Duke University Dissertation Template

Population Genetics of Species Associated with Deep-sea Hydrothermal Vents in the

Western Pacific

Andrew David Buchanan Thaler

Marine Science and Conservation Duke University

Date:______Approved:

______Cindy Lee Van Dover, Supervisor

______Robert Vrijenhoek

______Cliff Cunningham

______Jens Carlsson

Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Marine Science and Conservation in the Graduate School of Duke University

2012

ABSTRACT

Population Genetics of Species Associated with Deep-sea Hydrothermal Vents in the

Western Pacific

Andrew David Buchanan Thaler

Marine Science and Conservation Duke University

Date:______Approved:

______Cindy Lee Van Dover, Supervisor

______Robert Vrijenhoek

______Cliff Cunningham

______Jens Carlsson

An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Marine Science and Conservation in the Graduate School of Duke University

2012

Abstract

Genetic diversity, population structure, and connectivity influence interactions among communities and populations. At hydrothermal vents in the western pacific, population structure in vent-associated species could occur at spatial scales ranging from vent sites separated by a few hundred meters to oceanic basins separated by more than 3000 kilometers. The spatial scale of population structure has important conservation implications; species that are well-connected across large geographic regions are more resilient to natural and anthropogenic disturbance. This dissertation examines the genetic diversity, population structure, and connectivity of 3 vent- associated species in the western Pacific. It first presents results from the development of microsatellite primers for Ifremeria nautilei, a deep-sea vent associated snail, then uses mitochondrial COI sequences and a suite of microsatellite markers to examine the broader connectivity of three vent-associated species, Ifremeria nautilei, Chorocaris sp. 2, and Olgasolaris tollmanni, across three back-arc basins in the western Pacific.

Within Manus Basin, no significant genetic differentiation was detected in populations of Ifremeria nautilei (based on COI and microsatellite), Chorocaris sp. 2 (based on COI and microsatellite), or Olgasolaris tollmanni (based on COI). A previously documented low-abundance cryptic species, Chorocaris sp. 1, was detected from a single site, South Su (based on COI). The population of I. nautilei in Manus Basin was found to

be significantly differentiated from a second population that appeared to be panmictic across North Fiji and Lau Basin (based on COI and microsatellites). Chorocaris sp. 2 was also found to be significantly differentiated between Manus and North Fiji Basin (based on COI). Both I. nautilei and Chorocaris sp. 2 showed signs of potential low-level migration between Manus and other southwestern Pacific basins. O. tollmanni was undifferentiated between Manus and Lau Basin (based on COI). It is likely that a variable impedance filter exists that limits the realized dispersal of some, but not all species between Manus Basin and other western Pacific back-arc basins. The presence of a variable filter has implications for the conservation and management of hydrothermal vents in Manus Basin, as it is unclear what effects sustained anthropogenic disturbance will have on isolated populations of I. nautilei and Chorocaris sp. 2.

Dedication

For Amy, my partner on this voyage.

Contents

Abstract ...... iv

List of Tables ...... x

List of Figures ...... xiii

Acknowledgements ...... xv

1. Population structure and connectivity of deep-sea hydrothermal vent communities: an overview ...... 1

2. Characterization of 12 polymorphic microsatellite loci in Ifremeria nautilei, a chemoautotrophic gastropod from deep-sea hydrothermal vents ...... 14

3. The spatial scale of genetic subdivision in populations of Ifremeria nautilei, a hydrothermal-vent gastropod from the southwest Pacific ...... 21

3.1 Introduction ...... 21

3.2 Materials and Methods ...... 28

3.2.1 Geographic setting, sample collection, and DNA extraction ...... 28

3.2.2 COI amplification and analysis ...... 30

3.2.3 Microsatellite methods ...... 31

3.2.4 Common statistical methods ...... 32

3.2.5 Isolation with migration ...... 33

3.3 Results ...... 34

3.3.1 Summary statistics ...... 34

3.3.2 Microsatellite marker quality...... 36

3.3.3 Microsatellite marker identity and excluded markers ...... 37

vii

3.3.4 Population structure within Manus Basin ...... 38

3.3.5 Population structure among Manus, North Fiji, and Lau Basin ...... 43

3.3.6 Estimates of migration, effective population size, and divergence time ...... 45

3.4 Discussion ...... 49

3.4.1 Local population structure within Manus Basin...... 50

3.4.2 Basin-scale population structure ...... 51

3.4.3 Conclusions ...... 54

4. Comparative population genetics of a hydrothermal-vent-associated shrimp, Chorocaris sp.2, and limpet, Olgasolaris tollmanni from southwestern Pacific back-arc basins ...... 56

4.1 Introduction ...... 56

4.2 Materials and Methods ...... 60

4.2.1 Sample collection and DNA extraction ...... 60

4.2.2 COI amplification and analysis ...... 63

4.2.3 Microsatellite genotyping and statistical analyses ...... 65

4.3 Results ...... 66

4.3.1 Shrimp ...... 66

4.3.2 Limpets ...... 78

4.4 Discussion ...... 84

4.4.1 Shrimp population structure ...... 85

4.4.2 Limpet population structure ...... 87

4.4.3 Conclusions ...... 88

Appendix A. Summary Statistics for Ifremeria nautilei microsatellites ...... 92

viii

References ...... 96

Biography ...... 113

List of Tables

Table 1. Polymorphic loci from 71 Ifremeria nautilei individuals sampled from Manus Basin, Papua New Guinea. Concentration of MgCl ([MgCl]), annealing temperature (Ta), size range of alleles (base pairs), number of alleles (Na), observed heterozygosity (Hobs), expected heterozygosity (Hexp), and probability of deviation from Hardy-Weinberg Equilibrium (p) are reported. Significant deviation from Hardy-Weinberg Equilibrium after sequential Bonferroni correction is indicated with an asterisk. Cross-amplification with Alviniconcha type 1 and Alviniconcha type 2 are reported from 3 individuals of each species...... 19

Table 2. Ifremeria nautilei sampling locations from Manus, North Fiji, and Lau Basin. .... 29

Table 3. COI summary statistics for samples of Ifremeria nautilei collected from Manus, North Fiji, and Lau Basin, and divided by patches, mounds, and sites within Manus Basin. N = number of individuals, H = number of haplotypes, Hd = haplotype diversity (standard deviation), Fs = Fu’s Fs with significance (P < 0.05) indicated in bold...... 35

Table 4. Hierarchical analysis of F-statistics from populations of Ifremeria nautilei sampled within Manus Basin using 9 microsatellite loci and a 404-bp region of the COI gene sequence and sampled among Manus, North Fiji, and Lau Basins using 5 microsatellite loci and a 404-bp region of the COI gene sequence. P-values indicated in parentheses...... 41

Table 5. Pairwise comparison of Ifremeria nautilei populations from Manus, North Fiji, and Lau Basins. Pairwise FST from 5 microsatellite loci reported above the diagonal, pairwise φST from 404-bp COI sequences reported below the diagonal. Significant differentiation after correction for multiple tests (P < 0.05) indicated in bold...... 44

Table 6. Best fit models for coalescent analysis of Ifremeria nautilei populations among Manus, North Fiji, and Lau Basin. θ1 = effective size of population 2, θ2 = effective size of population 2, θa = effective size of ancestral population, m1 = migration into population 1 from population 2, m2 = migration in to population 2 from population 1, τ = splitting time between populations. Splitting time is calibrated against two mutations rates: μ1 = 5×10-8, μ2 = 1.5×10-8 adjusted for the number of base pairs in the sequence...... 48

Table 7. Chorocaris spp. and Olgasolaris tollmanni sampling locations from Manus, North Fiji, and Lau Basin...... 62

Table 8. Summary statistics for partial COI gene of two Chorocaris species from Manus and North Fiji Basin. Sequence length for Chorocaris sp. 1 and sp. 2 is 616 base pairs. N is number of individuals, H is number of haplotypes, Hd is haplotype diversity, FS is Fu’s FS...... 68

Table 9. Summary statistics for six microsatellite loci amplified from Chorocaris sp. 2 from Manus Basin. n = number of individuals, a = number of alleles, Rs = allelic richness, HE = expected heterozygosity, HO = observed heterozygosity (bold = significant heterozygote deficiency)...... 74

Table 10. Analysis of Molecular Variance (AMOVA) for Chorocaris sp. 2 partial COI gene sequence from 179 individuals and 6 microsatellite loci from ~270 individuals sampled in Manus Basin, Papua New Guinea and AMOVA for Chorocaris sp. 2 partial COI gene sequence from 188 individuals and in Manus and North Fiji Basin ...... 76

Table 11. Pairwise comparisons of Chorocaris sp. 2 from three sites in Manus Basin. FST from microsatellites above the diagonal, φST from partial COI below the diagonal. Significant values (P < 0.05) indicated in bold. No microsatellites were deployed in North Fiji Basin...... 77

Table 12. Summary statistics for partial COI gene of Olgasolaris tollmanni from Manus and Lau Basin. Sequence length is 476 base pairs. N is number of individuals, H is number of haplotypes, Hd is haplotype diversity, FS is Fu’s FS...... 79

Table 13. Analysis of Molecular Variance (AMOVA) for Olgasolaris tollmanni partial COI gene sequence from 136 individuals in Manus Basin, Papua New Guinea and AMOVA for O. tollmanni partial COI gene sequence from 204 individuals sampled from Manus and Lau Basin...... 81

Table 14. Pairwise φST comparisons of Olgasolaris tollmanni from three sites in Manus Basin and three sites in Lau Basin. No P-values were significant (P < 0.05)...... 82

Table 15. Summary statistics for nine microsatellite loci amplified from populations of Ifremeria nautilei within Manus Basin. n = number of individuals, a = number of alleles, Rs = allelic richness, PA = number of private alleles, HE = expected heterozygosity HO = observed heterozygosity (bold = significant deviation from HWE, * = significant heterozygote excess, † = significant heterozygote deficiency, - = null amplification or monomorphic genotype). Patches that contained only 1 allele not shown...... 92

Table 16. Summary statistics for eight microsatellite loci amplified from populations of Ifremeria nautilei from Manus, North Fiji, and Lau Basin. n = number of individuals, a = number of alleles, Rs = allelic richness, PA = number of private alleles, HE = expected heterozygosity HO = observed heterozygosity (bold = significant deviation from HWE, * = significant heterozygote excess, † = significant heterozygote deficiency, - = null amplification or monomorphic genotype). Patches that contained only 1 allele not shown...... 94

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List of Figures

Figure 1. Location of three hydrothermal vent sites in Manus Basin, Papua New Guinea. Latitude, Longitude, and depths of sampling sites provided...... 13

Figure 2. Ifremeria nautilei sampling locations in the western Pacific. Grey circles are approximate location of sampling. Dashed lines represent subduction zones. Three sites sampled in Manus Basin are shown in inset...... 27

Figure 3. Statistical parsimony network of Ifremeria nautilei haplotypes from Manus, North Fiji, and Lau Basin. Area of circles is proportional to number of individuals that possess each haplotype. Small black circles represent inferred haplotypes not recovered in this data set. Each node represents a one base pair difference between haplotypes. Boxes delineate each putative haplotype group...... 39

Figure 4. Structure inferred two populations of Ifremeria nautilei, finding North Fiji and Lau Basin as one population distinct from Manus Basin. Manus Basin population indicated in black, North Fiji/Lau population indicated in gray. Solid black line denotes division between samples from Manus and North Fiji Basin. Dashed line indicates division between samples from North Fiji and Lau Basin...... 42

Figure 5. Posterior probability densities for migration of Ifremeria nautilei between basins in the western Pacific, based on mitochondrial COI gene region ...... 46

Figure 6. Chorocaris spp. and Olgasolaris tollmanni sampling locations from Manus Basin and approximate sampling locations from North Fiji, and Lau Basins. Nested sites sampled within Manus Basin shown in inset. Grey circles are approximate location of sampling. Dashed lines represent subduction zones. Figure originally published in Thaler et al. (2011) and used with permission...... 61

Figure 7. Maximum-likelihood tree for a subset of Chorocaris spp. sampled from Manus Basin. Sequences are 600-base pairs in length. Substitution model is Tamura 3-parameter determined by Find Best Model application in Mega 5. Chorocaris sp. 1 and Chorocaris sp. 2 indicated by horizontal bars. Chorocaris vandoverae (accession # AF125417), Chorocaris chacei (accession # AF125414), Rimicaris exoculata (accession # FN393000), and Opaepele loihi (DQ328824) presented for comparison. Alvinocaris longirostris (accession # AB222050) used as an outgroup. Bootstrap values greater than 0.50 reported on branches. Scale bar is number of substitutions per base pair...... 69

xiii

Figure 8. Statistical parsimony network for Chorocaris sp. 2 haplotypes from Manus and North Fiji Basin. Large circles represent a single individual unless noted on the figure. Small black circles represent inferred haplotypes not recovered in this data set. Grey circles indicate samples from North Fiji Basin, white circles indicate samples from Manus Basin...... 71

Figure 9. Observed and expected mismatch curves of pairwise mitochondrial COI nucleotide differences for Chorocaris sp. 2 sampled from Manus and North Fiji Basin and Olgasolaris tollmanni sampled from Manus and Lau Basin. Each graph represents a comparison between simulated curves for pairwise nucleotide differences and observed pairwise differences for (A) Chorocaris sp. 2 under a model of constant population size, (B) Chorocaris sp. 2 under a model of population growth and decline, (C) O. tollmanni under a model of constant population size, and (D) O. tollmanni under a model of population growth and decline...... 72

Figure 10. Statistical parsimony network for Olgasolaris tollmanni haplotypes from Manus and Lau Basin. Large circles represent a single individual unless noted on the figure. Small black circles represent inferred haplotypes not recovered in this data set. Grey circles indicate samples from Lau Basin, white circles indicate samples from Manus Basin...... 83

xiv

Acknowledgements

This research was made possible with financial support from:

 The Duke Marine Lab and Duke University

 The Oak Foundation

 InterRidge

 ChEss (Chemosynthetic Ecosystem Science)

 Nautilus Minerals to Cindy Van Dover

 NSF to Cindy Van Dover (OCE-030554)

 NSF to Robert Vrijenhoek (OCE-0241613)

I would like to thank my advisor, Cindy Van Dover for her support and mentorship over the last five years, gin and tonics on the porch, adventures in distant countries, numerous opportunities to go to sea, keeping me on track when my interests strayed, and for the community she has built around a shared love for all things deep and spineless. Many thanks to Robert Vrijenhoek for opening his lab to me and for his guidance. Thanks to Jens Carlsson for introducing me to the world of population genetics and setting me along this path. Special thanks to Cliff Cunningham for sharing his insight into the inner-working of alleles. And thanks to Eric Palkovacs, for broadening my experience beyond the deep sea.

Thanks to the captain and crew of the R/V Cape Hatteras, the M/V Nor Sky,

Canyon Offshore, Rebecca, Pen, Sam, and Alison. Thanks to Lanier, John Wilson, and the Marine Operations Team. Thanks to Jeff and Tom in IT, who saved my data more than once. And thanks to Tom Schultz; I remember when the Marine Conservation

Molecular Facility was just a thermocycler on a folding table.

This journey could only be possible with the support, assistance, and companionship of my fellow Vandovernauts. Thanks to Patrick, Carol, Kevin, William,

Alix, David, Megumi, Josh, Bernie, and Sophie. And, of course, none of this would be possible with the love and support of my family, Mom, Dad, Caroline, and Billy and my soon-to-be family, Joanie and Ralph. Thanks to David; sharks are still sub-par, at best.

Finally, thanks to Amy.

xvi

1. Population structure and connectivity of deep-sea hydrothermal vent communities: an overview

A robust understanding of genetic diversity, population structure, and connectivity provides a framework for assessing population changes following natural and anthropogenic disturbance (Hunter 2008). Genetic diversity strongly correlates with the fitness of a population (Reed & Frankham 2003) and is essential in maintaining the resistance of an ecosystem to disturbance (reviewed in Frankham 2010). Loss of genetic diversity can result in inbreeding depression, which may trigger an extinction vortex, even in seemingly healthy populations, especially in populations with low effective sizes that are prone to genetic drift (Tanaka 2000). Although the International Union for the

Conservation of Nature (IUCN) recognizes that the maintenance of genetic diversity is one of the three essential components for the conservation of biodiversity (along with ecosystem and species biodiversity; McNeely et al. 1990), genetic diversity is largely overlooked in conservation policy and management (Laikre et al. 2010). Many large- scale, international, conservation initiatives have not accounted for genetic monitoring

(i.e. IUCN Redlist of Threatened Species, United Nations Environment Programme’s

World Conservation Monitoring Center, Global Biodiversity Information Facility; Laikre et al. 2010). In rare cases where genetic monitoring is incorporated into management and mitigation regimes, it has been highly successful, revealing otherwise imperceptible changes in demography, distribution, and ecology of at-risk populations (reviewed in

De Barba et al. 2010). For example, high intraspecific genetic diversity was shown to result in enhanced resistance to disturbance in a seagrass ecosystem (Hughes &

Stachowicz 2004). For species in which connectivity among groups of organisms has been limited, the occurrence and frequency of non-selected loci begin to assort independently of other demographic groups, resulting in genetic differentiation and the emergence of genetically distinct populations (Li et al. 2002, Christiakov et al. 2006). The extent of gene flow among populations can be assessed to determine how often and at what scale populations interact. By monitoring genetic diversity, population structure, and connectivity in a system both before and after an anthropogenic disturbance, we can potentially assess changes in population dynamics, loss and recovery of diversity, and ecosystem resilience.

In shallow-water marine ecosystems, population structure is often associated with oceanographic features that form effective dispersal barriers for a variety of taxa.

Cape Hatteras, on the coast of North Carolina, limits the migration of Quahog clams,

Mercenaria mercenaria (Baker et al. 2008). Similar dispersal barriers have been detected around Cape Cod in Massachusetts (Jennings et al. 2009), Cape Canaveral in Florida

(Pelc et al. 2009), and Point Conception in California (Pelc et al. 2009). Even so, panmixia has been documented in numerous, diverse taxa across geographic regions that can span entire oceans (Hellberg 2009). While reproductive mode and dispersal potential often serve as initial predictors of potential population structure, both have been shown to be

poor predictors of genetic differentiation in marine environments (Kelly & Palumbi

2010). High levels of population differentiation at spatial scales of a few kilometers or less has been reported from species with broad dispersal potential (Banks et al. 2007,

Bird et al. 2007, Tatarenkov et al. 2010), whereas surprisingly low levels of population differentiation have been reported in species with reproductive modes that would otherwise be expected to yield fine-scale population sub-division (i.e. direct developing brooders; Miller & Ayre 2008, Blanquer & Uriz 2010). For populations that experience anthropogenic disturbance, the presence of fine-scale population structure suggests that local populations may not be able to recover from disturbance though immigration, reducing the overall resilience of a disturbed ecosystem (Hauser & Carvalho 2008). This phenomenon has been observed in some reef building coral species and their algal endosymbionts that have experienced long-term anthropogenic disturbance (Van Oppen et al. 2006).

Deep-sea hydrothermal vents and their associated seafloor massive sulfide deposits are increasingly being explored for their potential mineral wealth. While deep- sea mining has been discussed for several decades (i.e. Thiel & Schriever 1990), it is only recently that technologic and economic factors have converged to make the possibility of deep-sea mining a reality (Scott 2007; Hoagland et al. 2010). Because deep-sea hydrothermal vents have been largely untouched by commercial ventures in the three- and-a-half decades since their discovery, we are currently in a situation where

management and conservation efforts can precede industrial exploitation and provide an anticipatory, rather than reactionary, assessment of the environmental impacts of an emergent industry.

Seafloor massive sulfides are geologic structures formed by active hydrothermal venting and are found throughout the world at seafloor spreading centers (Herzig &

Hannington 1995). They form when sulfide and metal enriched hydrothermal vent fluid interacts with surrounding cold ocean water, causing particulates to settle out into metalliferous sediment creating large, dense mounds surrounding active hydrothermal vents (Janecky & Seyfried 1984; Mills 1995). Recent estimates suggest there are approximately 900 seafloor massive sulfides distributed throughout the ocean (although that estimate could be as high as 5,000 deposits; Hannington et al. 2011). In some places these mounds have accumulated for thousands of years and form concentrated ore deposits (Moss et al. 1997; Hannington et al. 2010). Where hydrothermal fluids flow through deposits, the sulfide substratum is colonized by chemosynthetic communities fueled by microbial primary production and characterized by abundant, endosymbiont- hosting, invertebrates such as tubeworms, clams, and mussels (reviewed in Van Dover

2000).

The activity of individual vents within a sulfide mound is highly variable

(Tunnicliffe & Juniper 1990). While the entire geologic formation may persist for millennia (i.e. vent field), each discrete vent (chimney) is relatively ephemeral—with a

life-span of dozens to a few hundred years (Lalou & Brichet 1985). New vents continuously emerge and old vents become dormant or are destroyed by volcanic or tectonic processes (Tunnicliffe et al. 1997). The communities associated with these vents have evolved to cope with frequent, often catastrophic, disturbance (Shank et al. 1998; reviewed in Vrijenhoek 1997; 2010).

In ecosystems such as hydrothermal vents, that frequently experience natural disturbance, demographic patterns may be shaped by the frequency and intensity of that disturbance. Genetic population structure may be predominantly influenced by extrinsic factors related to habitat availability and stability rather than intrinsic life-history characteristics such as larval type and duration, adult motility, and endemicity (Wilcox et al. 2006; Lake 2000). Frequent disturbance of patchy ecosystems tends to correlate with increased genetic diversity as well as increased connectivity among populations dependent on those ecosystems (Cohen & Levin 1991; Hughes et al. 2007). Population structure may only be apparent across geomorphological boundaries or at the extremes of species ranges (Vrijenhoek 1997; 2010). Phenomena such as genetic drift and gene flow lead to isolation-by-distance and often shape the population structure of patchy stepping stone ecosystems (Cohen & Levin 1991), but these phenomena may be less prevalent in patchy systems that are subject to frequent disturbance (reviewed in Walker

2012). As a consequence, communities that rely on patchy, ephemeral ecosystems that

experience frequent disturbance may be taxonomically and genetically diverse but lack population structure at broad spatial scales.

The dynamic nature of deep-sea hydrothermal vents provides selective pressure that promotes resistance to ecological disturbance and resilience following catastrophic disturbance, allowing vent-associated communities to recover following disturbance.

Frequent autogenic disturbance shapes the diversity and distribution of hydrothermal- vent endemic communities. Faunal succession following volcanic eruption (Tunnicliffe

& Juniper 1990; Shank et al. 1998), changes in vent fluid composition that preclude colonization by certain species (Desbruyères et al. 2001), and dormancy of hydrothermal activity, can result in local extinction of vent-dependent taxa. Colonization of non-vent taxa that are still be dependent on a chemoautotrophic food web (Erickson et al. 2009) may follow such disturbances. Genetically diverse communities tend to be more resistant to both natural and anthropogenic disturbance and recover faster than less diverse communities (Hughes et al. 2007). While natural disturbance is commonplace, hydrothermal vent ecosystems have, due to their relative inaccessibility, been spared from large-scale anthropogenic disturbance until recently (Glasby 2000). A robust understanding of the existing genetic diversity, population structure, and connectivity of a pre-disturbance vent community is a necessary (though not sufficient) baseline for assessing the recovery of a vent field following anthropogenic disturbance (Hunter 2008;

Van Dover 2010; Hoagland et al. 2010).

Deep-sea hydrothermal vents that occur at mid-ocean ridges, such as the East

Pacific Rise and Mid-Atlantic Ridge, are distributed along linear axes that can function as dispersal corridors (Marsh et al. 2001, Thomson et al. 2003, Young et al. 2008).

Considerable evidence exists for geographic subdivision associated with geomorphological features along the East Pacific Rise, although the degree to which these features influence population structure varies depending on the species.

Bathymodiolus mussels on the East Pacific Rise are divided into northern and southern populations associated with the Easter Microplate (Won et al. 2003) but Alvinella pompejana and Branchipolynoe symmytilida, two polychaete species with a similar distribution do not show evidence for significant population differentiation across the same boundary (Hurtado et al. 2004). Two other polychaete species, Riftia pachyptila and

Tevnia jerichonana, display reduced gene flow across the Easter Microplate (Hurtado et al.

2004, although Coykendall et al. 2010 suggest that this interpretation may be incorrect).

Further north, populations of Lepetodrilus limpets diverge across the Blanco Transform

Fault, a 450-km long ridge offset, that separates the Juan de Fuca and Gorda ridges

(Johnson et al. 2006), while unidirectional gene flow was detected in a vent tube worm,

Ridgeia piscesae, across the same boundary (Young et al. 2008).

Relative to the East Pacific Rise, population structure on the Mid-Atlantic Ridge has been assessed for fewer species. Rimicaris exoculata, an abundant shrimp on the Mid-

Atlantic Ridge, was shown to be panmictic, with no genetic differentiation detected

across a range of 7,100 kilometers (Creasey et al. 1996, Teixera et al. 2010, 2012). Temporal estimates of genetic structure among Rimicaris exoculata suggest that the species has undergone a recent expansion or re-colonization of the Mid-Atlantic Ridge (Teixera et al.

2011). Populations of a mussel, Bathymodiolus puteoserpentis, are also well-connected

(Maas et al. 1999) but they form a hybrid zone with Bathymodiolus azoricus, a related species common at vents along the northern Mid-Atlantic Ridge. Limited genetic exchange appears to exist outside of the hybrid zone (O’Mullan et al. 2001). A related, but undescribed, complex of two Bathymodiolus species from south of the equator was shown to be divergent from B. puteoserpentis and B. azoricus, but these four mussel species form a monophyletic group that diverged from the northern Atlantic B. boomerang (van der Heijden et al. 2012). The vesicomyid clam, Abyssogena southwardae, exhibits connectivity between northern and southern populations and suggests north-to- south directional gene flow (van der Heijden et al. 2012).

In contrast to mid-ocean ridge systems, Pacific back-arc basins are distributed in a non-linear pattern, reflecting the complex tectonic history of the region (Desbruyères et al. 2006). Hydrothermal vents in western Pacific back-arc basins are geographically isolated from vents on the East Pacific Rise (Van Dover et al. 2002), with dramatically different faunal composition (Bachraty et al. 2009). Whereas hydrothermal vents in the eastern Pacific are characterised by the deep-sea tube worm, Riftia pachyptila, and vents on the Mid-Atlantic Ridge feature swarms of Rimicaris exoculata shrimp, western Pacific

vents are dominated by large, provannid gastropods and Bathymodiolin mussels

(Desbruyères et al. 2006, Bachraty et al. 2009). Evidence also exists for regional isolation of species among western pacific vents (Desbruyères et al. 2006). The Okinawa Trough and Izu-Ogasawara Arc have a distinct faunal assemblage from that of other western

Pacific hydrothermal vents, and Marianna Trough communities are distinct from those of neighbouring basins (Desbruyères et al. 2006). In the southwestern Pacific, many vent species shared among Manus, North Fiji, and Lau Basin are distinct from related species that occur in the northwestern Pacific, such as the Okinawa and Marianna Troughs or the Izu-Ogasawara Arc (Desbruyères et al. 2006).

It has been hypothesized that, because back-arc basin hydrothermal systems in the western Pacific are located on isolated ridge segments, this potential for reduced connectivity may yield more endemic vent fauna within discrete back-arc basins (Van

Dover 2000). Several species associated with western Pacific basins, such as the mussel

Bathymodiolus brevior (Kyuno et al. 2009), appear to be undifferentiated across multiple basins, while other species are restricted to single basins (e.g., neoverrucid barnacles;

Watanabe et al. 2005). Provannid snail species in the genus Alviniconcha comprise at least five evolutionary lineages, one that occurs at hydrothermal vents in North Fiji Basin, one that is restricted to vents in the Marianna Trough, two that co-occur in both Manus and

North Fiji Basin (Kojima et al. 2001), and one that has been identified, but not fully described, from the Indian Ridge (Kojima et al. 2004; Suzuki et al. 2005)—a hydrothermal

vent system that, while geographically distant from the western Pacific, appears to represent the same biogeographic province (Bachraty et al. 2009).

Studies of population structure within species associated with hydrothermal vents in the southwestern Pacific have focused on endosymbiont-hosting species including provannid gastropods and mytilid mussels (Desbruyères et al. 2006). Several endosymbiont-hosting species appear to be panmictic across discrete vent fields (i.e.

Bathymodiolus spp.: Moraga et al. 1994; Kyuno et al. 2009; Alviniconcha spp.: Kojima et al.

2001; Suzuki et al. 2006a; Ifremeria nautilei: Thaler et al. 2011). Intra-basin genetic structure among secondary consumers (those that do not host chemoautotrophic endosymbionts) occurring at western Pacific hydrothermal vents has only been assessed for populations within oceanic basins. Lepetodrilus schrolli, a vent-associated limpet (Johnson et al. 2008), and three species of vent-associated copepods, Stygiopontius brevispina, S. lauensis, and S. hispidulus, were undifferentiated throughout Lau Basin (Gollner et al. 2010).

Manus Basin is a southwestern Pacific back-arc basin spreading center located within the exclusive economic zone of Papua New Guinea (Figure 1). Numerous, well- studied, massive sulfide mounds are distributed throughout Manus Basin, including

Pacmanus (Binns & Scott 1993), Desmos Caldera (Gena et al. 1997), Vienna Wood (Tufar

1990), and SuSu Knoll (Scott & Binns 1995) which contains the Solwara I mining prospect (formerly known as Suzette vent field; Coffey Natural Systems 2008). Seafloor massive sulfide deposits in Manus Basin are among the largest known in the world, and

fields of active hydrothermal vents may cover several acres (Hannington et al. 2011).

Formation of sulfide deposits in Manus Basin began approximately 15 Ma with the subduction of the Pacific Plate beneath the Bismarck Plate, forming the Manus Trench

(Martinez & Taylor 1996). The eastern-most sites, (including SuSu Knoll and Solwara I) are among the youngest formations—rifting began approximately 3.5 Ma (Scott & Binns

1995). The St. Georges Undercurrent, which enters the basin from the southeast and exits in the northwest, connects the deep benthos of Manus Basin to surrounding oceanographic regions (Zenk et al. 2005).

Hydrothermal vent communities in Manus Basin are characterized by several biomass-dominant habitat builders, including abyssochrysoid snails in the genera

Ifremeria and Alviniconcha (Galkin 1997), and the bathymodiolin mussel, Bathymodiolus manusensis (Hashimoto & Furuta 2007). These foundation species, which host chemoautotrophic endosymbionts that generate organic carbon through sulfide oxidation, must live in close proximity to hydrothermal vent effluent (reviewed in Van

Dover 2000). These mollusks are often distributed around diffuse-flow sites in a concentric pattern along a temperature and chemical gradient, with Alviniconcha in closest proximity to the vent effluent, followed by I. nautilei, and B. manusensis

(Desbruyères et al. 2006; Podowski et al. 2009). The limpet, Olgasolaris tollmanni (Beck

1992), is often associated with I. nautilei, while B. manusensis hosts commensal polychaetes in the genus Branchipolynoe (Desbruyères et al. 2006). The barnacle,

Eochionelasmus ohtai, is found around the periphery of active vents (Galkin 1997) while stalked barnacles in the genus Vulcanolepas are found on rocky outcrops near dormant vent chimneys (Southward & Jones 2003; Erickson et al. 2009). Highly mobile shrimp in the genus Chorocaris occur in dense aggregations around hydrothermal vent plumes

(Galkin 1997). These eight species are among the most abundant metazoan organisms at

Manus Basin hydrothermal vents (Collins et al. 2012). The vent assemblages in Manus

Basin includes several other moderately abundant species, including additional gastropods, squat lobsters, crabs, other scale worms, vestimentiferan tube worms, sponges, anemones, and numerous low-abundance taxa (Galkin 1997; Collins et al. 2012; reviewed in Desbruyères et al. 2006).

There is currently a knowledge gap concerning our understanding of population structure and connectivity among vent-associated species in the southwestern Pacific.

This dissertation focuses on assessing the spatial scale and extent of population structure among three vent-endemic organisms (Ifremeria nautilei, Chorocaris sp. 2, and Olgasolaris tollmanni) from three sites (Solwara 8, Solwara 1, and South Su; Figure 1) in Manus

Basin, and on establishing patterns of population structure and connectivity among populations in Manus Basin and two other western Pacific back-arc basins, North Fiji and Lau Basin. One hydrothermal vent field in Manus Basin, Solwara I, is projected to become the first extracted seafloor massive sulfide. A nearby site, South Su (2.5 km distant), is designated as a reserve during the extraction of Solwara I. A third site,

Solwara 8, is ~40-kilometers distant from both Solwara I and South Su and is outside the current Solwara I mining lease (Coffey Natural Systems 2008), but is within the exploration lease.

Figure 1. Location of three hydrothermal vent sites in Manus Basin, Papua New Guinea. Latitude, Longitude, and depths of sampling sites provided.

2. Characterization of 12 polymorphic microsatellite loci in Ifremeria nautilei, a chemoautotrophic gastropod from deep-sea hydrothermal vents

This chapter has been published as:

Thaler AD, Zelnio KA, Jones R, Carlsson J, Van Dover CL, Schultz T (2010)

Characterization of 12 polymorphic microsatellite loci in Ifremeria nautilei, a

chemoautotrophic gastropod from deep-sea hydrothermal vents. Conservation

Genetics Resources. doi:10.1007/s12686-010-9174-9.

Authors' contributions

CLVD, JC, TFS, and ADT conceived the study; ADT, TFS and RJ collected samples; ADT, KZ, RJ, JC, and TFS undertook molecular benchwork and analyses; ADT,

KZ, and CLVD drafted the manuscript. All authors have read and approved the final manuscript.

Deep-sea hydrothermal vents are geographically discrete habitats that occur throughout the ocean at plate margins and in other volcanically active settings. Biomass- dominant taxa endemic to vent systems are key targets for understanding how populations are maintained and structured at vents (e.g., Hurtado et al. 2004, Shank & 14

Halanych 2007, Fusaro et al. 2008). Polymetallic ore deposits of commercial grade are often associated with hydrothermal vents, making some vent systems candidates for deep-sea mining operations (Rona 2003). Defining the scale of natural conservation units of key vent-endemic organisms can inform best practices for minimizing the environmental impact of mining activities (Halfar & Fujita 2002).

Ifremeria nautilei is a deep-sea provannid gastropod endemic to hydrothermal vents in the western Pacific (Bouchet & Warén 1991). It and species in the related genus

Alviniconcha are the dominant primary consumers at geographically isolated back-arc basins (e.g., Manus, Fiji, Lau; Desbruyères et al. 2006), are dependent on chemoautotrophic endosymbiotic Proteobacteria for their nutrition (Suzuki et al. 2006b) and create habitat for a diverse assemblage of other vent organisms (Urakawa et al.

2005). The ecological importance of I. nautilei in vent communities and its broad geographic distribution makes it an ideal model for assessing spatio-temporal population dynamics at hydrothermal-vent ecosystems in the western Pacific.

Ifremeria nautilei DNA was enriched for microsatellite-containing motifs using the enrichment protocol of Glenn & Schable (2005). Genomic DNA was isolated from ethanol-preserved foot tissue of I. nautilei by Chelex-Proteinase K extraction. Tissue (10 –

30mg) was digested with 120mg Proteinase K (Bioline: Taunton, MA) in 600µl 10%

Chelex-100 resin (Bio-Rad: Hercules, CA) overnight at 60oC, heated to 100oC for 15 minutes to inactivate Proteinase K, and centrifuged at 10,000 rpm for 5 minutes.

Supernatant was digested with RsaI, ligated to SNX linkers, hybridized with

Biotinylated oligonucleotide probes (Mix 2 and Mix 3, Glenn & Schable 2005), bound to

Streptavidin magnetic beads, and amplified with SNX-F primer. Enriched DNA was cloned with a Topo TA cloning kit (Invitrogen: Carlsbad, CA) into α-Select electrocompetent cells (Bioline: Taunton, MA). Inserts from 3000 clones were sequenced on an ABI 3730xl DNA Analyzer (Applied Biosystems: Foster City, CA). Clone sequences containing repetitive elements were located using Msatfinder (Thurston &

Field 2005) and flanking primers were designed with Primer3 software (Rozen &

Skaletsky 2000). A T3-tail (5'-ATTAACCCTCACTAAAGGGA-3') was added to the 5' end of each forward primer as an attachment site for FAM-labeled (6-carboxy- fluorescine) T3- primer (Schuelke 2000).

Genomic DNA was diluted 10:1 and used as a template in 20µl polymerase chain reactions (PCRs). Reactions were prepared as follows: 2µl template, 2µl 10x PCR Buffer

(200mM Tris, pH 8.8; 500mM KCl; 0.1% Triton X-100, 0.2mg/ml BSA), 2µl MgCl (final concentrations indicated in Table 1), 1µl 2.5mM dNTP's, 0.2µl 10µM T3-labeled forward primer, 0.8μl 10μM reverse primer, 0.8μl 10μM 5’-FAM-labeled T3 primer (Eurofins:

Huntsville, AL), and 0.2µl Taq polymerase (1 unit, Bioline: Taunton, MA). Amplification reactions were run on Eppendorf Mastercyclers (Westbury, NY) using the following protocol: 95oC 4 minutes, 25 cycles of 94oC 45 seconds, TAoC 15 seconds (annealing temperatures as indicated in Table 1), 72oC 45 seconds; followed by 8 cycles of 94oC 45

seconds, 53oC 30 seconds, and 72oC 45 seconds (to label the PCR products with FAM- labeled T3 primer), and a final extension step of 72oC for 10 minutes.

PCR products were diluted 1:10 and 1µl was mixed with 9µl diluted LZ500 size standard (0.05µl LZ500 and 8.9µl water per lane; MCLab, San Francisco CA). Dilute products and size standards were denatured at 95oC for 10 minutes prior to size- fragment analysis on an ABI 3730xl DNA analyzer (Applied Biosystems, Foster City,

CA). Chromatograms were imported into Genemarker v1.8 (SoftGenetics LLC: State

College, PA) and scored.

Deviation from Hardy-Weinberg Equilibrium (HWE), heterozygote excess and deficiency, and linkage disequilibrium was tested with Genepop version 4.0.10 (using default settings implemented in the program, Raymond & Rousset 1995) and sequential

Bonferroni was used to correct for multiple comparisons (Rice 1989). Presence of null alleles, stutter, and large allele dropout was assessed using MicroChecker (van

Oosterhout et al. 2004, 1000 randomizations). LOSITAN (using 15,000 simulations) was used to screen for loci potentially under selection (Antao et al. 2008).

Approximately 2,500 clones were screened, from which 105 primer pairs were developed and tested. Twelve loci were found to be polymorphic, yielding reproducible alleles. All 12 loci were assessed on 71 Ifremeria nautilei individuals from Manus Basin.

Four loci (Ifr20A, Ifr25, Ifr73, and Ifr94) deviated significantly from HWE (Table 1) but only three (Ifr20A, Ifr25, and Ifr73) deviated significantly after correction for multiple

tests (sequential Bonferroni correction, k = 12). Four loci (Ifr20A, Ifr25, Ifr73, and Ifr93) showed significant homozygosity excess (P < 0.05) loci and remained significant after correction for multiple tests (k =12). Micro-checker indicated the presence of null alleles at 4 loci (Ifr20A, Ifr25, Ifr73, and Ifr93), and two loci (Ifr20A and Ifr73) showed evidence of scoring errors due to stutter. No linkage disequilibrium between loci was detected nor did LOSITAN indicate selection at any locus.

All 12 markers were tested for cross amplification in two phylotypes of

Alviniconcha (Alviniconcha type 1 and Alviniconcha type 2; Kojima et al. 2001) using reaction conditions optimized for Ifremeria nautilei. Three loci (Ifr78, Ifr94, and Ifr103) showed positive cross-amplification. Among these loci, only Ifr103 showed 100% cross amplification in all tested individuals of both Alviniconcha species.

To our knowledge, this is the first report of microsatellite markers developed for a deep-sea vent gastropod; microsatellite markers have been developed for three other deep-sea hydrothermal-vent-endemic organisms (Riftia pachyptila: Fusaro et al. 2008;

Branchipolynoe seepensis: Daguin & Jollivet 2005; and a single marker from Bathymodiolus childressi: Carney et al. 2006). I. nautilei microsatellite markers are being deployed on populations within Manus Basin to investigate fine-scale (2m to 40km) genetic structure as well as to explore connectivity among Manus, Fiji, and Lau Basins (>2000km). In addition, the data generated will establish a baseline of genetic diversity against which genetic changes caused by mining or other disruptive events may be assessed.

Alviniconcha species [MgCl] allele size Locus Repeat Motif Primer sequence (5'-3') Ta (oC) Na Hobs Hexp p cross amplification - (mM) range (# positive/# total) F: 143 – 181 Alviniconcha type 1 – 0/3 Ifr20A (TC)7 1.0 62.0 14 0.41 0.86 0.000* GCGAGCGCTCATATACATGC R: Alviniconcha type 2 – 0/3

GCCCAGTCACCCACTATGGG 19 F: 147 – 290 Alviniconcha type 1 – 0/3 Ifr25 (ATGTT)20 TCGAACGACAACCTTAATTTC 2.0 60.5 28 0.70 0.92 0.000* C R: Alviniconcha type 2 – 0/3

CTTACACTGTCCCGTCATCG F: 202 – 218 Alviniconcha type 1 – 0/3 Ifr40 (CAAA)6 1.0 56.0 3 0.51 0.52 0.611 GTTCTGTCATGTGACAGATGC R: AGGCCAACCGTTTAACCAC Alviniconcha type 2 – 0/3 F: 174 – 200 Alviniconcha type 1 – 0/3 Ifr43 (AG)7 2.0 62.0 6 0.62 0.66 0.088 CTGCAACTGAAACCCACAAG R: Alviniconcha type 2 – 0/3

TCAAGTGTGCTCCAAGCATC F: 217 – 226 Alviniconcha type 1 – 0/3 Ifr52 (TTG)7 2.0 64.0 3 0.63 0.57 0.700 AGTGATAGGCCTGGTGTTCC R: Alviniconcha type 2 – 0/3 GACTAGGAACTCCCCACAAG C F: 221 – 231 Alviniconcha type 1 – 0/3 Ifr68 (TG)8 2.0 62.3 4 0.63 0.64 0.792 CGAATGACCCTGCAACTGTA

R: GCTGGTCTTGTTTTCCAAGC Alviniconcha type 2 – 0/3 F: 249- 253 Alviniconcha type 1 – 0/3 Ifr73 (ACTA)4 2.0 59.5 2 0.06 0.18 0.000* CATGTCAGCCCTGATGTATCC R: Alviniconcha type 2 – 0/3

TGCTCTTTTGAATGTCCTTCG Ifr78 F: 246 – 250 Alviniconcha type 1 – 3/3 (GT)6 2.0 60.5 5 0.41 0.48 0.321 CGTAAGCTTAGCCGGACTGG R: Alviniconcha type 2 – 2/3

CAAACAAACGTCCATGATGC F: 227 – 231 Alviniconcha type 1 – 0/3 Ifr86 (AG)9 2.0 56.0 3 0.30 0.32 0.459 CCCCTCTATGTCACCAGTCG R: Alviniconcha type 2 – 0/3

GAAGAAGGCGTCAGAAATGG Ifr93 (TG)5 (CT)10 F: ATCTGGCTGGTCTGTCTTGG 2.0 62.3 252 – 283 20 0.80 0.91 0.130 Alviniconcha type 1 – 0/3

20 R: Alviniconcha type 2 – 0/3

GGCTACATAACGCAGTGAGG Ifr94 (CAAA)3 F: TTGAGTCGACAGCCAGACC 2.0 63.5 258 – 279 5 0.30 0.35 0.050 Alviniconcha type 1 – 2/3 R: Alviniconcha type 2 – 2/3

CTACTGCCGGTCTTTGATGG F: 222 – 364 Alviniconcha type 1 – 3/3 Ifr103 (TTAG)3 TTGACAGAAACTTTGGTGTTG 2.0 60.5 4 0.51 0.53 0.488 G R: Alviniconcha type 2 – 3/3

CACTATTGCAGGCTGACTGG

3. The spatial scale of genetic subdivision in populations of Ifremeria nautilei, a hydrothermal-vent gastropod from the southwest Pacific

This chapter has been published as:

Thaler AD, Zelnio K, Saleu W, Schultz TF, Carlsson J, Cunningham C, Vrijenhoek R,

Van Dover CL (2011) The effects of spatial scale on the population dynamics of

Ifremeria nautilei, a hydrothermal vent endemic gastropod from the southwest

Pacific. BMC Evolutionary Biology, 11(1): 372

Authors' contributions

CLVD, JC, TFS, and ADT conceived the study; ADT, TFS and RCV collected samples; ADT, KZ, WS, JC, and TFS undertook molecular benchwork and analyses;

ADT, CC, RCV, KZ, and CLVD drafted the manuscript. All authors have read and approved the final manuscript.

3.1 Introduction

The spatial scales at which individuals within a population interact and the geographic extent of larval dispersal shape the dynamics of marine populations.

Dispersal capabilities of some species extend across entire ocean basins (Scheltema

1986), but larval propagules of many other species are retained close to their source

(Warner & Cowen 2002). Larval development can impose limits on dispersal. Species that brood their offspring (direct development) tend to have more restricted distributions than species with long-lived, planktonic larvae (Kelly & Palumbi 2010), though exceptions exist (Ayre et al. 2009). Species that aggregate in small patches may interact and reproduce with other individuals in an area encompassing a meter or less

(Kinlan et al. 2005) and larvae that lack broad dispersal potential may recruit to their natal population (Jones et al. 1999). A sampling scheme that fails to account for the localized effects of self-recruiting patches may create an appearance of panmixia, even if substructure exists among patches (Swearer et al. 2002).

Species dependent on deep-sea hydrothermal vents are restricted to patchy, ephemeral habitats that limit the areal extent and occurrence of populations.

Hydrothermal vent fields are found on mid-ocean ridges, back-arc spreading centres, and submarine volcanoes (Tunnicliffe & Fowler 1996). Organisms that thrive at vents are supported by chemoautotrophic microbes that metabolize reduced compounds in the vent effluent (Cavanaugh 1994). Vent habitats are transient, at temporal scales ranging from days to hundreds of years (Tunnicliffe a& Juniper 1990), and constituent species may be subject to frequent local extinction and recolonization events (Tunnicliffe et al.

1997, Shank et al. 1998). Survival of vent species therefore depends on fast growth, rapid

reproduction, and dispersal abilities that shape the diversity and genetic structure of populations (Vrijenhoek 1997, Vrijenhoek 2010).

At mid-ocean ridges, deep-sea hydrothermal vents are distributed along roughly linear axes that may function as dispersal corridors (Marsh et al. 2001, Thomson et al.

2003, Young et al. 2008). Geographic populations of hydrothermal vent-dependent species can be panmictic across the extent of their range (e.g., the shrimp, Rimicaris exoculata on the Mid-Atlantic Ridge Creasey et al. 1996, Shank et al. 1998, Teixera et al.

2010) but this is not always the case.

Evidence for isolation-by-distance in vent species has sometimes been ambiguous due to small sample sizes and inconsistency in the resolution of various genetic markers (Audzijonytė & Vrijenhoek 2010). Considerable evidence exists for geographic subdivision associated with geomorphological features that affect different taxa to varying degrees. For example, the Easter Microplate is associated with isolation of northern and southern East Pacific Rise populations of mussels, but not of polychaete annelids (Won et al. 2003). A 2000-m long “habitat gap” across the Equator is implicated in the isolation of some East Pacific Rise species and variable impedance of gene flow in other species (Hurtado et al. 2004, Plouviez et al. 2009). Similarly, a 350-km long ridge offset, the Blanco Transform Fault, isolates Juan de Fuca and Gorda ridge limpet populations (Johnson et al. 2006). The same barrier interacts with current regimes and is correlated with southward unidirectional gene flow in the vent polychaete Ridgeia

piscesae (Young et al. 2008). Life history and behavioral attributes of various taxa result in these differing responses to shared dispersal barriers (Vrijenhoek 2010).

Identification of population structure at various spatial scales depends in part on the choice of genetic markers. For example, amplified fragment length polymorphisms were used to test for fine-scale differentiation among discrete patches of the tubeworm,

Riftia pachyptila, separated by as little as 400 m in a venting area along the East Pacific

Rise, although sample sizes were small (n < 15 per site; Shank & Halanych 2007). More conservative mitochondrial and nuclear DNA sequences in R. pachyptila revealed panmixia at local scales and isolation-by-distance (Wright 1943) at greater geographical scales (Black et al. 1994, Coykendall et al. 2011).

In contrast to mid-ocean ridge systems, limited attention has been afforded to the population structure of vent organisms from western Pacific back-arc basins. These basins are distributed in a non-linear pattern, reflecting the complex tectonic history of the region (Desbruyères et al. 2006). Hydrothermal vents in western Pacific back-arc basins are geographically isolated from vents on the East Pacific Rise (Van Dover et al.

2002). Regional isolation of species was detected among western pacific vents

(Desbruyères et al. 2006): the Okinawa trough and Izu-Ogasawara Arc have a faunal assemblage distinct from that of other western Pacific hydrothermal vents, and the faunal composition of the Marianna Trough is distinct from that of neighbouring basins

(Desbruyères et al. 2006). Vent species tend to be shared among Manus, North Fiji, and

Lau Basin, but are distinct from species that occur at the Okinawa and Marianna

Troughs or the Izu-Ogasawara Arc (Desbruyères et al. 2006).

Because back-arc basin hydrothermal systems in the western Pacific are located on isolated ridge segments (in contrast to the linear, semi-continuous series of segments on mid-ocean ridges), it has been hypothesized that reduced connectivity among western Pacific back-arc basins may yield more endemic vent fauna within discrete back-arc basins (Van Dover 2000). Some species endemic to these basins appear to be panmictic across multiple basins (e.g., the mussel Bathymodiolus brevior; Kyuno et al.

2009), whereas others are restricted to single basins (e.g., neoverrucid barnacles;

Watanabe et al. 2005). Provannid snail species in the genus Alviniconcha represent a cryptic species complex composed of at least three evolutionary lineages, one that occurs at hydrothermal vents in North Fiji Basin, one that is restricted to vents in the Marianna

Trough, and one that co-occurs in both Manus and North Fiji Basin (Kojima et al. 2001).

A similar pattern of strong genetic differentiation may exist within other species. To date, comprehensive efforts have not been made to characterize population structure within vent taxa of western Pacific back-arc basins.

Ifremeria nautilei is a provannid gastropod that occurs in Manus, North Fiji, and

Lau Basins and depends on sulphur-oxidizing bacterial endosymbionts for nutrition.

Sessile adults live in discrete patches near the effluent of diffuse-flow hydrothermal vents (Bouchet & Warén 1991, Desbruyères et al. 2006). Females possess a specialized

brood-pouch in their foot and they release ciliated pre-veliger larvae (Warén’s larvae) that are hypothesized to have long-distance dispersal capabilities (Reynolds et al. 2010).

Preliminary studies indicated that I. nautilei exhibits distinct mitochondrial haplotypes in Manus and North Fiji Basins (Kojima et al. 2000), but population structure has not been assessed at smaller spatial scales—among vent fields within basins (henceforth sites), among sulphide mounds within vent fields (henceforth mounds), or among discrete patches on vent mounds (henceforth patches).

We examined genetic population structure of Ifremeria nautilei from hydrothermal vents in Manus, North Fiji, and Lau Basins at multiple scales, ranging from meters to thousands of kilometres (Figure ). A nested sampling strategy was employed within Manus Basin to test the null hypothesis that I. nautilei exhibits no population structure among discrete patches at spatial scales of meters to 40 kilometres.

The entire Manus Basin population was then compared to North Fiji and Lau basin samples to assess the relationship between increasing spatial scales (1000 kilometres to

3500 kilometres) and genetic differentiation. Genetic markers for differentiation at these scales included partial sequences of mitochondrial cytochrome-c-oxidase subunit I, and an array of nuclear DNA microsatellite loci (Thaler et al. 2010). By comparing these two types of molecular markers, we can separate evolutionary processes, revealed by COI sequence data and dependent on mutation rates, from ecologic processes, revealed by microsatellite allele frequencies and based on the recombination of alleles with each

generation (Hellberg 2009). If the specialized Warén’s larvae produced by Ifremeria nautilei are adapted for long-distance dispersal (Reynolds et al. 2010), population structure should be minimal over all scales. Alternatively, if I. nautilei disperse in a manner consistent with other sessile invertebrates with specialized habitat needs

(Palumbi 2004) genetic differentiation may occur at spatial scales less than one kilometre.

3.2 Materials and Methods

3.2.1 Geographic setting, sample collection, and DNA extraction

Ifremeria nautilei were collected from three hydrothermal vent sites in Manus

Basin: Solwara 8, Solwara 1, and South Su (Figure 2). One to four patches of I. nautilei were sampled from three mounds at each site (Figure 2). Samples were collected during

June-July 2008 with an ST212 trenching ROV modified for biological sampling. Foot tissue was preserved in 95% ethanol. Additional I. nautilei samples were acquired from a cruise that occurred during May-June 2005 with ROV Jason II from North Fiji and Lau

Basins (Figure 2). Foot tissue was stored briefly at -20oC and transferred to 95% ethanol prior to DNA extraction. Genomic DNA was isolated by Chelex-Proteinase-K extraction as described in Thaler et al. (2010) and extracted DNA was stored at 4oC until amplification.

Table 2. Ifremeria nautilei sampling locations from Manus, North Fiji, and Lau Basin.

Basin Site Mound Latitude Longitude Depth (m) Manus Solwara 8 Active 1 3° 43.740'S 151° 40.404'E 1720 Active 2 3° 43.824'S 151° 40.458'E 1710 Active 3 3° 43.668'S 151° 40.872'E 1650 Solwara 1 Active 4 3° 47.436'S 152° 5.472'E 1530 Active 5 3° 47.370'S 152° 5.778'E 1490 Active 6 3° 47.370'S 152° 5.616'E 1480 South Su Active 7 3° 48.564'S 152° 6.144'E 1300 Active 8 3° 48.492'S 152° 6.186'E 1350 Active 9 3° 48.432'S 152° 6.306'E 1320 North Fiji White Lady 16° 59.950'S 173° 54.950'E 1971 White Rhino 16° 59.950'S 173° 54.950'E 1971 Mussel Hill 16° 59.950'S 173° 54.950'E 1971 Lau Kilo Moana 20° 03.230'S 176° 08.010'W 2620 Tu'i Malila 20° 59.350'S 176° 34.100W 1884

3.2.2 COI amplification and analysis

The mitochondrial COI (404-bp segment) region was amplified with the species- specific primers COI-3 and COI-6 (Kojima et al. 2000) as follows: 10-100 ng DNA template, 10x PCR Buffer (20 mM Tris, pH 8.8; 50 mM KCl; 0.01% Triton X-100; 0.02 mg/ml BSA), 2 mM MgCl2, 0.2 mM dNTP's, 0.5 µM each primer, and 1 unit Taq polymerase (Bioline: Taunton, MA) in a 20 µl final volume. Reaction conditions were as follows: 94oC for 1 minute; 30 cycles of 92oC for 40 s, 50oC for 60 s, 72 oC for 90 s; final extension of 72oC for 5 min. Amplicons were verified on 1.8% agarose gels. To remove unincorporated nucleotides, 14 µl of PCR product was incubated with 0.2µl 10 X ExoAP buffer (50 mM Bis-Tris, 1 mM MgCl2, 0.1 mM ZnSO4), 0.05µl Antarctic Phosphatase

(New England Biolabs: Ipswich, MA), 0.05µl Exonuclease I (New England Biolabs:

Ipswich, MA) at 37oC for 60 min followed by 85oC for 15 min to inactivate the enzymes.

Bi-directional sequencing reactions were performed using the manufacturer’s protocol for Big Dye Terminator Reaction (Applied Biosystems: Foster City, CA). Sequenced PCR product was purified using AMPure magnetic bead system (Agencourt: Morrisville,

NC) following manufacturer’s protocol, analyzed on an ABI 3730xl DNA Analyzer

(Applied Biosystems International), and edited with Sequencher version 4.7 (Gene

Codes: Ann Arbor, MI). Consensus sequences were compared against the NCBI

GenBank database to confirm species identity (Benson 2005) and aligned using the

MUSCLE alignment algorithm (Edgar 2004). A sequence for each unique haplotype was

deposited in GenBank (North Fiji and Lau haplotypes – accession # JQ074110 to

JQ074134; Manus Basin haplotypes – accession # JQ074135 to JQ074170).

Neighbor-joining phylograms of aligned mitochondrial sequences were assembled in MEGA version 4 (Tamura et al. 2007) with an Alviniconcha sp. 2 as an outgroup. Statistical-parsimony networks were assembled in TCS version 1.21 (default settings; (Clement et al. 2000). Arlequin version 3.11, (Excoffier et al. 2005) was used to estimate haplotype (H), nucleotide diversity (π), Fu’s Fs, and pairwise φST.

3.2.3 Microsatellite methods

Nine microsatellite markers (Ifr040, Ifr043, Ifr052, Ifr068, Ifr078, Ifr086, Ifr093,

Ifr094, and Ifr103) were amplified from Manus, North Fiji, and Lau Basin samples following methods reported in Thaler et al. (2010). To assess marker quality, allelic richness and divergence from Hardy-Weinberg Equilibrium (HWE) were calculated in

GENEPOP (version 4.0; Rousset 2008). Permutation tests to determine significant variation in allelic richness were conducted in F-stat (version 2.9.3.2; Goudet 1995).

Departures from HWE toward heterozygote excess or deficiency were assessed for each locus using GENEPOP exact tests. Loci were screened using LOSITAN to test for the potential influence of selection (25,000 simulations; Antao et al. 2008). Microsatellite markers that showed deviations from HWE expectations or found to be under the influence of selection were excluded from subsequent analyses. Identity tests (PID and

PSIB) were used to indicate whether a given set of microsatellites contains sufficient information to be useful for assessing population structure (Woods et al. 1999, Taberlet &

Luikart 1999). PID and PSIB were calculated for all useful sets of microsatellite markers

(Gimlet version 1.3.3; Valière 2002).

3.2.4 Common statistical methods

Arlequin version 3.11 was used to conduct analysis of molecular variance

(AMOVA). HIERFSTAT (Goudet 2005, de Meeûs & Goudet 2007) was used to assess hierarchical φST and FST at various nested scales from patch to basin. Microsatellite

Analyser (MSA; Dieringer and Schlötterer 2003) was used to identify significant differentiation between patches, mounds, sites, and basins. Alpha levels were adjusts via

Sequential Bonferroni correction to account for multiple tests (Rice 1989). Structure version 2.3.3 (Pritchard et al. 2000) was used to visualize population structure. We used an admixture model with no a priori sample data and with sampling locations as prior distributions. Analyses were conducted with a 100,000 step burn-in, 1,000,000 Markov chain Monte Carlo repetitions, and 3 replicates per level from K = 1 to 12. The most likely

K was identified by the average maximal value of Ln P(D) returned by Structure. The program LDNe (Waples & Do 2008) was used in an attempt to estimate effective population size based on linkage-disequilibrium between microsatellite loci.

3.2.5 Isolation with migration

Migration rate (m), effective population size (θ), and divergence time (τ) between populations were estimated using the coalescent-based isolation-with-migration model implemented in IMa (Hey & Nielsen 2007). All estimates were scaled on mutation rates

(μ) that are unknown for COI in I. nautilei, so splitting time was calibrated against two hypothetical rates: μ1 = 5×10-8 (determined theoretically, see Audzijonytė & Vrijenhoek

2010), and μ2 = 1.5×10-8 (borrowed from rates in the gastropod, Littorina littorea, see

Cunningham 2008). IMa runs were performed on COI and microsatellite data among three sites within Manus Basin and across all three basins. Only microsatellites that did not deviate from expectations for the stepwise mutation model could be used for IMa analyses (Hey et al. 2004).

A series of short (< 2,000,000 steps) IMa runs was conducted to optimize model parameters and determine the efficient priors for full runs. Prior probabilities and heating schemes were established at θ = 100, m = 100, and τ = 1.5, in a 40 chain geometric model. Effective sample size, autocorrelations, and trend plots were monitored to evaluate convergence. Three independent runs were compared to ensure that marginal posterior distributions had achieved similar solutions and results were averaged across the three runs. Generated trees were analyzed in L-mode for best fit (default settings) and the most likely model was determined using Akaike Information Criterion (Kuhner

2009) and 2LRR tests.

3.3 Results

3.3.1 Summary statistics

Ifremeria nautilei were sampled from Manus, North Fiji, and Lau Basins (275 individuals total; Table 3). Thirty-six partial COI haplotypes (404 bp) were identified from 158 Ifremeria nautilei sampled from Manus Basin; an additional 25 haplotypes were identified from 117 individuals from North Fiji and Lau Basins. Haplotype diversity (Hd) ranged from 0.59 to 0.95 (Table 3) and nucleotide diversity (π) ranged from 0.003 to

0.007. Indices of genetic diversity and tests for selection are reported in Table 3. Fu’s FS values were negative, consistent with allelic excess driven by recent population expansion (but may also be indicative of a selective sweep; Fu 1995), with two exceptions (Patch 3 and Patch 16).

N H H d F s Manus Basin 158 36 0.83 (0.02) -27.301 Solwara 8 44 16 0.80 (0.06) -9.719 Mound 1 15 6 0.74 (0.09) -1.208 Mound 2 17 9 0.83 (0.09) -4.337 Mound 3 12 6 0.82 (0.10) -1.272 Solwara 1 58 16 0.79 (0.04) -8.427 Mound 4 11 8 0.95 (0.05) -3.723 Patch 1 7 6 0.95 (0.10) -3.027 Patch 2 4 4 1.00 (0.18) n.d. Mound 5 26 7 0.71 (0.07) -1.377 Patch 3 15 4 0.66 (0.08) 0.503 Patch 4 7 6 0.95 (0.10) -0.780 Patch 5 4 3 0.83 (0.22) n.d. Mound 6 21 9 0.80 (0.06) -3.403 Patch 6 6 3 0.60 (0.22) n.d. Patch 7 3 3 1.00 (0.27) n.d. Patch 8 12 6 0.76 (0.12) -1.475 South Su 56 20 0.88 (0.03) -4.918 Mound 7 18 11 0.91 (0.05) -5.865 Patch 9 6 5 0.90 (0.16) n.d. Patch 10 2 2 1.00 (0.50) n.d. Patch 11 2 2 1.00 (0.50) n.d. Patch 12 8 6 0.93 (0.08) -2.401 Mound 8 22 12 0.91 (0.04) -5.473 Patch 13 5 5 1.00 (0.13) n.d. Patch 14 10 7 0.93 (0.06) -2.906 Patch 15 7 3 0.67 (0.16) -2.354 Mound 9 16 8 0.80 (0.09) -2.474 Patch 16 7 4 0.81 (0.13) 1.081 Patch 17 9 7 0.92 (0.09) -2.952 North Fiji Basin 81 20 0.70 (0.05) -14.603 White Lady 25 11 0.83 (0.06) -5.655 White Rhino 27 9 0.65 (0.10) -3.321 Mussel Hill 29 8 0.61 (0.10) -3.033 Lau Basin 36 10 0.59 (0.09) -4.918

Nine microsatellite loci were amplified from 40 to 66 individuals per site within

Manus Basin. Total alleles per locus ranged from 3 to 23 (mean = 6.6). In permutation tests, allelic richness (Rs) did not vary significantly among patches, mounds, or sites

(10,000 permutations, P > 0.05). Three loci (Ifr040, Ifr052, and Ifr078) were monomorphic at the patch level but were polymorphic among patches (Appendix A, Table 15).

Only eight microsatellite loci were amplified from 20 to 38 individuals per site from North Fiji and Lau Basin (Appendix A, Table 16). One locus (Ifr086) failed to amplify in any North Fiji or Lau Basin samples. The total number of alleles per locus ranged from 2 to 23 (mean = 6.2). In permutation tests, allelic richness (Rs) did not vary significantly among sites or basins (10,000 permutations, P > 0.05).

3.3.2 Microsatellite marker quality

Tests for HWE deviation were used to assess the quality of sampled microsatellite markers. In Manus Basin, heterozygote deficiency was detected in one locus (Ifr086). Heterozygote excess was detected in only one locus at the site level

(Solwara 1, Ifr040). Significant deviation from HWE was not detected at any other patch, mound, or site from Manus Basin.

Two microsatellite loci deviated significantly from Hardy-Weinberg Equilibrium at sites within North Fiji Basin (Ifr068 and Ifr103). Four microsatellite loci were not in equilibrium at the basin level (Ifr068, Ifr078, Ifr093, and Ifr103). Neither directional nor

balancing selection was detected among microsatellites at any spatial scale within

Manus Basin (LOSITAN, P > 0.05), but one microsatellite locus (Ifr043) was under positive selection (LOSITAN, 25,000 simulations, P < 0.001) at the basin level.

3.3.3 Microsatellite marker identity and excluded markers

Identity tests were used to assess the utility of each microsatellite marker set.

Within Manus Basin, probability of identity tests (PID) and probability of sibling identity tests (PSIB) indicated that the nine microsatellite markers identify individuals (PID =

1.5×10-6), including those that shared 50% genetic similarity (PSIB = 3.5×10-3). IMa coalescent models require that microsatellite markers adhere to the stepwise mutation model; only four of nine microsatellite markers (Ifr043, Ifr052, Ifr078, and Ifr086) adhered to this model and could be used for IMa analysis. Identity tests for these four markers suggested that they are insufficient for assessment of population structure (PID = 1.1×10-2,

PSIB = 0.12).

Within North Fiji and Lau Basins, four of nine microsatellite markers (Ifr043,

Ifr068, Ifr086, and Ifr103) failed to amplify, were out of equilibrium, or were under selection. These markers were excluded from all analyses involving North Fiji and Lau

Basins. The five remaining microsatellite loci could identify individuals (PID = 4.0×10-4), even those that share 50% genetic similarity (PSIB = 4.4×10-2). Only two of those microsatellites (Ifr052 and Ifr078) adhered to the stepwise mutation model and could be

used in IMa analyses. Identity tests for these two markers suggested that they are insufficient for assessment of population structure (PID = 0.13, PSIB = 0.35).

3.3.4 Population structure within Manus Basin

Mitochondrial genealogies revealed two frequent haplotypes at all Manus Basin sites (individuals per haplotype > 30; Figure 3). Less abundant haplotypes radiated from the dominant haplotypes in a star-like pattern (many shallow branches radiating from numerically dominant haplotypes). South Su contained the most private haplotypes (n =

10), followed by Solwara 1 (n = 8) and Solwara 8 (n = 7).

No indication of population structure was detected at the patch, mound, or site level within Manus Basin: Analysis of molecular variance (AMOVA) indicated no variation among haplotypes using either mitochondrial or microsatellite markers (P >

0.05); pairwise comparisons of FST and φST revealed no significant genetic differentiation

(P > 0.05); hierarchical analysis of FST and φST did not reveal any significant population differentiation among nested samples (HIERFSTAT, P > 0.05; Table 4); assignment tests indicated that all I. nautilei collected in Manus Basin constitute a single population

(Structure, K = 1, Figure 4).

Hierachical Level 9 Loci COI Patch/Mound 0.03 (0.27) 0.01 (0.17) Mound/Site 0.01 (0.77) 0.01 (0.25) Site/Total 0.00 (0.07) 0.01 (0.08)

Hierachical Level 5 Loci COI Site/Basin 0.00 (0.56) 0.01 (0.06) Basin/Total 0.05 (0.04) 0.23 (0.03)

3.3.5 Population structure among Manus, North Fiji, and Lau Basin

Haplotypes present in Manus, North Fiji, and Lau Basins segregated into three groups (Figure 3). Haplotype group 1 included all samples from Manus Basin while haplotype group 2 contained a mix of individuals from North Fiji and Lau Basin (Figure

3). Haplotype group 3 also contained a mix of individuals from North Fiji and Lau Basin but was not directly connected to haplotype group 2 (Figure 3).

Assignment tests of individual multi-locus genotypes identified two geographical regions hosting distinct populations of Ifremeria nautilei (STRUCTURE, K =

2; Figure 4): one in Manus Basin and a second occupying North Fiji and Lau Basins.

STRUCTURE output suggests that one individual from North Fiji Basin may be second- generation migrant from Manus Basin and one individual from Manus Basin may be a second-generation migrant from the North Fiji/Lau Basin population. Hierarchical analyses of F-statistics also detected significant differentiation at this regional level

(HIERFSTAT, P < 0.05; Table 3), but no significant differentiation was detected at lower levels [among sites within regions, among sites within the Manus Basin, among mounds within Manus sites, or among patches within Manus mounds (Table 8). Pairwise FST and

φST values did not show a significant increase in differentiation with geographical distance among samples separated by less than 1100 km (Table 9).

MB NFB LB Manus Basin - 0.053 0.055 North Fiji Basin 0.514 - 0.000 Lau Basin 0.530 -0.005 -

3.3.6 Estimates of migration, effective population size, and divergence time

As reported above, the few microsatellites that adhered to the stepwise mutation model were insufficient to adequately assess gene flow. Only the mitochondrial COI data yielded consistent results in the IMa runs. Coalescent estimates of migration rate

(IMa) could not rule out the possibility of equal and bidirectional migration within

Manus Basin populations. Due to the high level of connectivity between Manus Basin samples, it is unlikely that estimates of gene flow would converge on the most likely solution. The posterior probabilities display multiple peaks and gradually increasing probabilities and thus results from these analyses should be approached with caution

(Figure 9).

Figure 5. Posterior probability densities for migration of Ifremeria nautilei between basins in the western Pacific, based on mitochondrial COI gene region

Across samples of I. nautilei from all three western Pacific basins, coalescent estimates of migration rate (IMa) suggest that migration between North Fiji and Lau

Basin is high (Table 10). Consistent with a single panmictic population, m-values could not be constrained between North Fiji and Lau Basin. No evidence for migration between Manus and either North Fiji or Lau Basin was detected (IMa), suggesting that I. nautilei from Manus Basin are isolated from North Fiji and Lau Basin (m1 = m2 = 0; Table

10). Posterior probability estimates for gene flow between North Fiji and Lau Basin were inconsistent, due to the fact that Ifremeria nautilei from North Fiji and Lau Basin are part of a single, undifferentiated population. An interpretation of directional migration between these two basins should be approached with caution.

Table 6. Best fit models for coalescent analysis of Ifremeria nautilei populations among Manus, North Fiji, and Lau Basin. θ1 = effective size of population 2, θ2 = effective size of population 2, θa = effective size of ancestral population, m1 = migration into population 1 from population 2, m2 = migration in to population 2 from population 1, τ = splitting time between populations. Splitting time is calibrated against two mutations rates: μ1 = 5×10-8 [21], μ2 = 1.5×10-8 [76] adjusted for the number of base pairs in the sequence.

Population 1 Population 2 θ 1 θ 2 θ a m1 m2 τ τ/μ 1 *404 τ/μ 2*404 Manus North Fiji 59.3 23.3 0.1 0.0 0.0 2.09 103,000 345,700 Manus Lau 66.0 12.6 0.4 0.0 0.0 1.55 79,900 256,400 Lau North Fiji 66.9 66.9 6.7 6.2 27.2 0.25 12,400 41,400

For Manus, North Fiji, and Lau Basin samples of Ifremeria nautilei, estimates of effective population size using either microsatellite linkage disequilibrium or coalescent analysis could not constrain population sizes among basins, suggesting that within each basin, effective population size is functionally infinite. Estimates of splitting time place the oldest divergence between North Fiji and Manus Basin, with a relatively recent split between North Fiji and Lau Basin (Table 12).

3.4 Discussion

The complete absence of genetic subdivision in populations of Ifremeria nautilei at distances up to 1000 kilometers suggests this species is able to colonize distant vent habitats and that the ciliated Warén’s larvae produced by I. nautilei are adapted for long- distance dispersal, as hypothesized by Reynolds et al. 2010. Despite this dispersal potential, a barrier to gene flow exists between Manus and North Fiji/Lau Basin populations that are separated by 2500 kilometers. Although mitochondrial COI gene sequences and nuclear microsatellite loci are informative at different temporal scales, both markers indicated identical patterns of population structure in I. nautilei, regardless of spatial scale.

3.4.1 Local population structure within Manus Basin

Ifremeria nautilei from the three Manus Basin sites belong to a single, panmictic population based on mitochondrial COI gene sequences and nuclear microsatellite markers. Although patch sizes were generally small, several patches (particularly in

Solwara 1) had a sufficient sample size to test and reject the hypothesis that self- recruiting patches of I. nautilei might create the appearance of panmixia within this sample set.

Ifremeria nautilei from South Su had the highest abundance of private haplotypes and private alleles. Under a scenario of colonization with subsequent migration, this pattern could suggest that South Su might serve as a source population that contributes individuals to other sites sampled in Manus Basin. This directional gene flow is consistent with the path of the St. George’s Undercurrent, which enters Manus Basin from the southeast and travels northwest, encountering South Su first, then Solwara 1 and Solwara 8, before merging with the Vitiaz Straight Undercurrent to form the New

Guinea Coastal Undercurrent (Zenk et al. 2005). This hypothesis could be tested with development of additional genetic markers that provide more detailed genealogical information than the microsatellite markers used in this study and by sampling and analysis of individuals from sites further west that are known to support Ifremeria nautilei. Rapid population expansion, as suggested by Fu’s FS and the star-like mitochondrial genealogies, could account for the emergence of private haplotypes and

alleles at each sampled site within Manus Basin. Alternatively, the negative Fu’s FS values could be a result of a selective sweep on the mitochondrial genome. Additional sequenced-based nuclear markers would be needed to rule out this possibility.

3.4.2 Basin-scale population structure

Individuals of Ifremeria nautilei sampled from across the known range of the species in the south western Pacific could be subdivided genetically into two populations, one restricted to Manus Basin and one distributed throughout North Fiji and Lau Basins. Under an isolation-by-distance scenario, genetic differentiation is expected to gradually increase with distance (Wright 1943), but distance alone does not appear to create a significant barrier to gene flow. North Fiji and Lau Basin samples, separated by ~1000 kilometers, were undifferentiated. Coalescent estimates of gene flow suggest that genetic isolation of I. nautilei populations between Manus Basin and North

Fiji/Lau Basins may have existed, with occasional migration, for several hundred thousand generations, but without an understanding of mutation rates and average generation times in these snails, it is impossible to place these estimates in a geological time frame.

The phylogeographic break between populations from Manus and North Fiji/Lau

Ifremeria nautilei is striking, considering the high degree of mixing within each population. Phylogeographic breaks are often associated with oceanographic features

(e.g., geomorphology, hydrology) that form effective dispersal barriers for a wide range of taxa (e.g., Cape Hatteras - Baker et al. 2008, Cape Cod - Jennings et al. 2009, Easter

Microplate - Vrijenhoek 2010). A related provannid snail complex, Alviniconcha spp., occurs throughout southwestern Pacific basins (Desbruyères et al. 2006) and is comprised of several cryptic species (Kojima 2001). The observed phylogeographic isolation of I. nautilei populations thus does not reflect a pattern that is shared by other southwestern Pacific vent taxa.

The phylogeographic break between Manus and North Fiji/Lau populations of

Ifremeria nautilei is not likely the result of a colonization event. Colonization would result in a founder effect, where the founded population contains a subset of alleles from the source population (Mayr 1942). The single mitochondrial haplotype shared between the Manus and North Fiji/Lau populations is intermediate between dominant haplotypes from the two populations and may be a product of incomplete lineage sorting between formerly connected populations (Cunningham and Collins 1998).

Haplotype Group 3 (North Fiji/Lau) is more closely related to Haplotype Group 1

(Manus Basin; Figure 2) and it consists of as many missing haplotypes as it does actual haplotypes. This abundance of missing haplotypes, relative to other haplotype groups, could be the result of inadequate sampling, or it may suggest that disproportionately more haplotypes in that lineage have gone extinct. Our interpretation is that the two populations once existed as a single population spanning Manus, North Fiji, and Lau

Basins and that this population became isolated through a vicariant process that remains to be determined. The presence of potential second-generation immigrants in each population suggests that isolation might not be complete between the two regions.

While Ifremeria nautilei occurs throughout Manus, North Fiji, and Lau Basins, populations of I. nautilei follow the trend of greater endemism and limited connectivity hypothesized for species endemic to back-arc basin spreading centers (Van Dover 2000).

In this context, it is not surprising that a population of I. nautilei is distributed through

North Fiji and Lau Basin, as these two basins share genera and species (Desbruyères et al. 2006). Water masses tend to be retained within Lau Basin, with some movement of northwestward flowing undercurrents from Lau into North Fiji Basin (Thurnherr, unpublished data, available at http://www.ldeo.columbia.edu/~ant/LAUB-FLEX/). This undercurrent movement is consistent with the weak signal of directional gene flow from

Lau into North Fiji Basin (Table 5). Likewise, the barrier to dispersal between North Fiji and Manus Basin that limits gene flow between I. nautilei populations is consistent with a barrier that was hypothesized to restrict species dispersal between these two basins

(Hessler and Lonsdale 1991). The barrier could be caused by geomorphological obstacles

(i.e. the Vanuatu Archipelago), the lack of depth overlap between sites in Manus Basin and sites in North Fiji and Lau Basin, or by a yet to be determined oceanographic feature.

3.4.3 Conclusions

Theoretical and experimental studies suggest that spatially and temporally unstable environments favor broad dispersal capabilities (Cohen & Levin 1991,

Friedenberg 2003), which in turn should lead to shallow or absent population subdivisions. In dynamic systems such as hydrothermal vents, where habitat availability is unpredictable, survival depends on long-distance dispersal of propagules. No significant genetic differentiation was found among samples of western Pacific Ifremeria nautilei at the patch, mound, or site levels within the Manus Basin. No differentiation was observed between samples of I. nautilei collected from North Fiji and Lau Basins, which are separated by ~1000 kilometres. The Manus Basin population of I. nautilei is isolated from that of the North Fiji and Lau Basins by an unknown process that limits contemporary gene flow.

Reproductive mode and larval type are often poor predictors of population structure in marine environments (Kelly & Palumbi 2010, Desbruyères et al. 2006, Van

Dover et al. 2002). Species with broad dispersal potential have been reported with high levels of differentiation at spatial scales of a few kilometers or less (Banks et al. 2007, Bird et al. 2007, Tatarenkov et al. 2010) while species that would otherwise be expected to show fine-scale population structure have been reported to show surprisingly high levels of connectivity throughout their geographic range (Miller & Ayre 2008, Blanquer

& Uriz 2010). The absence of Ifremeria nautilei population structure at all but the broadest spatial scales is consistent with long-distance dispersal and the barrier to gene flow between the North Fiji and Lau Basin population and the Manus Basin population is likely extrinsic and not related to life history characteristics.

Fine-scale spatial sampling and genetic analysis such as that used in this study can inform mitigation and best-management practices for mineral extraction at deep-sea hydrothermal vents. The Solwara 1 site is targeted for deep-sea mineral extraction

(Coffey Natural Systems 2008). A robust understanding of population genetic structure at multiple spatial scales can define natural conservation units that can be used to minimize loss of genetic diversity within and among populations of vent-restricted species (Van Dover 2011). For Ifremeria nautilei, high rates of gene flow among the sampled Manus Basin sites suggests that the Solwara 1 vents are likely to be repopulated from other Manus Basin localities, including South Su and Solwara 8. Monitoring of species recovery and genetic diversity as the Solwara 1 population recovers after extraction operations cease should add insight into the rates at which novel haplotypes and alleles accumulate in this species, providing a means to estimate the ages and sizes of extant populations. The Manus Basin population of I. nautilei comprises a genetically distinct unit that should be managed separately from the North Fiji/Lau population.

4. Comparative population genetics of a hydrothermal- vent-associated shrimp, Chorocaris sp.2, and limpet, Olgasolaris tollmanni from southwestern Pacific back-arc basins

4.1 Introduction

Connectivity among populations associated with patchily distributed, temporally unstable ecosystems may be influenced not only by the tempo of disturbance, but by the degree to which species are dependent on those ecosystems.

Generally, spatially heterogeneous (patchy) habitats (such as seamounts: Samadi et al.

2006; alpine lakes: Brunner et al. 1998; and coral reefs: Benzie 1999) tend to correlate with locally differentiated genetic structure, especially in species with limited dispersal potential (Cohen & Levin 1991). Patchy habitats that experience frequent disturbance, however, may favor increased dispersal and reduced population structure (Holt &

McPeek 1996; Ronce 2007). More frequent and intense disturbances may result in homogenously distributed populations, as the effects of temporal instability prevent the accumulation of private alleles and independent genetic drift (Karlson & Taylor 1995;

Friedenberg 2003; Matlack & Monde 2004). Species with limited dispersal capacity may show increased levels of genetic differentiation across spatially and temporally heterogeneous habitats due to low effective population sizes and consequently high rates of genetic drift (e.g. Keyghobadi et al. 1999; Yamamoto et al. 2004). Determining the spatial scale at which population differentiation occurs in species associated with

patchy, dynamic ecosystems provides insight into these ecosystems respond to natural and anthropogenic disturbance.

Deep-sea hydrothermal vents are patchy, dynamic ecosystems that host communities supported by habitat-building species that rely on free-living bacteria or chemoautotrophic endosymbionts (e.g., provannid gastropods in the genera Ifremeria and Alviniconcha, and mytilid mussels in genus Bathymodiolus; reviewed in Van Dover

2000; Desbruyères et al. 2006). These habitat-building species are dependent on hydrothermal fluid flux to sustain their endosymbionts and allow host tissue growth and they exhibit reproductive strategies shared with ‘weedy’ species (rapid growth, early reproduction, and broad dispersal capabilities; Vrijenhoek 1997; Vrijenhoek 2010).

Secondary consumers, such as Chorocaris shrimp and limpets in the genera Lepetodrilus and Olgasolaris (reviewed in Van Dover 2000; Desbruyères et al. 2006) are abundant, forming large populations associated with habitat-building, endosymbiont-hosting species. While endosymbiont-hosting species are dependent on hydrothermal fluid and must recruit close to vents, secondary consumers can often survive away from active hydrothermal vents and, in some cases, are able to move between habitats as adults

(Desbruyères et al. 2006). Although secondary consumers commonly occur at active vents, they may also be found at the periphery of vents or on inactive sulfide edifices,

(reviewed in Van Dover 2000; Erickson et al. 2009; Collins et al. 2012). I hypothesize that connectivity for secondary consumers that are able to access non-vent, stepping-stone,

habitats may be greater across lager distances than connectivity among endosymbiont- hosting species that are dependent on hydrothermal vents for sustenance (De Meester et al. 2002; Bowler & Benton 2005).

Substantial progress has been made in understanding dispersal and connectivity of deep-sea hydrothermal vents communities over the last two decades (reviewed in

Vrijenhoek 1997; Vrijenhoek 2010). In the western Pacific, the majority of studies have focused endosymbiont-hosting species including provannid gastropods and mytilid mussels (Desbruyères et al. 2006). Studies of population structure have not been able to reject panmixia in several endosymbiont-hosting species across discrete vent fields (i.e.

Bathymodiolus spp.: Moraga et al. 1994; Kyuno et al. 2009; Alviniconcha spp.: Kojima et al.

2001; Suzuki et al. 2006a; Ifremeria nautilei: Thaler et al. 2011). For at least one endosymbiont-hosting species, Ifremeria nautilei, a dispersal filter may exist, isolating one population in Manus Basin from an otherwise well-connected population that encompasses the rest of its known biogeographic range (North Fiji and Lau Basins;

Kojima et al. 2000; Thaler et al. 2011). Among secondary consumers that occur at western

Pacific hydrothermal vents, intra-basin genetic structure has been assessed at spatial scales that account for the variability of individual aggregations, but not at scales that compare populations across oceanographic basins. One limpet, Lepetodrilus schrolli, showed no genetic structure throughout Lau Basin, and no structure was detected among limpets from different habitat types (Johnson et al. 2008). Three species of

hydrothermal-vent-occurring copepods, Stygiopontius brevispina, S. lauensis, and S. hispidulus, appear to be panmictic throughout Lau Basin (Gollner et al. 2010).

To determine the spatial scale at which genetic structure is likely to emerge among populations of vent-associated secondary species and investigate the extent of a dispersal filter that isolates Manus Basin populations from other back-arc basins, we examined partial-COI sequences from two species, Chorocaris sp. 2 and Olgasolaris tollmanni, and microsatellite markers from Chorocaris sp. 2. Chorocaris sp. 2 is a deep-sea vent-associated shrimp that has planktotrophic larvae, is highly mobile as an adult, and is distributed throughout the western Pacific (Desbruyères et al. 2006; Komai et al. 2008; currently under description by T. Komai). A putative cryptic species, Chorocaris sp. 1, is currently being described by T. Komai. Olgasolaris tollmanni is a limpet commonly found at deep-sea vents, has planktotrophic larvae (Beck 1992; Sasaki et al. 2010) and is also known to occur at rocky outcroppings associated with inactive vents and non-vent areas

(Erickson et al. 2009; Collins et al. 2012). O. tollmanni is most abundant near active vents, where it is frequently associated with aggregations of Ifremeria nautilei (Desbruyères et al.

2006).

One site in Manus Basin, Solwara 1, is targeted for mineral extraction (Coffey

Natural Systems 2008). By understanding the extent of past and present connectivity among populations within Manus Basin and how those populations relate to others in the western Pacific, we can better anticipate the effect that removal of a vent field will

have on the genetic diversity and population structure of deep-sea hydrothermal-vent- occurring species from Manus Basin (Van Dover 2010).

4.2 Materials and Methods

4.2.1 Sample collection and DNA extraction

Shrimp and limpets were collected during June-July 2008 with an ST200 trenching ROV, modified for biological sampling, from three hydrothermal vent sites in

Manus Basin: Solwara 8, Solwara 1, and South Su (Figure 6). The sampling protocol ensured that three to four aggregations of shrimp and one to four patches of limpets were collected from each site (Table 7). Whole organisms were preserved in 95% ethanol. The ROV Jason II was used to collect shrimp from North Fiji Basin in May-June

2005 and limpets from Lau Basin in May-June 2009. Tissues were stored at -80°C and transferred to 95% ethanol prior to DNA extraction. Genomic DNA was isolated using a standard Chelex-Proteinase-K extraction (10–30 mg digested with 120 µg Proteinase K

(Bioline: Taunton, MA) in 600 µl 10% Chelex-100 resin (Bio-Rad: Hercules, CA) overnight at 60°C, heated to 100°C for 15 min, and centrifuged at 10,000 rpm for 5 minutes) or Wizard SVG tissue extraction kit (Promega Corp: Madison, WI) following manufacturer’s protocols. Extracted DNA was stored at 4°C until amplification, and then archived at -20°C for long-term storage.

Table 7. Chorocaris spp. and Olgasolaris tollmanni sampling locations from Manus, North Fiji, and Lau Basin.

Basin Site Mound/Aggregation Latitude Longitude Depth (m) Manus Solwara 8 Aggregation 1 3° 43.751'S 151° 40.410'E 1720 Aggregation 2 3° 43.826'S 151° 40.457'E 1720 Aggregation 3 3° 43.669'S 151° 40.873'E 1710 Solwara 1 Aggregation 4 3° 47.453'S 152° 5.485'E 1530

spp. Aggregation 5 3° 47.367'S 152° 5.781'E 1490 Aggregation 6 3° 47.372'S 152° 5.619'E 1480 South Su Aggregation 7 3° 48.537'S 152° 6.284'E 1300 Aggregation 8 3° 48.497'S 152° 6.298'E 1330

Chorocaris Aggregation 9 3° 48.572'S 152° 6.312'E 1320 Aggregation 10 3° 48.572'S 152° 6.317'E 1320 North Fiji Ivory Tower 16° 59.300'S 173° 54.900'E 1970 Manus Solwara 8 Mound 2 3° 43.824'S 151° 40.458'E 1710 Solwara 1 Mound 4 3° 47.436'S 152° 5.472'E 1530 Mound 5 3° 47.370'S 152° 5.778'E 1490 Mound 6 3° 47.370'S 152° 5.616'E 1480 South Su Mound 7 3° 48.564'S 152° 6.144'E 1300 Mound 8 3° 48.492'S 152° 6.186'E 1350 Mound 9 3° 48.432'S 152° 6.306'E 1320 Lau Kilo Moana 20° 03'S 176° 08'W 2620

Olgasolaristollmanni Tui Malila 21° 59'S 176° 34'W 1890 ABE 20° 46'S 176° 11'W 2150 Tow Cam 20° 46'S 176° 34'W 2700

4.2.2 COI amplification and analysis

Mitochondrial COI DNA fragments were amplified using the following reaction conditions for shrimp: 10 to 100 ng of DNA template was combined with 2 µL PCR buffer (20 mM Tris, pH 8.8; 50 mM KCl; 0.01% Triton X-100; 0.02 mg/ml BSA), 2 mM

MgCl+, 0.6 mM dNTP’s, 0.05 μM LCOI1490 and 0.05 μM HCOI2198 primers (Folmer et al. 1994), and 1 unit of Taq polymerase in a 25 µL reaction. Polymerase Chain Reactions

(PCR) were run using the following protocol: initial melting temperature of 94°C for 120 seconds; 25 cycles of 94°C for 35 seconds, 50°C for 35 seconds, 72°C for 80 seconds; and a final extension of 72°C for 600 seconds. Reactions were stored at 4°C until clean-up.

Partial COI fragments for limpets were amplified using the following reaction conditions: 10 – 100 ng of DNA template was combined with 2.75 µL PCR buffer, 2 mM

MgCl+, 0.1 mM dNTP’s, 0.5 μM LCOI1490 and 0.5 μM HCOI2198 primers (Folmer et al.

1994), and 1 unit of Taq polymerase in a 25 µL reaction. PCRs were run with the following protocol: initial melting temperature of 94°C for 120 seconds; 35 cycles of 94°C for 35 seconds, 50°C for 35 seconds, 72°C for 80 seconds; and a final extension of 72°C for

600 seconds. Reactions were stored at 4°C until clean-up.

To remove unincorporated nucleotides, 14 µl of PCR product was incubated with

0.2µl 10 X ExoAP buffer (50 mM Bis-Tris, 1 mM MgCl2, 0.1 mM ZnSO4), 0.05µl Antarctic

Phosphatase (New England Biolabs: Ipswich, MA), 0.05µl Exonuclease I (New England

Biolabs: Ipswich, MA) at 37°C for 60 min followed by 85°C for 15 min to inactivate the

enzymes. Bi-directional sequencing reactions were performed using the manufacturer’s protocol for Big Dye Terminator Reaction (Applied Biosystems: Foster City, CA).

Sequenced PCR product was purified using AMPure magnetic bead system (Agencourt:

Morrisville, NC) following manufacturer’s protocol, analyzed on an ABI 3730xl DNA

Analyzer (Applied Biosystems International), and edited with Sequencher version 4.7

(Gene Codes: Ann Arbor, MI). Consensus sequences were compared against the NCBI

GenBank database to confirm species identity when available (Benson 1997) and aligned using the MUSCLE alignment algorithm (Edgar 2004). A sequence of each unique haplotype was deposited in GenBank.

Neighbor-joining phylograms of aligned mitochondrial sequences were assembled in MEGA version 5 (Tamura 3-parameter substitution model determined by

Mega 5: Find Best-Fit Substitution Model for both shrimp and limpets; Tamura et al.

2011). Potential cryptic species were determined by comparing the extent of genetic differentiation between two putative species to the extent of genetic differentiation among established species within the same genus. We use the classification proposed in

Bickford et al. (2007). Cryptic species are recently diverged sister clades that are morphologically indistinguishable and were previously classified as a single species

(Bickford et al. 2007).These criteria may not be all-inclusive; COI is often a poor marker for defining species, and further analyses beyond the scope of this paper are required to confirm species status.

Statistical-parsimony networks were assembled in TCS version 1.21 (default settings; Clement et al. 2000) using only samples determined to be within the same putative species. DnaSP version 5.10.01 (Librado & Rozas 2009) was used to calculate number of haplotypes (H) haplotype diversity (Hd), and Fu’s FS. DnaSP was also used to construct mismatch distribution curves for expected values under constant population size and population growth/decline models and observed values for each population were compared against expected values. Arlequin version 3.11 (Excoffier et al. 2005) was used to estimate pairwise φST and permutation tests were used to identify significant departure from genetic homogeneity. Sequential Bonferroni correction was used to account for multiple samples (Rice 1989).

4.2.3 Microsatellite genotyping and statistical analyses

Six microsatellite markers (Cho30, Cho36, Cho63, Cho76, Cho91, Cho99) were amplified from Chorocaris sp. 2 following methods reported in Zelnio et al. (2010).

Allelic richness was calculated in MSA (version 4.05; Derringer and Schlötterer 2003) and divergence from Hardy-Weinberg Equilibrium (HWE) expectations were calculated in GENEPOP (default settings: version 4.0; Rousset 2008). Permutation tests to determine significant variation in allelic richness were conducted in F-stat (default settings: version 2.9.3.2; Goudet 1995). Potential presence of null alleles was tested using

MicroChecker (version 2.2.3, 1000 randomizations; van Oosterhout et al. 2004).

Arlequin was also used to conduct an analysis of molecular variance (AMOVA) to identify hierarchal population structure. Microsatellite Analyzer (MSA; Dieringer &

Schlötterer 2003) was used to identify pairwise genetic differentiation (FST) between aggregations and sites for Chorocaris sp. 2. Alpha levels were adjusted via Sequential

Bonferroni correction to account for multiple tests (Rice 1989). Structure version 2.3.3

(Pritchard et al. 2000) was used to visualize potential population structure using an admixture model with sampling locations as prior distributions. Analyses were conducted with a 100,000 step burn-in, 1,000,000 repetitions, and 3 replicates per level from K = 1 to 12.

4.3 Results

4.3.1 Shrimp

Shrimp (Chorocaris spp.) were sampled from Manus (191 individuals; Table 8) and North Fiji Basins (9 individuals; Table 8). Two putative species were identified,

Chorocaris sp. 1 (12 of 41 individuals collected from South Su), and Chorocaris sp. 2 (188 individuals total). Chorocaris sp. 1 and sp. 2 sequences are divergent from those reported in GenBank for Chorocaris vandoverae, a shrimp found at vents in the northwestern

Pacific and Chorocaris chacei, from vents on the Mid-Atlantic Ridge (Figure 7). Chorocaris sp. 2, the numerically dominant species, was further analyzed for nested population structure using both COI and microsatellite markers.

Species Location N H Hd F S South Su 12 8 0.92 -3.96 Aggregation 7 1 1 n/a n/a Chorocaris sp. 1 Aggregation 9 5 4 0.90 n/a Aggregation 10 6 4 0.87 n/a Manus Basin 179 106 0.98 -162.12 Solwara 8 88 60 0.98 -69.19 Aggregation 1 46 37 0.99 -35.36 Aggregation 2 23 20 0.99 -14.81 Aggregation 3 19 15 0.97 -7.05 Solwara 1 62 47 0.98 -58.13 Aggregation 4 14 15 1.00 -12.00 Chorocaris sp. 2 Aggregation 5 5 5 1.00 n/a Aggregation 6 43 32 0.98 -32.95 South Su 29 24 0.98 -17.19 Aggregation 7 5 3 0.70 n/a Aggregation 8 4 4 1.00 n/a Aggregation 9 8 8 1.00 n/a Aggregation 10 12 11 0.99 -4.89 Fiji Basin 9 7 0.92 -1.74

Figure 7. Maximum-likelihood tree for a subset of Chorocaris spp. sampled from Manus Basin. Sequences are 600-base pairs in length. Substitution model is Tamura 3- parameter determined by Find Best Model application in Mega 5. Chorocaris sp. 1 and Chorocaris sp. 2 indicated by horizontal bars. Chorocaris vandoverae (accession # AF125417), Chorocaris chacei (accession # AF125414), Rimicaris exoculata (accession # FN393000), and Opaepele loihi (DQ328824) presented for comparison. Alvinocaris longirostris (accession # AB222050) used as an outgroup. Bootstrap values greater than 0.50 reported on branches. Scale bar is number of substitutions per base pair.

One-hundred-six partial-COI haplotypes were identified from 179 individual

Chorocaris sp. 2 from Manus Basin. Haplotype diversity among aggregations in Manus

Basin ranged from 0.70 to 1.00 (Table 8). Fu’s FS values were all significantly negative

(Table 12). Eight COI haplotypes were identified from 12 Chorocaris sp. 1 individuals from South Su in Manus Basin (Table 8). Chorocaris sp. 1 haplotype diversity ranged from 0.87 to 0.90 among aggregations at South Su (Table 8) and Fu’s FS was significantly negative (Table 8). Nine Chorocaris individuals from North Fiji Basin were identified as

Chorocaris sp. 2 and aligned within sequences sampled from Manus Basin (Table 8,

Figure 8). Mismatch distribution curves followed a unimodal distribution, consistent with recent population expansion (Figure 9) and the mean number of pairwise differences among segregating sites was 5.1.

The statistical parsimony network follows a web-like topology, with many singletons connected through multiple nodes, suggesting high genetic variability (Figure

8). A small North Fiji clade branches off of the larger Manus clade (Figure 8). There are seven numerically dominant haplotypes (n > 3; Figure 8) but singletons radiate out along multi-node branches (Figure 8). Despite a strong signal of population differentiation, one dominant haplotype was shared between Manus and North Fiji Basin and a second

North Fiji singleton was found within the Manus Basin haplotype group (Figure 8).

There are several old lineages within Manus Basin that are divergent from the main

Manus haplotype group by up to 6 mutational steps; this is greater than the divergence between Manus and North Fiji Basin (Figure 8).

Six microsatellite loci were amplified from 64 to 92 Chorocaris sp. 2 individuals per site within Manus Basin (Table 9). Total alleles per locus ranged from 3 to 11 (mean =

7). In permutation tests, allelic richness (Rs) did not vary significantly among patches, mounds, or sites (10,000 permutations, P > 0.05). Neither directional nor balancing selection was detected among microsatellites at any spatial scale (LOSITAN, P > 0.05), although past studies have indicated that small microsatellite marker sets may be insufficient to detect selection (Lemaire et al. 2000). Three loci deviated significantly from

HWE and showed evidence for heterozygote deficiency (Cho63, Cho76, Cho91; Table 9).

Subsequent tests using MicroChecker suggested that null alleles were present at all three loci and were responsible for heterozygote deficiencies.

Cho30 Cho36 Cho63 Cho76 Cho91 Cho99 n 91 91 84 78 89 88 a 3 7 10 9 6 5 Rs 2.66 6.88 9.93 8.90 5.89 4.65 Solwara 8 as 185 - 189 157 - 189 155 - 179 232 - 266 201 - 213 222 - 234 HO 0.11 0.53 0.57 0.31 0.21 0.47 HE 0.15 0.52 0.65 0.49 0.30 0.46 n 92 81 86 67 89 82 a 4 9 10 10 9 4 Rs 3.76 9.00 9.90 9.90 8.23 4.00 Solwara 1 as 183 - 189 149 - 189 159 - 179 248 - 276 199 - 215 222 - 232 HO 0.12 0.40 0.48 0.21 0.29 0.41 HE 0.11 0.44 0.64 0.45 0.47 0.43 n 90 88 82 64 81 87 a 5 11 8 10 7 6 Rs 5.00 9.59 8.00 10.00 7.00 6.00 South Su as 183 - 191 145 - 189 157 - 173 244 - 268 201 - 213 212 - 234 HO 0.21 0.42 0.37 0.20 0.16 0.61 HE 0.25 0.47 0.70 0.32 0.39 0.59

Analysis of Molecular Variance (AMOVA) for Chorocaris sp. 2 indicated no spatially-related hierarchical structure within Manus Basin (Table 10). Pairwise tests (FST and φST) for population differentiation in Manus Basin, using both microsatellite markers and COI, indicated no significant differentiation at any spatial scale (Table 11).

Assignment tests placed all Chorocaris sp. 2 from Manus Basin into a single population

(Structure, K = 1, data not shown). AMOVA performed on COI data indicated that almost 35% of the observed genetic variability was accounted for by divergence between

Manus and North Fiji Basin (Table 10) and strong, statistically significant, population differentiation was detected with COI between Manus and North Fiji Basin (φST = 0.334 to 0.372; Table 11).

Degrees of Sum of Variance Percentage of Source of Variation Gene Freedom Squares Components Variation Among sites within COI 2 5.07 0.01 0.30 Manus Basin MSAT 2 4.27 0.00 -0.27 Among aggregations COI 7 15.71 -0.01 -0.56 within sites MSAT 7 16.08 0.02 2.13 COI 169 413.66 2.49 100.26 Within aggregations MSAT 538 593.04 1.10 98.14 Between basins in COI 1 24.1 1.26 34.26 the Western Pacific Among sites within COI 2 5.07 0 0.06 basins Within sites COI 184 445.6 2.42 65.68

Solwara 8 Solwara 1 South Su Solwara 8 - 0.005 0.007 Solwara 1 0.006 - 0.013 South Su -0.009 0.000 - North Fiji 0.334 0.372 0.339

4.3.2 Limpets

Olgasolaris tollmanni were sampled from Manus (136 individuals) and Lau Basins

(68 individuals; Table 12). One species was identified consistently across all sites and in both basins. No representative samples of O. tollmanni COI sequences were available through either GenBank or the Barcode of Life Database as of August 2012. Fifty-eight partial-COI haplotypes were identified from 136 O. tollmanni individuals sampled from

Manus Basin. Haplotype diversity among patches in Manus Basin ranged from 0.83 to

1.00 and all but two Fu’s FS values were significantly negative (Patch 11 and Mound 8;

Table 12). Mismatch distribution curves followed a unimodal distribution (mean number of pairwise differences among segregating sites = 2.6; Figure 9) and fit with expected observations under a model of population growth/decline. Thirty-four haplotypes were identified from 68 O. tollmanni individuals sampled from Lau Basin.

Haplotype diversity ranged from 0.88 to 1.00 among sites in Lau Basin and Fu’s FS were negative and significant (Table 12).

Location N H Hd F S Manus Basin 136 58 0.93 -76.08 Solwara 8 34 22 0.93 -16.89 Mound 2 34 22 0.93 -16.89 Solwara 1 68 31 0.94 -26.15 Mound 4 15 13 0.98 -8.64 Patch 1 3 3 n/a n/a Patch 2 6 6 1.00 n/a Patch 3 3 3 n/a n/a Patch 4 3 3 n/a n/a Mound 5 29 19 0.95 -14.90 Patch 5 9 8 0.97 -5.29 Patch 6 10 9 0.98 -5.23 Patch 7 10 9 0.83 -5.94 Mound 6 24 15 0.93 -9.75 Patch 8 4 3 0.83 n/a Patch 9 4 4 1.00 n/a Patch 10 7 5 0.91 n/a Patch 11 9 4 0.58 -0.45 South Su 34 17 0.88 -12.57 Mound 7 2 2 n/a n/a Mound 8 9 6 0.83 -2.41 Mound 9 23 14 0.91 -9.76 Patch 12 11 9 0.95 -6.31 Patch 13 12 9 0.91 -5.20 Lau Basin 68 34 0.94 -33.97 Kilo Moana 21 11 0.88 -6.29 Tui Malila 4 4 1.00 n/a ABE 21 13 0.94 -5.50 Tow Cam 22 18 0.97 -15.60

Analysis of Molecular Variance (AMOVA) for COI indicated no hierarchical structure within or among patches, mounds, and sites in Manus Basin or among mounds, sites, and basins within the western Pacific (Table 13). Pairwise tests for population differentiation (φST) between patches, between mounds, between sites, and between basins indicated no significant differentiation at any spatial scale (Table 14).

The statistical parsimony network follows a star-like topology, with many singletons radiating from one central haplotype and six numerically dominant haplotypes (n > 3;

Figure 10).

Degrees of Sum of Variance Percentage of Source of Variation Freedom Squares Components Variation Among mounds 6 7.30 -0.02 -1.28 within sites Among patches 11 16.23 0.02 1.23 within mounds

Within patches 117 161.07 1.38 100.04

Between basins in 1 1.74 0.00 0.22 the Western Pacific Among sites within 5 7.24 0.00 0.18 basins Within sites 197 272.04 1.38 99.60

Table 14. Pairwise φST comparisons of Olgasolaris tollmanni from three sites in Manus Basin and three sites in Lau Basin. No P-values were significant (P < 0.05).

Solwara 8 Solwara 1 South Su Kilo Moana ABE Solwara 1 0.006 - South Su -0.005 0.001 - Kilo Moana -0.006 -0.001 -0.010 - ABE 0.023 0.024 0.028 0.034 - Tow Cam -0.011 -0.004 -0.001 -0.004 0.004

4.4 Discussion

Two populations of the numerically abundant Chorocaris sp. 2 were detected in the southwestern Pacific, one distributed throughout Manus Basin and another found at one in North Fiji Basin, while the less abundant Chorocaris sp. 1 was restricted to a single site in Manus Basin. In contrast, O. tollmanni, exhibited no COI-based population structure at any spatial scale and was undifferentiated across ocean basins separated by more than 3000 kilometers. The pattern of population structure exhibited by Chorocaris sp. 2 is similar to that of Ifremeria nautilei, a habitat-building, vent-endemic gastropod that is also present in Manus and North Fiji Basins (Thaler et al. 2011). The presence of a shared Chorocaris sp. 2 haplotype between Manus and North Fiji Basins and two distinct

North Fiji haplotype lineages suggests that some migration must have occurred between these two populations. The ability to use non-vent habitats as stepping stones may explain how O. tollmanni is able to pass through a dispersal filter which limits migration of several vent-dependent species in the western Pacific (other examples of intermediate

‘stepping stones’ increasing connectivity among populations are reviewed in Shulman

1998). Enhanced larval duration or variation in larval behavior between O. tollmanni and

Chorocaris spp. could also explain these contrasting patterns of population structure. At the scale of Manus Basin, neither abundant species exhibited population structure within or among vent sites, suggesting that all examined sites are well-connected with

extensive, ongoing gene flow, while a currently unidentified process limits the distribution of the rare cryptic species, Chorocaris sp. 1.

4.4.1 Shrimp population structure

Despite strong signals of genetic differentiation between populations of

Chorocaris sp. 2 from Manus and North Fiji Basin, the presence of shared haplotypes indicates that some migration must occur. The absence of haplotypes that descend from the North Fiji clade in Manus Basin suggests that migration may be one-way, from

Manus into North Fiji Basin, though this could be the result of sample bias. Chorocaris sp.

1 and Chorocaris sp. 2 radiated from a common ancestor that diverged from other

Chorocaris species. Chorocaris sp. 1 has not been reported from any other ocean basin but is likely to be more widespread than Manus Basin and its presence at South Su may be due to a recent invasion. Sediment geochemistry at Solwara 1 is significantly richer in heavy metals than South Su (Moss & Scott 2001). If Chorocaris sp. 1 is sensitive to heavy metal concentrations, this increase may preclude colonization of Solwara 1.

Within Manus Basin, the distribution of haplotypes is consistent with patterns of continuous migration among sites—the dominant haplotypes are surrounded by multi- node branches with numerous singletons, although significantly negative Fu’s FS tend to be associated with star-like topologies. The low abundance of older haplotype lineages

(those separated from the dominant group by 4 or more mutations) could be the result

of frequent, non-selective, habitat turnover which is less likely to eliminate abundant haplotypes but would randomly remove rare haplotypes from the population. The high mobility of adult Chorocaris sp. 2 may allow individuals to easily travel among vent sites, reducing the potential for population structure or for the accumulation of private haplotypes. This pattern, in which relatively rare alleles propagate though a population, is commonly associated with a ‘sweepstakes’ interpretation of demographic history where high fecundity coupled with high variance in reproductive success and larval recruitment results in successful colonists drawn at random from a large genetic pool

(Hedgecock 1994).

The low sample size from North Fiji Basin limits the conclusions drawn from these data. Although the signal for genetic differentiation is strong, it is possible that this could be due to a sample bias and that additional sampling will reveal more shared haplotypes between Manus and North Fiji Basin. Samples were acquired from a single site in North Fiji Basin, while Chorocaris sp. 2 are known to occur throughout North Fiji and Lau Basin. Additional sampling at other hydrothermal vent sites and back-arc basins will help clarify the relationships among Chorocaris sp. 2 throughout the western

Pacific. Population genetics studies of a related shrimp, Rimicaris exoculata, from hydrothermal vents along the Mid-Atlantic Ridge, indicated no discernible population structure across more than 5000 kilometers (Teixeira et al. 2011). Genetic diversity for R. exoculata is lower than Chorocaris sp. 2 and has a star-shaped haplotype network. While

one interpretation of the results of Teixeira et al. (2011) suggest that the R. exoculata population is the product of a recent founder event followed by demographic expansion throughout the Mid-Atlantic Ridge, Chorocaris sp. 2 population structure between basins appears to be the product of infrequent immigration followed by extended periods of isolation.

4.4.2 Limpet population structure

In contrast to Chorocaris sp. 2, Olgasolaris tollmanni sampled from hydrothermal vents in Manus and Lau Basins clustered into one, well-mixed, population with no apparent subdivision or cryptic speciation, suggesting that O. tollmanni is panmictic between Manus and Lau Basin with frequent migration. The intermediate position of

North Fiji Basin between these two basins, the evidence for connectivity among North

Fiji and Lau Basins for other hydrothermal vent species (i.e. Bathymodiolus spp. - Moraga et al. 1994, Alviniconcha spp. - Kojima et al. 2001; Suzuki et al. 2006, Ifremeria nautilei -

Thaler et al. 2011), and deep-water current profiles of the region (Thurnherr, unpublished data, available at http://www.ldeo.columbia.edu/~ant/LAUB-FLEX/) suggest that there may be a single, panmictic population of Olgasolaris tollmanni distributed throughout Manus, North Fiji, and Lau Basins. Geologically, these three basins are all relatively young, having formed over the last 10 million years (Muller et al.

2008). Of these three basins, North Fiji is the oldest (approximately 10 million years old;

(Auzende et al. 1988), while both Manus and Lau Basin are approximately 4 million years old (Taylor 1979; Taylor et al. 1996). The relative age of North Fiji Basin suggests that it may have functioned either as a source of propagules for O. tollmanni in the southwest Pacific, or as a stepping stone for ancient migrants between Manus and Lau

Basin.

Mitochondrial DNA represents only one locus and is maternally inherited, thus it does not reveal a complete picture of population structure. The suggestion of panmixia might be challenged if additional genetic markers (microsatellites, single nucleotide polymorphisms, or nuclear sequences) are employed.

4.4.3 Conclusions

A variable dispersal filter exists in the western Pacific that isolates populations of some, but not all, vent-occurring species from Manus Basin. In addition to Chorocaris sp.

2, three habitat-building deep-sea snails (Ifremeria nautilei, Alviniconcha sp.1, and

Alviniconcha sp. 2) also exhibit strong population isolation between Manus Basin and other oceanic basins within their known biogeographic ranges (Denis & Jollivet 1993;

Kojima et al. 2001; Suzuki et al. 2006; Thaler et al. 2011). Olgasolaris tollmanni is the first vent-associated species examined to date that exhibits no population differentiation between Manus Basin and other western Pacific back-arc basins, although results from other, more powerful genetic markers, could challenge this conclusion. The lack of

population structure among O. tollmanni individuals provides some support for the hypothesis that connectivity is influenced by the degree of vent dependence, and that species that can take advantage of non-vent habitat may exhibit greater connectivity across broad spatial scales. That the population structure of Chorocaris sp. 2 resembles that of the endosymbiont-hosting Ifremeria nautilei suggests that Chorocaris sp. 2 may have more restrictive habitat needs than O. tollmanni.

Variable dispersal filters have been documented from hydrothermal vents in the eastern Pacific, often associated with geomorphological features (i.e. Easter Microplate –

(Won et al. 2003; Hurtado et al. 2004); Blanco Transform Fault – (Johnson et al. 2006;

Young et al. 2008) or hydrodynamic gyres (i.e. Hurtado et al. 2004). Hydrothermal vents in the eastern Pacific are distributed in a roughly linear pattern (Marsh et al. 2001;

Thomson et al. 2003), with population differentiation often occurring along a north/south gradient (Young et al. 2008; Plouviez et al. 2009; Plouviez et al. 2010; Matabos et al. 2011; with the notable exception of the Galapagos Spreading Center - Vrijenhoek 2010). In the western Pacific, vents are not distributed linearly, and hydrothermal vent fields occur patchily throughout spreading centers associated with back-arc basins (Desbruyères et al. 2006). This non-linear distribution of western Pacific hydrothermal vents leads to more complex patterns of population differentiation and gene flow.

We previously hypothesized that the formation of the New Guinea archipelago created a physical barrier to migration (Thaler et al. 2011) while the north-westward path

of the St. George’s Undercurrent may reduce the potential for propagules to disperse from Manus towards North Fiji and Lau Basins (Zenk et al. 2005). Larval transport models based on an assumption of long-lived, lecithotrophic, deep-sea larvae indicate that few larvae would be transported into Manus Basin from surrounding regions, even after 500 days of dispersal (Yearsley & Sigwart 2011). Chorocaris sp. 2 distribution is consistent with the pattern of distribution seen in Ifremeria nautilei, suggesting that genetic differentiation between Manus and other western Pacific Basins is the result of infrequent colonization and prolonged isolation. The high degree of connectivity among

Olgasolaris tollmanni indicates that pathways for effective dispersal must exist between

Manus and Lau Basin.

Understanding patterns of connectivity among and within populations is essential for developing best practices for managing and mitigating anthropogenic disturbance and establishing a conservation framework prior to exploitation, especially among relatively unexplored ecosystems such as hydrothermal vents (Van Dover 2011).

In Manus Basin, Solwara I is a target for deep-sea mineral extraction, with South Su set aside as a refuge during extraction (Coffey Natural Systems 2008). The high degree of connectivity among all three sites in Manus Basin suggests that the loss of genetic diversity in populations of Chorocaris. sp. 2 and Olgasolaris tollmanni may be minimal and that there is potential for the re-colonization of Solwara I by South Su, Solwara 8, and other, unstudied sites in the region following mineral extraction, provided that the

effective population size is not severely reduced and critical stepping stones for larval dispersal remain. For at least two species (Chorocaris sp. 2 and Ifremeria nautilei) Manus

Basin hosts isolated populations that are poorly connected to the rest of the western

Pacific. While hydrothermal vent communities in Manus Basin may be well connected and resilient to some degree of anthropogenic disturbance, it is unclear what effect sustained or sequential disturbance across multiple sites could have on these isolated populations.

Appendix A. Summary Statistics for Ifremeria nautilei microsatellites

Solwara 8 Solwara 1 South Su S S S W M M M W M P M P M S M M P P M P P 8 1 2 3 1 4 1 5 2 6 u 7 8 4 5 9 6 7 I n 50 19 18 13 56 14 8 27 23 15 51 13 19 7 8 19 8 11 f a 3 2 3 2 3 3 2 3 3 2 4 2 2 3 2 4 2 4 r Rs 3.00 2.00 2.72 2.00 2.99 2.93 2.00 2.48 2.30 2.00 3.96 2.00 2.00 2.00 2.00 3.37 2.00 3.27

0 P A 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 HE 0.51 0.48 0.51 0.54 0.50 0.50 0.53 0.51 0.50 0.51 0.52 - - - - 0.55 - 0.61 0 HO 0.52 - 0.06 - 0.34* 0.07 - 0.03 0.04 - 0.59 - - - - 0.11 - 0.18 I n 49 19 18 12 65 19 11 30 25 16 57 12 24 10 10 21 11 10 f a 6 4 6 3 3 3 3 3 3 3 5 4 5 3 5 5 4 4 r Rs 6.00 3.96 4.97 3.00 3.00 3.00 3.00 3.00 3.00 3.00 4.98 4.00 3.99 3.00 5.00 4.14 3.90 4.00

0 P A 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 HE 0.7 0.7 0.6 0.7 0.6 0.7 0.6 0.7 0.7 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.7 0.7 3 HO 0.65 0.42 0.39 0.50 0.55 0.42 0.27 0.40 0.44 0.25 0.67 0.58 0.58 0.70 0.60 0.52 0.64 0.40 I n 48 17 18 13 63 17 11 32 28 14 51 11 19 8 9 21 10 11 f a 3 3 2 2 3 3 3 3 3 3 3 2 3 2 3 3 2 3 r Rs 3.00 2.99 2.00 2.00 3.00 2.88 2.73 2.89 2.82 2.96 3.00 2.00 2.58 2.00 2.89 2.52 2.00 2.73

0 P A 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 HE 0.5 0.6 - - 0.6 0.6 0.6 0.6 0.6 0.6 0.5 - 0.5 - 0.5 0.5 - 0.6 2 HO 0.60 0.24 - - 0.59 0.12 0.09 0.15 0.18 0.14 0.53 - 0.50 - 0.11 0.50 - 0.09 I n 44 18 13 13 65 18 11 33 28 14 55 11 23 8 11 21 10 11 f a 4 4 4 4 5 4 4 5 4 4 4 4 4 4 4 4 4 4 r Rs 4.00 3.86 3.84 3.98 4.68 3.95 3.93 3.89 3.25 3.96 4.00 4.00 3.87 4.00 3.73 3.78 3.80 3.73

0 P A 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 HE 0.63 0.65 0.65 0.49 0.64 0.66 0.73 0.60 0.59 0.72 0.65 0.50 0.68 0.66 0.70 0.68 0.64 0.71 8 HO 0.66 0.22 0.69 0.46 0.65 0.44 0.45 0.42 0.43 0.50 0.73 0.36 0.52 0.62 0.36 0.48 0.40 0.55 I n 48 17 17 14 63 18 11 32 27 13 57 13 24 10 11 20 9 11 f a 2 2 2 2 2 2 2 2 2 2 5 4 3 3 2 2 2 2 r Rs 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 4.53 4.00 2.54 2.90 2.00 2.00 2.00 2.00

0 P A 0 0 0 0 0 0 0 0 0 0 3 2 1 1 0 0 0 0 7 HE 0.47 - - - 0.44 - - - - - 0.51 0.59 0.50 0.57 - - - - 8 HO 0.44 - - - 0.44 - - - - - 0.54 0.08 0.04 0.10 - - - - I n 51 19 18 14 60 17 9 27 22 16 53 10 24 9 11 19 9 10 f a 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 r Rs 3.00 2.69 2.80 2.91 3.00 2.92 3.00 2.67 2.69 2.85 3.00 3.00 2.89 3.00 2.00 2.96 3.00 3.00

0 P A 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 HE 0.29 0.24 0.34 0.32 0.31 0.36 0.31 0.27 0.29 0.33 0.40 0.28 0.41 0.39 - 0.46 0.52 0.43 6 HO 0.29 0.16 0.11 0.00† 0.32 0.18 0.22 0.11 0.14 0.13 0.36 0.10 0.08 0.00 - 0.21 0.22 0.20 I n 50 19 17 14 66 17 10 33 28 16 57 12 23 9 10 22 11 11 f a 19 16 12 12 18 15 10 16 15 12 15 11 11 10 9 12 9 9 r Rs 19.0 12.7 10.3 11.1 17.0 12.5 9.4 11.6 10.1 10.9 14.7 11.0 9.7 10.0 8.6 9.6 8.1 8.6

0 P A 2 1 0 1 1 0 0 1 1 0 3 1 2 1 1 0 0 0 9 HE 0.92 0.93 0.90 0.89 0.91 0.93 0.88 0.92 0.92 0.90 0.90 0.93 0.89 0.93 0.87 0.89 0.87 0.91 3 HO 0.84 0.84 0.71 0.86 0.82 0.82 0.80 0.73 0.71 0.63 0.84 0.75 0.78 0.67 0.80 0.82 0.73 0.91 I n 50 18 18 14 64 19 11 29 26 16 53 12 23 9 11 18 7 11 f a 5 3 4 4 5 3 2 4 4 3 5 4 3 3 2 4 3 3 r Rs 5.00 2.90 3.53 3.57 4.56 2.50 1.88 3.24 2.69 2.69 4.89 4.00 2.55 2.74 1.64 3.63 3.00 2.60

0 P A 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 HE 0.33 0.44 0.30 0.21 0.23 0.15 0.17 0.31 0.31 0.18 0.30 0.43 0.17 0.31 0.09 0.39 0.47 0.32 4 HO 0.32 0.44 0.17 0.21 0.23 0.16 0.18 0.31 0.31 0.19 0.21† 0.25 0.09 0.11 0.09 0.17 0.29 0.09 I n 50 18 18 14 61 15 10 30 25 16 42 10 21 9 9 11 2 9 f a 4 4 3 4 4 3 2 4 4 4 4 3 3 3 2 2 2 2 r Rs 4.00 3.36 2.80 3.43 3.97 2.67 1.79 2.89 2.07 3.73 4.00 3.00 2.86 1.81 1.76 2.00 2.00 1.91

1 P A 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 HE 0.55 0.48 0.49 0.52 0.51 0.42 0.44 0.55 0.55 0.50 0.49 0.47 0.42 0.39 0.42 0.52 0.67 0.52 3 HO 0.56 0.56 0.33 0.50 0.54 0.40 0.40 0.53 0.44 0.38 0.52 0.60 0.38 0.22 0.56 0.55 1.00 0.44 93

Basins North Fiji Sites N M a F n i L u j a W W M Locus s i u L R H I n 157 97 37 33 28 36 f a 4 5 4 4 3 4 r Rs 2.90 3.74 4.00 3.15 2.78 2.61

0 P A 0 1 0 0 0 1 4 HE 0.51 0.50 0.50 0.50 0.52 0.47 0 HO 0.48 0.51 0.29 0.42 0.43 0.64 I n 171 83 32 20 29 34 f a 7 5 4 4 3 4 r Rs 4.53 4.33 4.00 3.43 2.59 3.41

0 P A 2 0 0 0 0 0 4 HE 0.65 0.42 0.40 0.56 0.44 0.28 3 HO 0.61 0.33 0.28 0.45 0.24 0.32 I n 162 78 18 21 29 28 f a 3 4 2 3 2 3 r Rs 2.84 2.46 2.00 2.23 2.00 2.17

0 P A 0 1 0 0 0 1 5 HE 0.54 0.49 0.46 0.49 0.48 0.51 2 HO 0.52 0.50 0.44 0.48 0.52 0.57 I n 164 75 29 22 31 22 f a 5 11 12 6 9 10 r Rs 4.15 8.99 12.00 4.77 4.60 7.92

0 P A 0 0 0 0 0 0 6 HE 0.64 0.74 0.85 0.69 0.88 0.60 8 HO 0.42 0.29† 0.34† 0.64* 0.71 0.55† I n 168 98 39 26 34 38 f a 5 5 3 3 3 3 r Rs 2.70 3.19 3.00 2.39 2.29 2.26

0 P A 2 1 0 0 1 0 7 HE 0.47 0.42 0.45 0.47 0.36 0.44 8 HO 0.46 0.54* 0.54 0.62 0.62 0.45 I n 173 99 36 26 30 43 f a 23 13 11 11 9 11 r Rs 15.94 11.05 11.00 6.97 6.22 6.69

0 P A 6 1 0 1 0 0 9 HE 0.91 0.79 0.76 0.82 0.79 0.76 3 HO 0.78 0.77† 0.81 0.81 0.73 0.77 I n 167 104 44 28 32 44 f a 5 5 4 4 3 4 r Rs 4.20 4.07 4.00 3.09 2.76 2.88

0 P A 0 1 0 0 0 1 9 HE 0.28 0.38 0.37 0.31 0.32 0.49 4 HO 0.23 0.42 0.43 0.29 0.59 0.39 I n 153 105 40 28 33 44 f a 4 2 2 2 2 2 r Rs 3.90 2.00 2.00 2.00 2.00 2.00

1 P A 0 0 0 0 0 0 0 HE 0.52 0.50 0.51 0.51 0.50 0.51 3 HO 0.46 0.97† 0.95* 1.00† 1.00† 0.93† 95

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Biography

Andrew David Thaler

Born 9 March 1984

Education

2007 B.S. Biology, Duke University, Durham, North Carolina

Technical Publications

Thaler AD, Zelnio K, Saleu W, Schultz TF, Carlsson J, Cunningham C, Vrijenhoek R, Van Dover CL (2011) The effects of spatial scale on the population dynamics of Ifremeria nautilei, a hydrothermal vent endemic gastropod from the southwest Pacific. BMC Evolutionary Biology, 11(1); 372 Thaler AD, Vilgalys R, Van Dover CL (2011) Ascomycete phylotypes recovered from a Gulf of Mexico methane seep are identical to an uncultured deep-sea fungal clade from the Pacific. Fungal Ecology. doi:10.1016/j.funeco.2011.07.002. Schultz TF, Hsing PY, Eng A, Zelnio KA, Thaler AD, Carlsson J, Van Dover CL (2010) Characterization of 18 polymorphic microsatellite loci from Bathymodiolus manusensis (Bivalvia, Mytilidae) from deep-sea hydrothermal vents. Conservation Genetics Resources. doi: 10.1007/s12686-010-9272-8 Zelnio KA, Thaler AD, Jones RE, Saleu W, Schultz TF, Van Dover CL, Carlsson J (2010) Characterization of nine polymorphic microsatellite loci in Chorocaris sp. (Crustacea: Caridea: Alvinocarididae) from deep-sea hydrothermal vents. Conservation Genetics Resources. doi:10.1007/s12686-010-9243-0. Thaler AD, Zelnio KA, Jones R, Carlsson J, Van Dover CL, Schultz T (2010) Characterization of 12 polymorphic microsatellite loci in Ifremeria nautilei, a chemoautotrophic gastropod from deep-sea hydrothermal vents. Conservation Genetics Resources. doi:10.1007/s12686-010-9174-9. Forward RB, Thaler AD, Singer R (2007) Entrainment of the activity rhythm of the mole crab Emerita talpoida. Journal of Experimental Marine Biology and Ecology, 341(1); 10 – 15.

Book Chapters

Thaler AD, Zelnio KA, Freitag A, MacPherson R, Shiffman D, Bik H, Goldstein M, McClain C (2012) Digital Environmentalism: Tools and strategies for the evolving

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online ecosystem. In D. Gallagher (Ed.), Environmental Leadership: A Reference Handbook. SAGE Reference.

Grants and Fellowships

InterRidge/ISA Fellowship: Capacity building: Training and professional development for a visiting scholar from Papua New Guinea $5000 2012 Consortium of Open Access Publishing Equity publication grant $1915 2011 NESCent Travel Award $200 2011 Halsted Fellowship Full Stipend 2011 – 2012 Oak Foundation Independent Study Advisor Fellowship Full Stipend 2010 – 2011 Oak Foundation support for ConGen Workshop $1,100 2009 Ridge2000 Student Travel Grant $1,000 2009 ChEss Student Travel Grant $1,000 2009 Oak Foundation – Fungal diversity in Gulf of Mexico methane seeps $1,997 2009 InterRidge Student Travel Grant $500 2007

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