2001; Seyoum et al. 2008b) 4. Anarhichas denticulatus, northern wolffish (Chapters 3, 6) 5. Anarhichas lupus, Atlantic wolffish (Chapters 3, 5) 6. Anarhichas minor, spotted wolffish (Chapters 3, 6) 7. Archosargusprobatocephalus, sheepshead (Anderson et al. 2008) 8. Brevoortia patronus, Gulf menhaden (Bowen & Avise 1990; Anderson 2007; Anderson & Karel 2007; Anderson & McDonald 2007) 9. Brevoortia tyrannus, Atlantic menhaden (Bowen & Avise 1990; Anderson 2007; Anderson & Karel 2007) 10. Carcharhinus limbatus, blacktip shark (Keeney et al. 2005) 11. Carcharhinus plumbeus, sandbar shark (Heist et al. 1995; Portnoy et al. 2006; Daly-Engel et al. 2007) 12. Centropomus undecimalis, common snook (Tringali & Bert 1996; Seyoum et al. 2005) 13. Champsocephalus gunnari, mackerel icefish (Williams et al. 1994; Papetti et al. 2007) 14. Clupea harengus, Atlantic herring (Kornfield & Bogdanowicz 1987; Shaw et al. 1999b; McPherson et al. 2001; Hauser et al. 2001; McPherson et al. 2004) 15. Clupeapallasii, Pacific herring (Grant & Bowen 1998; Shaw et al. 1999b; Small et al. 2005) 16. Conger myriaster, whitespotted conger (Ishikawa et al. 2001b; Kimura et al. 2003) 17. Cynoscion nebulosus, spotted weakfish (Gold & Richardson 1998a; Ward et al. 2007) 18. Cynoscion regalis, weakfish (Graves et al. 1992; Cordes & Graves 2003) 19. Dicentrarchus labrax, common sea bass (Garcia De Leon et al. 1997; Naciri et al. 1999; Lemaire et al. 2005) 20. Dissostichus eleginoides, Patagonian toothfish (Appleyard et al. 2002b; Appleyard et al. 2004; Shaw etal. 2004) 21. Engraulis encrasicolus, European anchovy (Landi et al. 2005; Grant & Bowen 2006; Magoulas et al. 2006) 22. Engraulis japonicus, Japanese anchovy (Yu et al. 2002; Yu et al. 2005; Liu et al. 2006b) 23. Epinephelus morio, red grouper (Richardson & Gold 1997; Zatcoff et al. 2004) 24. Epinephelus quernus, Hawaiian grouper (Rivera et al. 2003; Rivera et al. 2004)

182 25. Gadus morhua, Atlantic cod (Carr & Marshall 1991; Carr et al. 1995; Bentzen et al. 1996; Arnason & Palsson 1996; Ruzzante et al. 1998; Amason et al. 2000; Sigurgislason & Arnason 2003; Pampoulie et al. 2006; O'Leary et al. 2007) 26. Genypterus blacodes, pink cusk-eel (Ward et al. 2001; Smith & Paulin 2003) 27. Helicolenus dactylopterus, blackbelly rosefish (Aboim et al. 2003; Aboim et al. 2005) 28. Hippocampus capensis, Knysna seahorse (Teske et al. 2003; Galbusera et al. 2007) 29. Hoplostethus atlanticus, orange roughy (Smolenski et al. 1993; Elliott et al. 1994; Baker et al. 1995; Smith et al. 1996; Oke et al. 1999) 30. Isurus oxyrinchus, shortfin mako (Heist et al. 1996; Schrey & Heist 2003) 31. Lateolabrax japonicus, Japanese sea bass (Liu et al. 2006a; Jiang et al. 2008) 32. Lutjanus argentimaculatus, mangrove jack (Ovenden & Street 2003) 33. Lutjanus campechanus, red snapper (Garber et al. 2004; Gold & Burridge 2004; Pruett et al. 2005; Saillant & Gold 2006) 34. Lutjanus erythropterus, crimson snapper (Lo et al. 2006; Zhang et al. 2006) 35. Macruronus magellanicus, long-tailed hake (D'Amato & Carvalho 2005; D'Amato 2006) 36. Makaira nigricans, blue marlin (Graves 1998; Buonaccorsi et al. 2001) 37. Mallotus villoses, Atlantic capelin (Roed et al. 2003; Gordos et al. 2005; Dodson et al. 2007) 38. Megalops atlanticus, tarpon (Blandon et al. 2003; McMillen-Jackson et al. 2005; Seyoum et al. 2008a) 39. Melanogrammus aeglefinus, haddock (Zwanenburg etal. 1992; Lage & Kornfield 1999; Lage et al. 2001; O'Reilly et al. 2002) 40. Merluccius merluccius, European hake (Lundy et al. 1999; Castillo et al. 2004; Lo Brutto et al. 2004) 41. Mugil cephalus, flathead grey mullet (Miggiano et al. 2005; Semina et al. 2007) 42. Mullus barbatus, red mullet (Mamuris et al. 2001; Garoia et al. 2004; Galarza et al. 2007) 43. Mullus surmuletus, surmullet (Mamuris et al. 2001; Galarza et al. 2007) 44. Nemadactylus macropterus, jackass morwong (Grewe et al. 1994; Burridge & Smolenski 2003) 45. Odontesthes argentinensis, marine silverside (Beheregaray & Sunnucks 2001) 46. Ophiodon elongates, lingcod (Withler et al. 2004; Marko et al. 2007) 47. Pagellus bogaraveo, blackspot seabream (Bargelloni et al. 2003; Pinera et al. 2007) 48. Pagrus auratus, silver seabream (Tabata & Taniguchi 2000; Hauser et al. 2002; Bernal- Ramirez et al. 2003)

183 49. Pagrus major, red seabream (Tabata & Taniguchi 2000; Hatanaka et al. 2006) 50. Pagrus pagrus, red porgy (Ball et al. 2007) 51. Paralabrax clathratus, kelp bass (Luzier & Wilson 2004; Selkoe et al. 2006) 52. Paralichthys olivaceus, Japanese flounder (Sekino & Hara 2000; Coimbra et al. 2001; Sekino & Hara 2001; Sekino et al. 2002; Ortega-Villaizan et al. 2006)* 53. Pleuronectesplatessa, European plaice (Watts et al. 1999; Hoarau et al. 2002; Hoarau et al. 2004; Watts et al. 2004) 54. Pomatoschistus microps, common goby (Gysels et al. 2004; Berrebi et al. 2006) 55. Pomatoschistus minutus, sand goby (Stefanni & Thorley 2003; Pampoulie et al. 2004) 56. Pterapogon kauderni, Banggai cardinal fish (Bernardi & Vagelli 2004; Hoffman et al. 2005) 57. Raja clavata, thornback ray (Chevolot et al. 2006) 58. Reinhardtius hippoglossoides, Greenland halibut (Vis et al. 1997; Knutsen et al. 2006) 59. Sardinapilchardus, European sardine (Atarhouch et al. 2006; Gonzalez & Zardoya 2007) 60. Sardina sagax, Pacific sardine (Lecomte et al. 2004; Pereyra et al. 2004) 61. Sciaenops ocellatus, red drum (Gold et al. 1993; Gold & Richardson 1998a; Gold & Turner 2002; Chapman et al. 2002; O'Malley et al. 2003) 62. Scomber japonicus, chub mackerel (Zardoya et al. 2004; Yagishita & Kobayashi 2008) 63. Scomberomorus cavalla, king mackerel (Gold et al. 1997; Broughton et al. 2002; Gold et al. 2002; Santa Brigida et al. 2007) 64. Scomberomorus commerson, narrow-barred Spanish mackerel (Hoolihan et al. 2006; van Herwerden et al. 2006) 65. Sebastes auriculatus, brown rockfish (Seeb 1998; Buonaccorsi et al. 2005) 66. Sebastes caurinus, copper rockfish (Seeb 1998; Buonaccorsi et al. 2002) 67. Sebastes maliger, quillback rockfish (Seeb 1998; Wimberger et al. 1999) 68. Sebastes melanops, black rockfish (Miller et al. 2005; Burford & Bernardi 2008) 69. Sebastes mystinus, blue rockfish (Cope 2004; Burford & Larson 2007; Burford & Bernardi 2008) 70. Sebastes schlegeli, Korean rockfish (Higuchi & Kato 2002; Yoshida et al. 2005) 71. Sebastes thompsoni, rockfish (Sekino et al. 2001; Higuchi & Kato 2002) 72. Seriola dumerili, greater amberjack (Gold & Richardson 1998b; Renshaw et al. 2006a) 73. Seriola lalandi, yellowtail kingfish (Nugroho et al. 2001) 74. Solea senegalensis, Senegalese sole (Diaz-Ferguson et al. 2007; Porta et al. 2007)

184 75. Solea vulgaris, common sole (Iyengar et al. 2000; Guarniero et al. 2002; Garoia et al. 2006; Garoia et al. 2007) 76. Syngnathus leptorhynchus, bay pipefish (Wilson 2006) 77. Tetrapturus albidus, white marlin (Graves 1998; Graves & McDowell 2006) 78. Tetrapturus audax, striped marlin (Graves 1998; McDowell & Graves 2008) 79. Thalassoma bifasciatum, bluehead wrasse (Haney et al. 2007) 80. Theragra chalcogramma, walleye pollock (O'Reilly et al. 2000; Olsen et al. 2002; O'Reilly et al. 2004) 81. Thunnus alalunga, albacore (Graves & Dizon 1989; Scoles & Graves 1993; Takagi et al. 2001; Vinas et al. 2004; Nakadate et al. 2005) 82. Thunnus albacares, yellowfin tuna (Scoles & Graves 1993; Graves 1998; Appleyard et al. 2001; Diaz-Jaimes & Uribe-Alcocer 2006) 83. Thunnus obesus, bigeye tuna (Chow et al. 2000; Appleyard et al. 2002a; Chiang et al. 2006) 84. Thunnus thynnus, Atlantic bluefin tuna (Carlsson et al. 2004; Clark et al. 2004; Bremer et al. 2005b) 85. Trachurus trachurus, horse mackerel (Karaiskou et al. 2004; Comesana et al. 2008; Kasapidis & Magoulas 2008) 86. Verasper moseri, barfin flounder (Ortega-Villaizan et al. 2006) 87. Verasper variegates, spotted halibut (Ortega-Villaizan et al. 2006) 88. Xiphias gladius, swordfish (Rosel & Block 1996; Chow et al. 1997; Reeb et al. 2003; Bremer et al. 2005a; Jean et al. 2006)

185 Appendix 2. List of freshwater and diadromous fishes and citations used. 1. Acipenser fuhesens, lake sturgeon (Dehaan et al. 2006) 2. Acipenser naccarii, Adriatic sturgeon (Ludwig et al. 2003) 3. Acipenser oxyrinchus, American sturgeon (King et al. 2001b; Wirgin et al. 2007) 4. Acipenser transmontanus, white sturgeon (Brown et al. 1992; Brown et al. 1993; Smith et al. 2002) 5. Acrossocheilus paradoxus, cyprinid (Wang et al. 2000; Hsu et al. 2004) 6. Alosa alosa, Allis shad (Faria et al. 2004; Alexandrino et al. 2006) 7. Alosa fallax, Twaite shad (Faria et al. 2004; Alexandrino et al. 2006; Volk et al. 2007) 8. Alosa pseudoharengus, alewife (Palkovacs et al. 2008) 9. Alosa sapidissima, American shad (Brown et al. 1996; Waters et al. 2000; Hasselman and Bentzen, unpub.) 10. Anguilla anguilla, European eel (Daemen et al. 2001; Maes et al. 2006) 11. Anguilla japonica, Japanese eel (Tseng et al. 2001; Ishikawa et al. 2001 a) 12. Aphanius fasciatus, killifish (Babbucci et al. 2007; Rocco et al. 2007; Triantafyllidis et al. 2007) 13. Apollonia melanostoma, round goby (Brown & Stepien 2008) 14. Arapaima gigas, Arapaima (Farias et al. 2003; Hrbek et al. 2005) 15. Aristichthys nobilis, bighead carp (Lu et al. 1997; Cheng et al. 2008) 16. Astyanax fasciatus, Characid (Strecker et al. 2003; Strecker et al. 2004) 17. Carassius langsdorfii, Japanese silver crucian carp (Ohara et al. 2003) 18. Catostomusplebeius, Rio Grande sucker (McPhee et al. 2008) 19. Clarias gariepinus, North African catfish (Galbusera et al. 1996; Agnese et al. 1997; Giddelo et al. 2002) 20. Coregonus albula, vendace (Huuskonen et al. 2004; Schulz et al. 2006) 21. Coregonus clupeaformis, lake whitefish (Bernatchez etal. 1999; Lu et al. 2001; Gagnon & Angers 2006) 22. Coregonus lavaretus, European whitefish (Hansen et al. 1999) 23. Coregonus nasus, broad whitefish (Patton et al. 1997) 24. Coregonus oxyrhynchus, houting (Hansen etal. 1999) 25. Cottus gobio, bullhead (Hanfling et al. 2002; Knapen et al. 2003) 26. Cyprinus carpio, common carp (Kohlmann et al. 2003)

186 27. Eretmodus cyanostictus, Tanganyika clown (Ruber et al. 2001) 28. Esox lucius, northern pike (Brzuzan et al. 1998; Maes et al. 2003; Jacobsen et al. 2005; Gagnon & Angers 2006) 29. Fundulus heteroclitus, mummichog (Gonzalez-Vilasenor & Powers 1990; Adams et al. 2005) 30. Gasterosteus aculeatus, three-spine stickleback (Makinen et al. 2006; Makinen & Merila 2008) 31. Hypophthalmichthys molitrix, silver carp (Lu et al. 1997; Liao et al. 2007) 32. Lates calcarifer, Barramundi (Giant seaperch) (Chenoweth et al. 1998; Doupe & Lymbery 1999; Zhu et al. 2006a; Zhu et al. 2006b) 33. Leuciscus cephalus, chub (Durand et al. 1999; Larno et al. 2005) 34. Lota lota, burbot (Van Houdt et al. 2005; Barluenga et al. 2006) 35. Melanotaenia eachamensis, Lake Eacham rainbow fish (Zhu et al. 1998) 36. Melanotaenia splendida, Eastern rainbow fish (Zhu et al. 1998) 37. Micropterus punctulatus, spotted bass (Coughlin et al. 2003) 38. Micropterus salmoides, largemouth black bass (Johnson et al. 2001; Lutz-Carrillo et al. 2006; Lutz-Carrillo et al. 2008) 39. Morone saxatilis, striped bass (Waldman et al. 1998; Brown et al. 2005) 40. Nannoperca australis, southern pygmy perch (Cook et al. 2007) 41. Notropis mekistocholas, Cape Fear shiner (Gold et al. 2004) 42. Oncorhynchus gorbuscha, pink salmon (Brykov et al. 1996; Brykov et al. 1999; Salmenkova et al. 2006) 43. Oncorhynchus keta, chum salmon (Olsen et al. 2004; Sato et al. 2004; Chen et al. 2005; Smaller/. 2006) 44. Oncorhynchus kisutch, coho salmon (Carney et al. 1997; Small et al. 1998; Smith et al. 2001; Ford et al. 2004; Olsen et al. 2004) 45. Oncorhynchus masou, Masu salmon (Kawamura et al. 2007) 46. Oncorhynchus mykiss, rainbow trout, steelhead (Bagley & Gall 1998; Aguilar & Garza 2006; Pearse et al. 2007a) 47. Oncorhynchus nerka, sockeye salmon (Bickham et al. 1995; Withler et al. 2000; Beacham et al. 2005; Brykov et al. 2005; Zelenina et al. 2006) 48. Oncorhynchus tshawytscha, chinook salmon (Adams et al. 1994; Heath et al. 2006; Withler et al. 2007) 49. Oreochromis mossambicus, Mozambique tilapia (D'Amato et al. 2007)

187 50. Oreochromis niloticus, Nile tilapia (D'Amato et al. 2007) 51. Osmerus eperlanus, European smelt (Taylor & Dodson 1994; Taylor et al. 2008) 52. Osmerus mordax, rainbow smelt (Baby etal. 1991; Taylor & Bentzen 1993; Saint-Laurent et al. 2004; Bradbury et al. 2006) 53. Pangasianodon gigas, giant catfish (Ngamsiri et al. 2007) 54. Percaflavescens, American yellow perch (Billington 1993; Gagnon & Angers 2006; Leclerc et al. 2008) 55. Percafluviatilis, European perch (Nesbo et al. 1998; Behrmann-Godel et al. 2006; Bergek & Bjorklund 2007) 56. Petromyzon marinas, sea lamprey (Bryan et al. 2005; Waldman et al. 2006) 57. Plecoglossus altivelis, ayu sweetfish (Ikeda & Taniguchi 2002; Ikeda et al. 2003; Iwata et al. 2006; Yamamoto et al. 2007) 58. Poecilia reticulata, guppy (Alexander et al. 2006; Neff et al. 2008) 59. Pseudocrenilabrus multicolor victoriae, cichlid (Crispo & Chapman 2008) 60. Pseudocrenilabrus philander, cichlid (Koblmueller et al. 2008) 61. Rutilus rutilus, roach (Rezvani et al. 2006; Keyvanshokooh et al. 2007) 62. Salmo salar, Atlantic salmon (Verspoor et al. 1999; King et al. 2001a; Tonteri et al. 2007) 63. Salmo trutta, sea trout (Bernatchez 2001; Hansen et al. 2002; Cortey & Garcia-Marin 2002) 64. Salvelinus alpinus, Arctic char (Brunner et al. 2001; Wilson et al. 2004; Lundrigan et al. 2005) 65. Salvelinus confluentus, bull trout (Taylor et al. 1999; Taylor et al. 2001) 66. Salvelinus fontinalis, brook trout (Danzmann et al. 1991; Jones et al. 1996; Hebert et al. 2000) 67. Salvelinus leucomaenis, white-spotted char (Oleinik et al. 2005; Kubota et al. 2007) 68. Salvelinus malma, Dolly varden (Oleinik et al. 2002; Crane et al. 2004; Oleinik et al. 2005; Koizumi et al. 2006) 69. Salvelinus namaycush, lake trout (Wilson & Hebert 1996; Wilson & Hebert 1998; Guinand et al. 2003) 70. Sarotheradon melanotheron, blackchin tilapia (Pouyaud et al. 1999; Falk et al. 2003) 71. Silurus glanis, Wels (Som) catfish (Krieg et al. 2000; Triantafyllidis et al. 2002) 72. Squalius aradensis, cyprinid (Mesquita et al. 2005) 73. Tanganicodus cf. irsacae, cichlid (Ruber et al. 2001) 74. Thaleichthys pacificus, eulachon (McLean et al. 1999; McLean & Taylor 2001)

188 75. Thymallus arcticus, Arctic grayling (Stamford & Taylor 2004) 76. Thymallus thymallus, grayling (Koskinen et al. 2002; Gum et al. 2005) 77. Xystichromisphytophagus, cichlid (Abila et al. 2004)

189 Appendix 3. Observed and expected heterozygosity for freshwater/diadromous (above) and marine fishes (below) relative to sample sizes. Only sample sizes less than 100 are shown.

i.o 1.0

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20 40 60 80 100 20 40 60 80 100 II

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0.2 0.2 20 40 60 80 100 20 40 60 80 II

190 Appendix 4. Representative graphs of the relationships between diversity and sample size in the primary dataset (all fishes). Only sample sizes less than 100 are shown.

100

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100 100

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192 Appendix 5. Graphs of the highest correlations between genetic diversity and catch. Those for mtDNA data are from Tables 5 and 5b and those for microsatellite data are from Table 4.

0.4 • • • - 0-2 • • • • * * I 0> - 0.0 • afl % • # £ -0.2 • m « * X • A" •0.4 • • •

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193 App. 5 cont'd.

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194 Appendix 6. MtDNA primers DL-F, DL-R, ND1-F, and ND1-R were used for PCR amplification. Internal primers (labeled with "Int") were used for sequencing PCR (see text). Primer Name Nucleotide sequence DL-F CTCCCAAAGCTAGGATTCTAA DL-R TTTCTAGGGCCCATCTTAAC IntDL-F ATTGAAGGTGAGGGACAAG IntDL-R TATGTAAGCGTCGATGAAAG ND1-F GGCAGAGCCCGGTWATTG ND1-R GTTCACTCTATCAAAGTGG IntNDl-F GTGGGCCTCTAATTCAAAG IntNDl-R ATGTTTAGGAGAATAAGGCC

Appendix 7. Amino acid variation at the following amino acid sites in the ND1 region within the family Anarhichadidae. Blanks indicate no change in amino acid from the first one listed. n 5 19 75 80 101 178 182 245 275 317 324 Atlantic 104 I V M A V V L A V L V 3 I spotted 54 I northern 3 69 I 1 I T Bering 1 I F M wolf-eel 2 V I T M I M M F

195 Appendix 8. Haplotypes found across the range of Atlantic wolffish. Haplotype numbers correspond with the phylogenetic tree and minimum spanning networks (Figures 4,7). Scotian Gulf of St. Newfoundland Iceland North Sea Barents Total Shelf Lawrence Hap_l 8 4 3 2 7 2 26 Hap_2 8 8 4 4 1 25 Hap_3 1 1 Hap_4 1 1 1 6 1 1 11 Hap_5 1 Hap_6 1 Hap_7 Hap_8 Hap_9 Hap_10 Hap_ll 3 2 Hap_12 Hap_13 Hap_14 Hap_15 Hap_16 Hap_17 Hap_18 1 Hap_19 1 Hap_20 3 Hap_21 2 Hap_22 2 Hap_23 2 Hap_24 1 Hap_25 1 Hap_26 Hap_27 Hap_28 Hap_29 Hap_30 Hap_31 Hap_32 Hap_33 18 15 15 20 23 107

196 Appendix 9. Haplotypes found across the range of spotted wolffish. Haplotype numbers correspond with the phylogenetic tree and minimum spanning networks (Figures 4,7). Newfoundland Iceland Barents Total Hap_l 14 5 4 23 Hap_2 4 5 7 16 Hap_3 2 2 Hap_4 1 1 Hap_5 1 1 2 Hap_6 1 1 Hap_7 1 1 Hap_8 1 1 Hap_9 3 3 Hap_10 1 1 Hapjl 1 1 Hap_12 1 1 Hap_13 1 1 21 16 17 54

Appendix 10. Haplotypes found across the range of northern wolffish. Haplotype numbers correspond with the phylogenetic tree and minimum spanning networks (Figures 4,7). Newfoundland Mid-Atlantic Ridge Iceland Barents Total Hap_l 13 9 9 9 40 Hap_2 6 5 3 3 17 Hap_3 1 Hap_4 2 3 Hap_5 1 Hap_6 3 4 Hap_7 1 Hap_8 2 2 Hap_9 1 1 Hap_10 1 1 Hapl 1 1 1 Hap_12 1 1 24 14 19 16 73

197 Appendix 11. Dominant current patterns in the North Atlantic Ocean (http://www.mar- eco.no/learning-zone/ data/page/471/current_l_LG.jpg) •rn ?rn

7fl*W txrtt st'w 4§*w 3i*W arw irw *• Atlantic water V Mfticvwiter •> CMSW war«r

198 Locus Alu9 AMO Alu\ 1 AlulA Alu2\ Alu22 Alu23 Alu24 Alu25 Alu26 Alu21 Alu2% Alu29 Alui\ Overall "< 2 -o|

NA 13 10 24 20 14 11 14 6 19 21 25 23 8 14 \ &

Scotian Shelf-2002 0.67 0.67 0.83 0.69 0.72 0.12 0.55 0.66 0.61 0.79 0.87 0.87 0.53 0.85 0.67 © P P » as Scotian Shelf-2004 0.65 0.68 0.75 0.68 0.71 0.25 0.53 0.64 0.72 0.75 0.84 0.87 0.57 0.85 0.68 f* la S Gulf of St. Lawrence 0.61 0.70 0.79 0.66 0.72 0.08 0.54 0.66 0.61 0.78 0.85 0.79 0.55 0.84 0.66 <* ** B «s N Gulf of St. Lawrence 0.69 0.71 0.82 0.71 0.74 0.26 0.55 0.68 0.70 0.82 0.88 0.86 0.42 0.85 0.69 It " && Southern Newfoundland 0.65 0.66 0.81 0.69 0.75 0.15 0.59 0.66 0.66 0.78 0.85 0.87 0.56 0.86 0.68 £• » IZ °B- SE Grand Banks 0.70 0.67 0.83 0.77 0.75 0.35 0.62 0.66 0.65 0.82 0.85 0.89 0.53 0.84 0.71 | g NE Grand Banks 0.66 0.66 0.82 0.78 0.77 0.38 0.62 0.66 0.69 0.80 0.88 0.90 0.55 0.85 0.72 a West Greenland 0.72 0.70 0.83 0.84 0.71 0.38 0.61 0.67 0.76 0.87 0.80 0.90 0.53 0.84 0.73 §"

East Greenland 0.77 0.71 0.86 0.83 0.74 0.30 0.61 0.67 0.77 0.85 0.81 0.91 0.57 0.86 0.73 3 era Iceland-2002 0.75 0.72 0.82 0.78 0.68 0.42 0.61 0.66 0.76 0.85 0.83 0.91 0.55 0.85 0.73 I.

Iceland-2004 0.76 0.72 0.82 0.77 0.71 0.36 0.59 0.66 0.76 0.86 0.85 0.92 0.56 0.85 0.73 *» *^ Spitsbergen 0.83 0.70 0.90 0.80 0.69 0.32 0.64 0.69 0.78 0.84 0.79 0.91 0.50 0.84 0.73 g "S- BarentsSea 0.80 0.69 0.86 0.79 0.70 0.34 0.64 0.68 0.76 0.88 0.85 0.91 0.55 0.85 0.74 »

North Sea 0.82 0.72 0.85 0.76 0.75 0.37 0.60 0.68 0.80 0.86 0.86 0.91 0.54 0.84 0.74 g

Rockall Bank-2005 0.77 0.68 0.79 0.86 0.66 0.37 0.55 0.68 0.73 0.86 0.81 0.91 0.52 0.86 0.72 *" 8. Rockall Bank-2006 0.79 0.75 0.80 0.75 0.69 0.46 0.56 0.68 0.79 0.87 0.84 0.93 0.57 0.83 0.74 sS a Population . g , # loci polymorphic polymorphic Hj S.E.(Hj) Var(Hj) Varl(Hj) Varl% VarL(Hj) VarL Iff samples scored % loci loci Frequency-based- no outliers -pi Scotian Shelf 31 170 94 55% 0.20 0.0146 0.0002 0.00002 9% 91% is 0.0002 i S £ Eastern Grand Banks 31 170 105 62% 0.21 0.0142 0.0002 0.00002 11% 0.0002 89% Iff Iceland 52 170 111 65% 0.21 0.0135 0.0002 0.00001 8% 0.0002 92% Spitsbergen 32 170 117 69% 0.23 0.0134 0.0002 0.00002 13% 0.0002 87% HI RockallBank 32 170 109 64% 0.21 0.0140 0.0002 0.00002 11% 0.0002 89% P» Sii oto — F o Bayesian method with non-uniform prior distribution of allele frequencies- no outliers li Gfi Scotian Shelf 31 170 118 69% 0.23 0.0135 0.0002 0.00003 14% 0.0002 86% t 3 O IDS C- Eastern Grand Banks 31 170 124 73% 0.24 0.0136 0.0002 0.00002 13% 0.0002 87% "S 8. fl n ft ft w BT Iceland 52 170 127 75% 0.23 0.0135 0.0002 0.00002 9% 0.0002 92% I 2" » 8- B Spitsbergen 32 170 133 78% 0.26 0.0132 0.0002 0.00002 13% 0.0002 87% n ft

RockallBank 32 170 133 78% 0.23 0.0127 0.0002 0.00003 16% 0.0001 84% D S

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n #loci # P loci % P loci Hj S.E.(Hj) Varl% VarL% Atlantic Canada 34 183 132 72% 0.22 0.012 16% 84% Mid-Atlantic Ridge 24 183 117 64% 0.22 0.013 17% 83% Iceland 26 183 111 61% 0.21 0.013 14% 86% Barents 34 183 126 69% 0.21 0.012 15% 85%

n #loci # P loci % P loci Hj S.E.(Hj) Varl% VarL% Atlantic Canada 34 183 132 72% 0.24 0.012 11% 89% Mid-Atlantic Ridge 24 183 134 73% 0.24 0.012 19% 81% Iceland 26 183 130 71% 0.23 0.013 16% 84% Barents 34 183 126 69% 0.22 0.012 12% 88%

Appendix 15b. Spotted wolffish population data from AFLP-SURV [Lynch & Milligan method]: frequency-based (upper) and Bayesian approach (lower).

n #loci # P loci % P loci Hj S.E.(Hj) Varl% VarL% Atlantic Canada 31 98 74 76% 0.26 0.018 13% 87% West Greenland 33 98 73 75% 0.26 0.018 13% 87% Iceland 34 98 79 81% 0.28 0.018 12% 88% Barents 35 98 77 79% 0.26 0.019 10% 90%

n #loci # P loci % P loci Hj S.E.(Hj) Varl% VarL% Atlantic Canada 31 98 85 88% 0.30 0.016 15% 85% West Greenland 33 98 85 87% 0.29 0.017 14% 86% Iceland 34 98 83 86% 0.30 0.016 14% 86% Barents 35 98 78 81% 0.28 0.017 12% 88%

202 Appendix 16. Chebyshev inequality based on effective densities of 10 for northern and 1 spotted wolffishes (ind'km ). Slopes used for this analysis were based on range-wide FST for northern wolffish and with Barents Sea samples removed for spotted wolffish.

SD Dispersal (km) Prob 1/k2 northern wolffish 1 59 < 1 2 118 < 0.25 3 178 < 0.11 4 237 < 0.06 5 296 < 0.04 spotted wolffish 1 91 < 1 2 182 < 0.25 3 273 < 0.11 4 364 < 0.06 5 455 < 0.04

Appendix 17. Comparative sizes of microsatellite repeat arrays among three wolffish species for loci found to be monomorphic in either spotted or northern wolffishes Atlantic spotted northern Alu\\ (TATC)8-37 (TATC)5 (TATC)4-17 AlulA (TG)10-16 (TG)12 (TG)10-13 Alu29 (GT)6-15 (GT)6-17 (GT)7

203