Genetic Structure of Two Endemic Marine Invertebrates, Ophionereis Schayeri and Cellana Tramoserica, Along the South-Eastern Coast Of

Genetic structure of two endemic marine invertebrates, Ophionereis schayeri and Cellana tramoserica, along the south-eastern coast of

Australia

Fiona G. Andrews

Master of Research

Western Sydney University 2019 Statement of Authentication

The work presented in this thesis is, to the best of my knowledge and belief, original except as acknowledged in the text. I hereby declare that I have not submitted this material, either in full or in part, for a degree at this or any other institution.

……… (Signature)

Acknowledgements

I am extremely grateful to Sebastian Holmes for his generosity and support which helped me to develop both as a scientist and person over the course of this

Master of Research. There were many times when I felt close to failure, and it was only his faith which gave me the strength to continue. Thanks must also go to Karen

Stephenson for assistance in the lab and Mailie Gall for support with field work.

Existing and proposed outputs from this thesis

This thesis has been written as a series of independent manuscripts, as such there is some repetition of general information in the individual chapters. Chapter 1 provides a general introduction. Chapter 4 provides a summary and synthesis of the proceeding chapters and outlines directions for future research.

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Table of Contents Statement of Authentication………………………………………………………….ii Acknowledgements.….………………..…………………………………………….iii Existing and proposed outputs from this thesis………...……………………………iii Contents……………………………………………………………………………...iv List of Tables…………………………………………………………………….…..vi List of Figures………………………………………………………………….……vii

CHAPTER 1 General introduction 1.1 Drivers influencing the genetic structure of shallow subtidal and intertidal marine invertebrates 1.1.1 Dispersal in the marine environment…………………………………………...1 1.1.2 Drivers which influence population divergence…………………………….….5 1.1.3 Drivers influencing range sizes and limits……………………………………...8

1.2 Drivers influencing the genetic structure of marine invertebrates along the south-eastern coast of Australia 1.2.1 Biogeography of the south-eastern coastline of Australia…………………….10 1.2.2 Oceanography of south-eastern Australia……………………………………..11

1.3 Thesis aims and structure……………………………………………………..13 CHAPTER 2 Development of microsatellites and application to two populations of Schayer's brittle star (Ophionereis schayeri (Müller & Troschel, 1844)) in south-eastern Australia 2.1 Abstract………………………………………………………………………….14 2.2 Introduction……………………………………………………………………...15 2.3 Materials and methods…………………………………………………………..18

2.3.1 Sampling acquisition….……………………………………………….18 2.3.2 DNA Extractions….…………………………………………………...18 2.3.3 Microsatellite development….…………………………………….…..19 2.3.4 Application of markers to populations.….…………………………….19

2.3.5 Data Analysis…..……………………………………………………..20 2.4 Results…..……………………………………………………………………….23 2.5 Discussion..…...………………………...……………………………………….26 2.6 Conclusion..……………………………………………………………………..29

CHAPTER 3 The genetic structure of the intertidal limpet (Cellana tramoserica (Holten, 1802)) in south-eastern Australia: microsatellite development and application 3.1 Abstract………………………………………………………………………….30 3.2 Introduction……………………………………………………………………...31 3.3 Materials and methods…………………………………………………………..35

3.3.1 Sampling acquisition.………………………………………………….35 3.3.2 DNA Extractions….…………………………………………………...35 3.3.3 Microsatellite development….…………………………………….…..36 3.3.4 Application of markers to populations.….…………………………….36 3.3.5 Data analysis…….…………..………...……………………...….……37 3.4 Results…………………………………………………………………………...41 3.5 Discussion……………………………………………………………………….46 3.6 Conclusion...…………………………………………………………………….51

CHAPTER 4 General discussion 4.1 Summary of findings…………………………………………………………….52 4.2 Synthesis…………….…………………………………………………………..53

4.2.1 The effects of oceanography on dispersal patterns.…………………...53 4.2.2 The effects of PLD on dispersal capacity….………………………….53 4.2.3 The factors influencing population divergence….…………………….54 4.2.4 Future research……………………….….…………………………….55

References………………………………………………………………………….57

List of Tables

Table 2.1: Primer sequences and characteristics of eleven microsatellite loci for O. schayeri from Little Bay and Bass Point. Number of alleles (Na).

Table 2.2: Microsatellite variation across loci including the number of individuals genotyped (N), the number of alleles (Na), the effective number of alleles (Ne), the observed heterozygosity (HO), the expected heterozygosity (HE), the unbiased expected heterozygosity (UHE) and the inbreeding coefficient (FIS).

Table 2.3: Analysis of molecular variation (AMOVA) partitioning the genetic variation in the microsatellites using FST among all populations of O. schayeri sampled at Little Bay and Bass Point, New South Wales.

Table 2.4: Analysis of molecular variation (AMOVA) partitioning the genetic variation in the microsatellites using RST among all populations of O. schayeri sampled at Little Bay and Bass Point, New South Wales.

Table 3.1: Primer sequences and characteristics of ten microsatellite loci for C. tramoserica. Number of alleles (Na).

Table 3.2: Microsatellite variation across populations including the number of individuals genotyped (N), the number of alleles (Na), the effective number of alleles (Ne), the observed heterozygosity (HO), the expected heterozygosity (HE), the unbiased expected heterozygosity (UHE) and the inbreeding coefficient (FIS).

Table 3.3: Analysis of molecular variation (AMOVA) partitioning the genetic variation in the microsatellites using F-statistics among all populations of C. tramoserica sampled at Little Bay, Ulladulla, Bermagui, Mallacoota, Walkerville and Lauderdale.

Table 3.4: Analysis of molecular variation (AMOVA) partitioning the genetic variation in the microsatellites using R-statistics among all populations of C. tramoserica sampled at Little Bay, Ulladulla, Bermagui, Mallacoota, Walkerville and Lauderdale.

Table 3.5: Population pairwise FST (above the diagonal)/RST values (below the diagonal) for the microsatellite data set. Bold denotes no significant difference between population following pairwise comparisons (9999 iterations) and a Bonferroni correction.

List of Figures

Figure 2.1: Google Earth image highlighting the location of the sampling sites at Little Bay and Bass Point in New South Wales.

Figure 3.1: Google Earth image highlighting the location of the sampling sites at Little Bay, Ulladulla and Bermagui in New South Wales, Mallacoota and Walkerville in Victoria and Lauderdale in Tasmania.

Figure 3.2: Estimated population structure produced by STRUCTURE for K = 3 (top plot) and K = 4 (bottom plot) from CLUMPAK Distruct. Each individual is represented by a thin vertical line, which is partitioned into K coloured segments with solid black lines denoting different populations.

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Chapter 1

General Introduction

1.1 Drivers influencing the genetic structure of shallow subtidal and intertidal marine invertebrates

1.1.1 Dispersal in the marine environment

Dispersal is fundamental to the long-term survival of populations and species influencing population dynamics (Roughgarden et al. 1988), population genetics

(Cowen & Sponaugle 2009), biogeography (Scheltema 1986) and speciation

(Claramunt et al. 2012). It is the primary mechanism by which new genetic material is introduced into populations, decreasing the likelihood of population extinction in response to environmental, genetic, demographic and/or catastrophic stochasticity

(Frankham et al. 2002). Knowledge of the factors that influence species dispersal capacity is important for the long-term management of populations and predicting their vulnerability to future perturbation (Cowen et al. 2006).

Marine invertebrates disperse through several mechanisms including the production of planktonic larva (Cowen & Sponaugle 2009), floating on mucous- strands (Yavelow et al. 1979), byssal thread drifting (Martel & Chia 1991) and rafting

(Cumming et al. 2014). For marine species which are fixed (sedentary) as adults, maintenance of population connectivity can only occur through the production of dispersive larvae (Cowen & Sponaugle 2009). Such larvae can be classified into three groups (Thorson & Jørgensen 1946, Thorson 1950) as follows: i) pelagic larvae - characterized by larvae with a dispersive phase in the water column; and ii) direct

1 developers - where the larvae hatch from external eggs/egg masses as juveniles

(ovivipary) or the larvae complete development within the parent and are released as juveniles (ovovivipary). Pelagic larvae can be further subdivided based on their duration in the water column, i.e. their pelagic larval duration or PLD: i) teleplanic who have an extended pelagic duration (> 1 month); ii) actaeplanic who have an intermediate duration (5 days to 4 weeks); and iii) anchiplanic who have a short duration (minutes to 4 days) (Havenhand 1995). Larvae can be feeding

(planktotrophic) or non-feeding (lecithotrophic), with planktotrophic larvae generally having a longer PLD (days to years) than lecithotrophic larvae (hours to months).

The dispersal capacity of a species is often equated with the time its larvae spend in the water column, with the prediction that species with long PLD’s will have widespread dispersal capacity whereas those with a short PLD will have restricted dispersal abilities (Siegel et al. 2003, Shanks 2009). The theory that life-history predicts a species dispersal capacity, is long standing (Mayr 1954, Mileikovsky 1971,

Palumbi 1994) and there are many studies which support it (Waples 1987, Doherty et al. 1995, Bohonak 1999). For example, populations of the gastropod Nerita melanotragus with a planktonic phase of 2 - 3 months show no differences in genetic structure at the scale of 750 km (Reisser et al. 2014). Correspondingly for species that lack a dispersive stage or have a short-lived PLD, Schopf (1977) found genetic differences between populations of the bryozoan Schizoporella errata separated by 10 km whose larvae have a 2-day PLD (see also Goldson et al. 2001, Hoffman et al. 2011 for supporting meta-analyses/reviews). Support for the paradigm also comes from species with planktonic larvae that have trans-oceanic distributions, e.g. Schletema

(1986) found that 81 % of prosobranch molluscs that produced teleplanic larvae had

Atlantic-wide distributions.

Although the paradigm relating PLD with dispersal capacity is intuitive, many studies find a marginal or absent relationship between PLD and dispersal capacity.

Colson and Hughes (2004) found that populations of the dog-whelk Nucella lapillus, which hatch directly from benthic eggs, were re-colonised by individuals originating from sites ranging anywhere from 10 - 100 km apart (see Weersing & Toonen 2009 and Selkoe & Toonen 2011 for meta-analyses/reviews which find poor support for a relationship). The ‘Paradox of Rockall’ is a classic example of how PLD and dispersal are poorly related for some species. In this case, the rocky shores of an island (Rockall) in the middle of Atlantic are dominated by direct-developing fauna (Johannesson

1988). The widespread, trans-continental distributions of shallow-water species that lack or have a short-lived planktonic phase (e.g. direct developers) also indicate that other vectors (e.g. rafting) facilitate long distance dispersal (Cumming et al. 2014) (see also O’Hara et al. 2104, Waters et al. 2014, Richardson et al. 2016 for review).

Dispersal can be measured in a variety of ways: i) by tracking individuals using population specific markers (chemical composition); ii) by tagging individuals with stains, radiotracers, rare-earth minerals (Levin 1990); and iii) indirectly, by examining the population genetic structure. Due to the microscopic size of larvae and the size of the oceans, tracking larvae is logistically unfeasible for most species, except those that have a very limited and short-range dispersal capacity (Davis & Butler 1989, Bingham

& Young 1991). Genetic tools allow for an inference to be made about the dispersal capacity of a species based on the assumption that the degree of genetic similarity between populations will correspond to the degree of dispersal (Avise 1994). High levels of dispersal should enhance the exchange of genetic material thereby reducing genetic divergence between populations and conversely, restricted dispersal will lead

3 to greater divergence between populations (Scheltema 1971, Hedgecock 1986,

Hedgecock et al. 2007).

Genetic tools can also provide insight into the number of individuals migrating into a population, the population of origin (assignment testing), and heritage (parental analysis). Classically, gene flow is measured using FST and analogues, which relate the number of migrants per generation to the level of genetic differentiation (Holsinger &

Weir, 2009). This allows estimation on the number of migrants entering a population

(FST = 1/(1+4Nm), where Nm is number of migrants). FST-based estimates have limitations, for example they cannot distinguish patterns arising from historic vs recent processes. For example, large differences in FST can reflect recent restrictions in gene flow or historic bottlenecks (Grosberg & Cunnigham, 2001). Whilst FST also tends to provide an 'average' estimate of migration over many generations, with limited resolution in identifying temporal or cohort variation for each generation. The level of gene flow necessary to influence FST is minute, one migrant per generation is enough to counter random genetic drift and in the absence of differential selection pressure prevent population differentiation (Wright 1965, see also Mills & Allendorf 1996).

The relationship between FST and Nm is reciprocal meaning that as FST approaches zero, migration values quickly inflate, for instance, an FST of 0.0025 and 0.000025 corresponds to 100 and 10,000 migrants respectively (Palumbi, 2003). Thus, as FST gets smaller and approaches zero, the ability to accurately estimate migration decreases.

Different types of genetic markers have varying power to resolve genetic structure because of their characteristics, i.e. rates of mutation, modes of inheritance and degrees of polymorphism. Markers which mutate at a fast pace (e.g.

4 microsatellites, single nucleotide polymorphisms (SNPs)) are best at identifying fine- scale and recent (contemporary) genetic changes (Selkoe & Toonen, 2006), however, they are less useful for identifying the signature of historic/evolutionary processes.

Markers which mutate at a slow pace (e.g. 16S, cytochrome oxidase 1) have limited power to resolve contemporary processes but are excellent at identifying historical divergence. For instance, the cytochrome oxidase 1 genetic region mutates at a slow pace (e.g. 3.1 % per million years for sea urchins, McCartney 2000, Palumbi 1996) with a small level of divergence implying population separation for hundreds or thousands of years. The ability of these types of markers to retain signatures of historical processes makes them useful in evolutionary studies, and for constructing genealogies and phylogenetic trees (Patwardhan et al. 2014). However, their

'conservative' nature means an absence of genetic differences does not necessarily imply panmixia. Hence, the results of genetic studies need to be interpreted with consideration of the marker implemented (Qu et al. 2012), with a range of markers employed so that a comprehensive picture of the processes effecting genetic structure can be understood (Flanders et al. 2009).

1.1.2 Drivers which influence population divergence

In the marine environment there are few visible barriers to gene flow in contrast to the terrestrial environment, where mountain ranges and land breaks are apparent.

The absence of visible barriers combined with the perception that currents homogenise the oceans has fuelled the paradigm that most marine populations are open (Cowen et al. 2000). However, in the marine environment, there are many factors that can influence dispersal and hence drive population divergence, including vicariance (e.g. landbridges such as Wilson’s Promontory (Mirams et al. 2011)), ocean currents (White

5 et al. 2010, Coleman et al. 2011b), habitat (Riginos & Nachman 2001, Selkoe et al.

2010) and life history considerations (Bohonak 1999). Isthmuses or ‘landbridges’ are submarine landmasses which emerge with climatic change due to drops in sea levels.

The Pleistocene (2.6 Ma – 11.7 Ka) was characterized by climatic oscillations which saw periodic sea level fluctuations and the emergence of isthmuses around the world

(Ludt & Rocha 2015). Genetic divergence (a historical signature) across or in the vicinity of isthmuses has been shown in many taxa (Waters 2008, Mirams et al. 2011), although not always consistently. For example, Mirams et al. (2011) found only 8 of

13 species showed genetic divergence consistent with the emergence of the Torres

Strait landbridge. Such inconsistencies may be derived from life-history considerations (Pelc et al. 2009), maintenance of population connectivity through alternative dispersal routes (Mirams et al. 2011) and/or erosion of the signature by contemporary gene flow (Pelc et al. 2009).

Marine larvae can be considered passive particles at a large scale, having a limited capacity to counter the direction of prevailing currents (i.e. they have low

Reynolds numbers, Vogel 1994). However, at a fine scale, marine larvae can choose to settle or resuspend if a surface is encountered (for contrary viewpoint see Rittschof et al. 1998). Hence the dispersal patterns of larvae, irrespective of their larval duration, are likely to be influenced by currents (White et al. 2010, Thompson et al. 2018).

Oceanographic features such as frontal systems can drive population divergence by acting as barriers to larval exchange. Galarza et al. (2009) found 6 species of fish with contrasting life-histories showed genetic divergence across a frontal system, indicating oceanographic features outweighed life history considerations (Gilg & Hilbish 2003).

Meso-scale features such as eddies can sweep large packets of larvae in directions

6 counter to the prevailing flow creating patchy or homogenous patterns of genetic structure (Selkoe et al. 2006, Selkoe et al. 2014).

The direction of alongshore currents can be altered by physical features such as headlands which can promote larval retention or facilitate their transfer offshore

(Morgan et al. 2011). Estuarine and nearshore environments can also alter genetic structure by promoting the retention of larvae (Siegle et al. 2013) and variation in the strength of currents may alter the level of connection between populations (Galindo et al. 2010, Coleman et al. 2011b). Coleman et al. (2011b) found that patterns of isolation by distance (IBD) varied according to strength of three different boundary currents.

Correspondingly, temporal shifts in the direction of currents may alter larval sources and limit mixing between larval pools driving fine-scale temporal and spatial variation in genetic structure (Carson et al. 2010). Selkoe et al. (2006) found seasonal shifts in the direction of prevailing currents corresponded with unique cohort genetic signatures.

Stochasticity in current trajectories can drive patchy structure across broad temporal and spatial scales. Johnson and Black (1982) found substantial heterogeneity in genetic structure of the intertidal limpet Siphonaria with the genetic structure being different between years within a site, and amongst sites along a 50 m shoreline (see also Hogan et al. 2010, Broquet et al. 2013 for examples). Conventionally such seemingly random patterns in population structure at fine-spatial scales which cannot be attributed to any obvious environmental factors, have been known as ‘chaotic genetic patchiness’, arising through variation in the larval pool, recruitment and selection (Johnson & Black 1982, Toonen & Grosberg 2011). More recently, it has been suggested this fine-scale and stochastic heterogeneity is not ‘background noise’,

7 but instead may relate to environmental variation not considered before. Selkoe et al.

(2010) identified very low levels of genetic differentiation between populations of the kelp bass Paralabrax clathratus, Kellet’s whelk Kelletia kelletii and the California spiny lobster Panulirus interruptus, in the Southern California Bight, where kelp bed size was found to be an informative predictor for all species. Habitat availability can also influence genetic structure, where in its absence, the exchange of propagules between populations may be restricted or absent (Riginos & Liggins 2013). Riginos and Nachman (2001) examined the population genetics of the rocky shore blennie

Axoclinus nigricaudis and found that populations separated by sandy habitats exhibited greater genetic divergence than populations between rocky shores.

Overwhelming the signature of contemporary processes can be historical events, the ‘ghosts of dispersal past’ (Marko 2004, Crandall et al. 2014). The signature of historical population expansion/contraction (Marko et al. 2010, Moore & Chaplin

2014), transient population separation by isthmuses (Waters 2008), or historical oceanography (Fraser et al. 2009) can persist in the genome of species despite current day gene flow. Attribution of genetic differences to the correct process requires disentangling historical signatures from contemporary ones, which can sometimes be achieved by using different molecular markers, e.g. microsatellites (contemporary) versus conventional gene (e.g. COI) sequencing (historical) (Flanders et al. 2009, Qu et al. 2012, Strugnell et al. 2012).

1.1.3 Drivers influencing range sizes and limits

A species range can be a spatial reflection of the breadth of its fundamental niche (Brown 1984). Brown’s hypothesis (1984) suggests that the breadth of species fundamental niche determines the extent of its geographical range. When a species

8 fundamental niche is defined by its physiological tolerance, widespread species can be considered to have broad physiological tolerance for a range of environmental conditions, and the opposite for species with small range sizes. Although a species niche can be defined in several ways (e.g. diet, habitat), a recent meta-analysis found that a species environmental tolerance was the strongest predictor of range size

(Slatyer et al. 2013). Tolerance for temperature variation may be one of the most important drivers of this relationship, as suggested by the climate variability hypothesis (CVH, Stevens 1989) which attributes the breadth of thermal variation a species experiences with its range extent (Bozinovic et al. 2011). Mos et al. (2017) found the river swimming crab Varuna litterata have extended their range southward by 290km due to warming temperatures although may be living on the limit of their thermal tolerance.

Dispersal ability has been suggested to determine the extent of a species range

(Lester et al. 2007) and in the marine environment dispersal capacity has been defined by pelagic larval duration (PLD). Positive relationships between range size and dispersal ability have been shown in fish (Bonhomme & Planes 2000, Zapata & Herrón

2002), gastropods (Scheltema 1971, Hansen 1980, Jablonski & Lutz 1983), echinoderms (Emlet 1995, Jeffery et al. 2003) and cowries (Paulay & Meyer 2006).

However, other studies find no relationship and have suggested that environmental factors can override any biological pre-disposition. Lester & Ruttenberg (2005) found a positive relationship between PLD and dispersal capacity for fish in the Indo-Pacific

Ocean but not for fish across the Atlantic Ocean, which they suggest relates to habitat availability and ocean basin size.

1.2 Drivers influencing the genetic structure of marine invertebrates in south-east Australia

1.2.1 Biogeography of the south-eastern coastline of Australia

The south-east Australian coastline is characterized by several barriers to dispersal corresponding to regional biogeographic patterns divided into three faunal provinces, the Peronian, Flindersian and Maugean (Bennett & Pope 1953). The

Peronian province extends from southern Queensland to Bass Strait, the Flindersian from Bass Strait to Geraldton and the Maugean across Bass Strait and Tasmania

(O'Hara & Poore 2000). The forces involved in maintaining these terrestrial systems are poorly understood, but patterns may be shaped by a combination of historic and contemporary forces (Waters 2008, Waters et al. 2010). Hypotheses to explain the complex faunal patterns of south east Australia which place emphasis on historical processes including oceanography (Fraser et al. 2009, Li et al. 2013) and the emergence of the Bassian Isthmus (Burridge 2000, York et al. 2008). The Bassian

Isthmus periodically connected Tasmania to mainland Australia and disrupted dispersal between eastern and western Australia (Dartnall 1974). Deep phylogenetic disjunctions on either side of the Isthmus (York et al. 2008, Ayre et al. 2009) implicate a role in the formation of incipient species. Contemporary factors suggested to shape faunal distributions include habitat limitations/breaks (e.g. Ninety Mile Beach (Ayre et al. 2009)), ocean currents/oceanography (Waters 2008, Miller et al. 2013) and temperature (O'Hara & Poore 2000).

1.2.2 Oceanography of south-eastern Australia

The main oceanic current along the east coast of Australia is the East Australian

Current (EAC). The EAC is the major western boundary current of the South Pacific

Gyre carrying warm tropical and subtropical waters from the coral sea, predominantly in a poleward direction along the east coast of Australia (reviewed by Suthers et al.

2011), and is highly variable with mesoscale eddies, meanderings and non-linear dynamics dominating it (O’Kane et al. 2011). The current is strongest during summer

(~ 90 cm s-1) reducing to half strength in winter (Ridgway & Dunn 2003). The majority of the EAC breaks from the east coast of Australia around 32 °S (Smokey

Cape), heads east across the Tasman sea giving rise to the Tasman front (Warren 1970,

Stanton 1981). The separation point can be highly variable with oscillations between

28 and 37 °S recorded over the last 30 years (Cetina-Heredia et al. 2014). Below the main separation point, residual eddies of the EAC continue southward to Merimbula,

New South Wales (Hutchins 1991). Over the past 60 years, the EAC has intensified in strength with a poleward shift influencing SST (sea surface temperature) as far south as Tasmania (~ 2 °C increase in SST during the summer months and 0.48 PSU over the last century; Ridgway & Dunn 2003, Ridgway 2007).

The main oceanic currents in the southern seaboard of Australia are the South

Australian Current and the Zeehan current (Gill et al. 2006). Both currents are extensions of the westward flowing Leeuwin current which is the major Eastern boundary current of Western Australia. The currents predominantly move in an eastward direction, but during summer when the Leeuwin current weakens in strength, coastal currents reverse in direction (flow westwards) due to strong coastal winds which blow from the south-east. Drift bottle experiments suggest surface currents may

11 reach speeds of 0.1 cm s-1 during summer (Olsen & Shepherd 2006). In Bass Strait, current reversal is a common feature between summer and winter (east to west, respectively) with the incursion of EAC eddies in winter as they are transported southwards (Sandery & Kämpf 2007). A unique feature of the southern Victoria and

South Australian coastline is the Bonney Upwelling. From December through to

March from Cape Jaffa, South Australia to Portland, Victoria nutrient rich cold water is brought to the surface and transported westwards, increasing regional primary productivity (Kämpf et al. 2004, McClatchie et al. 2006). Alongshore velocities during are in the order of 25 – 40 cm s-1 which can transport water 215 - 430 km over a 10- day period (Gill et al. 2006).

1.3 Thesis aims and structure

The overarching aim of this thesis is to examine how the genetic structure of two marine invertebrate species in south-eastern Australia is affected by known

(documented) drivers for population differentiation in the region. By examining the genetic structure of the subtidal ophiuroid, Ophionereis schayeri (Müller & Troschel,

1844) with a 7-day PLD and the intertidal gastropod, Cellana tramoserica (Holten,

1802) with a 3-day PLD, the role of PLD and environmental factors (inclusive of biogeographic features) in determining population structure can be evaluated.

If PLD is the most important influence on dispersal capacity, an overwhelming signature and similar signature (i.e. the PLD of both species can be considered the same) consistent with PLD of each species should be detected. Hence, populations of both O. schayeri and C. tramoserica should display strong genetic divergence at small spatial scales (<50 km). If other environmental factors have a stronger influence, then genetic patterns will correspond with these features in the study regions.

In chapter 2, the genetic structure of two populations of O. schayeri separated by ≥100 km, is examined using microsatellite markers developed in the chapter. If the genetic structure of O. schayeri arises from its PLD (7 d), then the two populations should be genetically distinct from each other.

In chapter 3, the genetic structure of six populations of C. tramoserica is investigated using microsatellites developed in the chapter, along the south east

Australian coast (from Sydney, NSW to Walkerville, Victoria) and in Tasmania. The short PLD of C. tramoserica (3 d) would be expected to restrict migration resulting in population divergence across all populations.

Chapter 2

Development of microsatellites and application to two populations of Schayer's brittle star (Ophionereis schayeri

(Müller & Troschel, 1844)) in south-eastern Australia

2.1 Abstract

The development and application of eleven microsatellite markers to two populations of Schayer's brittle star (Ophionereis schayeri) separated by ≥100km, revealed the absence of any genetic structure, despite the species having a relatively short PLD (7 d). Examination of FIS and Ne revealed that for the majority of the markers, the species could be considered panmictic. The mechanisms responsible for maintaining such an open population structure despite the species PLD, is discussed.

2.2 Introduction

Most sessile marine invertebrates rely on a mobile larval phase for dispersal

(Adams et al. 2012). Dispersal acts to ensure gene flow, thereby decreasing the likelihood of population extinction through environmental, genetic, demographic and/or catastrophic stochasticity (Kesäniemi et al. 2014b). Accordingly, the distance a larva travels, will determine the scale at which a species interacts with its environment

(Riginos & Liggins 2013), altering patterns of gene flow (Iacchei et al. 2103), genetic diversity (Bohrer et al. 2005), population structure (Selkoe et al. 2014) and range size

(Lester et al. 2007).

Residence in a dense, fluid environment coupled with the fact that larvae are small with a weak swimming ability means that for larvae, viscous forces dominate

(Riginos & Liggins 2013). Therefore, a fundamental constraint on a species ability to disperse is its pelagic larval duration (PLD). Many studies have shown that a species dispersal capacity and/or range can correlate with its PLD (Bohonak 1999, Riginos &

Victor 2001, Mc Veigh et al. 2017). Reisser et al. (2014) found that populations of the gastropod Nerita melanotragus with a planktonic phase of 2 - 3 months showed no differences in genetic structure at the scale of 750 km whilst its sibling species, N. atramentosa, with a PLD of 5 - 6 months, showed no differentiation over distances of

2000 km (Waters et al. 2005). For species that lack a dispersive stage or have a short- lived PLD, Costantini et al. 2007 found strong genetic differences between populations of the red coral Corralium rubrum which broods its offspring, at spatial scales of tens of metres, and Boulding and Kyle (2000) found genetic differences between populations of the direct-developing gastropod Littorina subrotundata separated by 15 km. Positive relationships have also been found between geographical range and

15 dispersal ability in fish (Bonhomme & Planes 2000, Zapata & Herrón 2002), gastropods (Hansen 1980, Jablonski & Lutz 1983), echinoderms (Emlet 1995) and cowries (Paulay & Meyer 2006). However, it should be noted that other studies have found no correlation between PLD and genetic structure (Boulding & Kyle 2000,

Marko 2004, Miller & Ayre 2008) and there are several studies which have found that species which lack a dispersal stage are more successful at colonising isolated habitat than species with planktonic larvae (Martel & Chia 1991, Cañete et al. 2007).

Although PLD can be a significant determinant of a species dispersal capacity

(Shanks et al. 2003, Siegel et al. 2003, Shanks 2009) other factors may be more important. One of the most prevailing forces in the marine environment are ocean currents (Pineda et al. 2007). The western boundary current of the South Pacific gyre forms the East Australian Current (EAC) (Roughan et al. 2011, Suthers et al. 2011) which flows southwards along the eastern seaboard of Australia. At around 32 °S, the current breaks from the coast to form the Tasman front, heading eastwards towards

Lord Howe Island and New Zealand (Godfrey et al. 1980). A residual southward component (EAC extension) continues to flow poleward to Tasmania carrying warm tropical waters (Cetina-Heredia et al. 2014). The strength of the EAC combined with production of eddies, generates highly variable patterns of current strength and direction (Mata et al. 2006), promoting the transport of propagules both equatorward and poleward (Coleman et al. 2011b). The EAC has a strong influence on the biology, ecology and connectivity of the marine communities of eastern Australia (Baird et al.

2006), which has become even more evident with its strengthening in response to climate change and a concomitant southward shift in the southern range of some species (Ridgway 2007, Ling et al. 2009, Figueira & Booth 2010). For example, Miller et al. (2013) found no evidence of genetic structuring in the mollusc, Donax deltoides

16 along the south-eastern coast of Australia and similarly Banks et al. (2007) found an absence of genetic structure between populations of Centrostephanus rodgersii along the eastern coast of Australia. Both authors postulated this was because of the strong influence of the EAC on the species populations.

Schayer’s brittle star Ophionereis schayeri is an endemic temperate ophiuroid species from Australia, inhabiting subtidal reefs up to depths of 30 m and are typically found aggregating under large boulders (Baker 1982). O. schayeri spawns for eight months of the year (January through to August) producing a lecithotrophic larva with settlement occurring after a short planktonic life of seven days (Shepherd & Edgar

2013). O. schayeri has an extensive distribution along the coasts of eastern, southern and western Australia; from southern Queensland to eastern Tasmania, along the southern and western coasts of Victoria, South Australia and Western Australia

(Selvakumaraswamy & Byrne 1995). Despite the abundance of O. schayeri, there is limited biological research on the species.

In this chapter, 11 microsatellite markers are developed and applied to two populations of O. schayeri. Given the short PLD (7 d) of the species it is envisaged that populations should show genetic differentiation, however, the mixing effect of the

EAC may overwhelm any biological considerations.

2.3 Materials and methods

2.3.1 Sampling acquisition

Samples of Ophionereis schayeri were collected from two sites (populations) in New South Wales; Little Bay (33°58’48.2”S, 151°15’8.0”E) and Bass Point

(34°35’36.8”S, 150°52’55.2”E) with a separation of ≥100km between locations (see

Figure 2.1). At each site, 30 to 31 specimens of various size classes were obtained by snorkelling. An arm was taken from each brittle star and placed into a 30 ml tube of

95% ethanol. All animals were returned to their site once all tissue had been collected.

2.3.2 DNA Extractions

Genomic DNA was extracted from 61 individuals using the CTAB DNA extraction method of Doyle & Doyle (1987) modified with the addition of a two-hour proteinase K incubation @ 55°C step followed by a standard chloroform/isoamyl alcohol extraction. DNA quality was verified on a 1.5 % Agarose gel and the purity and concentration of the DNA, determined using a NanoDrop 2000 Spectrophotometer

(Thermo Fisher Scientific, Waltham, Massachusetts, USA).

2.3.3 Microsatellite development

Using DNA extracted from the arm of a random selected individual, microsatellites were obtained by shotgun sequencing a portion of the genome on a

Roche 454 platform using GS FLX Titanium reagents on a sixth of a plate in line with the protocol of Gardner et al. (2011). A total of 182,995 reads were obtained with an average read sequence length of 569 base pairs (bp) after removal of reads with length

<350 bp. Suitable sequences were identified by scanning for repeats using QDD2 ver.

2.1. (Meglécz et al. 2010) and a list of 5931 possible microsatellites regions was identified. From this list, 40 microsatellite loci were selected for further testing with a preference for di-, tetra-, penta- motif classes (repeat between 8 and 20). All forty primers pairs where tested on 10 randomly selected individuals (see 2.3.4 for reaction conditions and protocol), resulting in the selection of 11 pairs (see Table 2.1) deemed suitable (i.e. they were repeatable, lacked null alleles and were polymorphic) to examine differentiation between the two populations.

2.3.4 Application of markers to populations

All individuals (n=61) were genotyped across the 11 selected microsatellites using forward primers with a universal tail at the 5’ end of the sequence (see Blacket et al. (2012), tails ‘A’, ‘B’ or ‘C’), allowing incorporation of a fluorescently labelled tail into the product. Markers were amplified using Platinum Multiplex master mix

(Invitrogen, Scoresby, Australia) at 10μl reaction volumes composed of 5μl of

Invitrogen Multiplex master mix, 1.2μl GC Enhancer, 2μl sterile H2O, 1μl genomic

DNA and a final reverse primer concentration of 100nm and 50nm of the forward and fluorescent universal primer. Amplification of the loci was performed using the 19 following program: an initial denaturation of 95°C for 120 secs, followed by 35 cycles of 30 secs at 95°C, 90 secs at 60°C, 30 secs at 72°C and a final elongation step at 60° for 30 mins. To maximise efficiency, reactions products were pooled based their size and/or the fluorescent probe (maximum of three markers per pool). Products were detected on an ABI 3730 Sequencer and scored using the Microsatellite plugin (version

1.4.6) in Geneious version 11.1.5 (http://www.geneious.com, Kearse et al. 2012).

2.3.5 Data Analysis

Genetic diversity

Measures of genetic diversity were calculated using GenAlEx 6.501

(http://biology-assets.anu.edu.au/GenAlEx/Welcome.html, Peakall & Smouse 2006,

2012). The presence of null alleles was assessed using Micro-Checker version 2.2.3

(http://www.nrp.ac.uk/nrp-strategic-alliances/elsa/software/microchecker/van

Oosterhout et al. 2004). Determination of whether populations and loci were in Hardy

Hardy-Weinberg equilibrium were made in Micro-Checker (van Oosterhout et al.

2004) and GenAlEx 6.501 (Peakall & Smouse 2006, 2012).

Population differentiation

Partitioning of genetic variation within and between populations was made using an Analysis of Molecular Variance (AMOVA) in GenAlEx version 6.501

(Peakall & Smouse 2006, 2012) for both FST and RST.

Figure 2.1: Google Earth image highlighting the location of the sampling sites at

Little Bay and Bass Point in New South Wales.

Table 2.1: Primer sequences and characteristics of eleven microsatellite loci for O. schayeri from Little Bay and Bass Point. Number of alleles (Na).

Locus Primer sequences (5' to 3') Repeat motif Na

osc 1b F: GGAAGGAACATAAGGAGGGC AAGGC 9

R: GACGGTTAATGATCATACCTGCTA

osc 20c F: TGCAACCAGCCTAGGATTAG AG 9

R: TGAACTTTGACCAGGTCGTG

osc 102b F: TATCTGCGTGTACCGCTGTC AC 8

R: CGAAGTTACATCGGTTTGGTG

osc 12c F: CCTACGTCAGGCCGCAAG ACCT 10

R: CACAAACCAGACAACAAACACA

osc 105b F: CACATTTCCTGCATACCGTT AC 9

R: GCAACCAAGGACGGTGTATT

osc 107a F: AATCTCATTAACCCATCGGC AT 6

R: GTCGACCTTGTGCAAATTCC

osc 113a F: GCTGGGATGGACTTATGTGG AG 14

R: ACCCAAACAAAGTTGCTCCA

osc 117b F: AAATATCTTGATCGCGGGAG AT 9

R: GCTCTGCTGAAATTGGTCAAA

osc 202a F: GCTCTGATGGCTCATTTCCT AG 8

R: GGACTCGCTATGAGCTGACC

osc 205a F: CGTTACGTGCGAAACTTGAA AAC 8

R: ATCGTGTACGGGAGACGAAC

osc 207c F: ACGACCGTTTAAGGTGATGC AC 10

R: CGACAAAGGACGAACCAGAT

2.4 Results

Genetic diversity

No null alleles were detected across any of the markers in either population by micro-Checker (van Oosterhout et al. 2004) and both populations were considered to be in Hardy-Weinberg equilibrium. Examination of individual loci revealed that one marker, 205A, was not in Hardy-Weinberg equilibrium in either population. All loci where polymorphic for all populations (see Table 2.2).

Population differentiation

Analysis of the data using AMOVA revealed no partitioning of genetic variation in the microsatellites for FST or RST across both populations (FST =0.004, p

>0.05; RST =0.006, p >0.05; see Tables 2.3 and 2.4), i.e. for either metric there is no genetic difference between the populations.

Loci N Na Ne HO HE UHE FIS

107a 30.5 6 3 0.89 0.66 0.67 -0.34

1b 30.5 9.5 2.3 0.61 0.56 0.57 -0.08

12c 30.5 12 6.2 0.90 0.84 0.85 -0.08

202a 30.5 7.5 3.7 0.89 0.73 0.74 -0.19

102b 30.5 7 3 0.70 0.67 0.68 -0.06

20c 30.5 9 3.7 0.82 0.73 0.74 -0.12

113a 30.5 12.5 3.4 0.93 0.70 0.71 -0.33

105b 30.5 8 3.9 1.00 0.74 0.75 -0.36

207c 30.5 9 4.2 0.87 0.76 0.78 -0.14

117b 30.5 8.5 3.1 0.52 0.67 0.68 0.22

205a 30.5 7 4.3 0.67 0.76 0.78 0.12

Source df SS MS Est. Var. %

Among Pops 1 4.876 4.876 0.015 0%

Within Pops 120 476.722 3.973 3.973 100%

Total 121 481.598

Source df SS MS Est. Var. %

Among Pops 1 2998.985 2998.985 12.484 1%

Within Pops 120 268520.178 2237.668 2237.668 99%

Total 121 271519.164

2.5 Discussion

For the two populations of O. schayeri no genetic difference between the populations could be detected. Nine of the eleven FIS (inbreed coefficient) values for each loci (see Table 2.2) were negative, -0.06 to -0.36, indicating that the populations are outbred and effectively panmictic, contrary to what would have been expected from their short pelagic larval development (PLD). Several authors have found a similar lack of differentiation for other invertebrate species along the New South

Wales coast. For example, Johnson and Black (2006a) found no genetic differentiation for the intertidal snails, Bembicium nanum and Bembicium auratum with a PLD of 10 days. Other invertebrate species includes the sea anemone Oulactis muscosa (Hunt &

Ayre 1989), the surf-clam, Donax deltoides (Miller et al. 2013), the kelp Ecklonia radiata (Coleman 2013) and the urchin Centrostephanus rodgersii (Banks et al. 2007).

Contrary to these studies, Teske et al. (2011) found significant structure between populations of the tunicate Pyura praeputialis with a PLD of 5 days.

It is probable that the lack of genetic differentiation between populations on

Australia’s eastern seaboard is driven by the mixing effects of the East Australian

Current (EAC) arising from its strength, equatorward counter-currents and the shedding of eddies (Mata et al. 2006). Coleman et al. (2011b) looking for evidence of isolation by distance, found that east coast populations of the kelp E. radiata had very low levels of genetic structure. The extent to which the larvae of coastal species are entrained in EAC, transported further south and then returned inshore is unknown, although there is direct observational evidence substantiating the process. For instance, the appearance of tropical 'vagrants' in southern Australia (Figueira & Booth 2010,

Booth et al. 2007), the range extension of C. rodgersii into Tasmania (Ling et al. 2009)

26 and the presence of the larvae of coastal and inshore species in the cores of offshore eddies (Suthers et al. 2011, Booth et al. 2007).

Other explanations for the lack of difference between the two populations could be due to the lack of habitat discontinuity (Johnson & Black 2006b) (i.e. all of the south eastern seaboard has shallow subtidal reefs), substantial reproductive output

(St-Onge et al. 2015) and recruitment (hop-scotching) between adjacent populations at a short spatial scale which can blur local genetic signatures (Crandall et al. 2012).

O. schayeri spawns for eight months of the year producing large amounts of larvae therefore reproductive output can be substantial. O. schayeri spawning is characterised by a period of intensive gamete release followed by a low-intensity gamete release for several months (Selvakumaraswamy & Byrne 1995). Furthermore, Falkner and Byrne

(2003) found that mature specimens of another ophiuroid species, Ophiactis resiliens, were present approximately two months longer at Clovelly Bay, NSW compared to an adjacent population at Little Bay, NSW, which was due to an extended gametogenesis.

The authors also found that juveniles were rare in the subtidal habitat of Clovelly Bay, whereas, small juveniles were observed in the subtidal habitat of Little Bay, therefore illustrating how recruitment can be variable within planktonic larvae species, even between similar habitats only eight km apart.

Although the two populations of O. schayeri are open, it is very difficult to translate this into the number of recruits between populations, i.e. there is a disconnect between what is perceived as demographic processes and what we infer from genetic measures (Waples & Gaggiotti 2006). This arises because <10 migrants per generation will forestall the accumulation of genetic differences (Slatkin 1987, Palumbi 2003) and because, the relationship between the number of immigrants to a population (Nm)

27 and the fixation index (FST) is reciprocal. Hence distinguishing moderate levels of gene flow from panmixia is often precluded because the resolution of the difference required to make such a distinction, falls within the error associated with the calculation of FST

(Waples 1998, Palumbi 2003).

If a population has not reached equilibrium between the homogenising effects of gene flow and the diversifying effects of mutation, the genetic structure observed may not reflect contemporary patterns of gene flow (Grosberg & Cunningham 2001).

Given the large effective population sizes of many marine invertebrates, some authors have argued that populations never achieve equilibrium and that current genetic studies may be unable to capture true patterns of contemporary gene flow (Whitlock &

McCauley 1999). This means there may be a strong mismatch between the genetic structure detected and its relevance to present day demographic processes (Palumbi

2003, Slatkin 1987) with even small levels of gene flow enough to prevent fixation of allele frequencies within a population. Hence an absence of genetic difference, can reflect high levels of larval dispersal or contrarily the fate of only a few (Slatkin 1987).

Thus, a finding of 'high connectivity' in this study, does not necessarily mean that connections between populations across O. schayeri 's range are well-maintained in

'ecological' time frames (Palumbi 2003). For instance, most larvae could be retained locally, but settlement of only a few in distant locations, might obscure any genetic differences (Palumbi 2003, Slatkin 1987).

A lack of genetic differentiation does not necessarily equate to an absence of genetic differences (Grosberg & Cunningham 2001). Instead markers may lack sufficient resolution and/or uninformative genetic regions may have been surveyed.

However, it should be noted that all markers where polymorphic for all populations

28 and ten of the markers for both populations where in Hardy-Weinberg equilibrium. If the loci originated from populations which have only recently diverged (incomplete lineage assortment; Qu et al. 2012, Yasuda et al. 2015) and/or there are differences in the mutation rate between loci (Dharmarajan et al. 2013) differences between populations may be masked. Although microsatellites do provide superior resolution compared to other marker systems (e.g. allozymes) they can still be conservative in their ability to detect weak differences in structure (Selkoe & Toonen 2006). Waples and Gaggiotti (2006) found for low levels of genetic differentiation (lower mutation rates, fewer loci and populations and smaller sample sizes), the performance of microsatellite markers declined compared to SNPs. Jeffries et al. (2016) have demonstrated that a RAD-seq approach can detect finer population structure compared to microsatellites, whilst SNPs should provide the finest resolution. Hamblin et al.

(2007) comparing the ability of microsatellite and SNP markers to measure relatedness in a population and determined that more individuals were classified as mixed ancestry using SNPs compared to microsatellites (48% versus 22%).

2.6 Conclusion

No genetic differentiation was observed between the two populations of O. schayeri examined suggesting that O. schayeri is panmictic. This is likely to be driven by the EAC and/or the extensive availability of habitat coupled with reproductive output rendering perceived populations as a part of one large open population.

Chapter 3

The genetic structure of the intertidal limpet (Cellana tramoserica (Holten, 1802)) in south-eastern Australia: microsatellite development and application

3.1 Abstract

The development and application of ten microsatellite markers to six populations of the limpet (Cellana tramoserica) in south eastern Australia, revealed that two populations, Ulladulla and Walkerville, separated by 700 km were genetically the same as each other, yet encompassed a further two populations, Bermagui and

Mallacoota, which in turn were the same as each other but different to the first two populations. A population from Sydney and a population from Tasmania were different to each other and all other populations. Although the species has a short pelagic larval duration (~ 3 days) and hence genetic structure would be expected, the results reveal an unusual genetic structure, i.e. how can populations encompassing two populations be the same as each other, yet two different populations be different to those they are encompassed by. The mechanisms responsible for this unusual structure is discussed.

3.2 Introduction

A fundamental goal of evolutionary ecology is to determine the forces which shape the genetic structure of species and populations. In the marine environment, many studies emphasise the importance of abiotic factors (e.g. oceanography, clines) in shaping population structure (Riginos & Liggins 2013) whilst others have highlighted the role of biotic factors (e.g. pelagic larval duration (PLD) and settlement cues (Selkoe et al. 2010). The signature of historical events (e.g. the formation of barriers to gene flow) which no longer exist can overwhelm the signature of modern- day (contemporary) patterns (Benzie 1999), such that without a historical perspective the observed patterns in structure can make little sense. For example, York et al. (2008) found that populations of the intertidal barnacle, Catomerus polymerus, could be divided into four regions based on microsatellite (contemporary signature) data which could be explained by contemporary oceanography. Examination of the same populations using mtDNA sequences (historical signature) identified two highly divergent east–west clades which corresponded with the historical Bassian Isthmus.

Hence both historical and contemporary processes were important in shaping its biogeography (Waters et al. 2005, Waters 2008). Furthermore, Ayre et al. (2009) using mtDNA sequences, found weak genetic differentiation between populations of C. tramoserica, due to the restriction of the biogeographic barrier of Ninety Mile Beach.

By examining a species genetic structure using different methodologies (i.e. historical versus contemporary markers), insight can be gained into how both have shaped a species biogeography.

One long-standing theory invoked to explain differences in genetic structure between species of marine invertebrates is the role of pelagic larval duration (PLD)

(Hedgecock 1986, Waples 1987). For most sessile and sedentary species, dispersal and hence gene flow takes place through the production and broadcast of free-living larvae

(Mileikovsky 1971). By virtue of their small size and limited swimming ability, larvae are passive particles, and the time spent by a larva in the water-column (PLD) reflects its dispersal capacity (Gooch 1975, Crisp 1978, Palumbi 1994, Benzie 1999). Thus, species with short PLD’s will display population genetic structure (Hedgecock 1986,

Hellberg 1996), whereas species with a long PLD will exist as large and genetically homogenous populations (Riginos & Victor 2001, Sherman et al. 2007, Bell 2012).

Despite the intuitive nature of this relationship, many studies challenge this hypothesis and suggest the opposite to be true (Todd et al. 1998, Boulding & Kyle 2000, Taylor

& Hellberg 2003) and meta-analyses/reviews have found mixed support. For example,

Waples (1987), Doherty et al. (1995), Bohonak (1999) and Riginos and Victor (2001) have concluded that there is a relationship between PLD and dispersal whilst Weersing and Toonen (2009) and Selkoe and Toonen (2011) find no relationship. Furthermore, the broad geographical distribution of direct-developing species (no dispersive larva) and the dominance of species with this life-history on isolated islands (e.g. ‘the

Paradox of Rockall’, Johannesson 1988), indicates that dispersal capacity can be mediated by additional forces (e.g. vicariance and rafting, Gibson et al. 2006).

The interaction of an organism with its environment shapes spatial patterns of genetic variation, with landscape features acting to restrict or enhance gene flow

(Riginos & Liggins 2013). Restrictions in gene flow will usually result in population divergence through the effects of drift and selection, with enhanced gene flow negating population divergence/speciation (Slatkin 1985). Because of the viscosity of seawater and low Reynolds numbers (i.e. viscous forces dominate, Vogel 1994) larvae will have little control to oppose the prevailing direction of ocean currents, and oceanography

32 will be an important factor shaping the genetic patterns of populations (Pineda et al.

2007). Correspondingly, Galindo et al. (2010), Gilg & Hilbish (2003) Selkoe et al.

(2006) and White et al. (2010) have found even weak or random variation genetic variation can be explained by ocean currents and seasonal changes in the direction of current flow. Oceanography may not only enhance dispersal capacity (Mitarai et al.

2009, Coleman et al. 2011b) but can also restrict it, for example, Holmes et al. (2003) found considerable genetic differentiation between populations of the bivalve Arctica islandica, with a 30-day PLD, located across the opening of a fjord with only 20 km of separation. A limited exchange of water in and out of the fjord was invoked to explain this pattern.

The common limpet, Cellana tramoserica, is an endemic marine gastropod, widely distributed along the south-eastern coast of Australia to Tasmania, Victoria and

South Australia, inhabiting intertidal rocky platforms (Underwood 1974). C. tramoserica is an important keystone species in the intertidal community, enhancing biodiversity as it grazes on macroalgae sporelings and microalgae on the substratum

(Branch & Branch 1980; Coleman et al. 2011a). Where there is a high density of C. tramoserica, the grazing controls the distribution of macroalgae being established therefore allowing other organisms to graze on microflora on the substratum

(Underwood & Jernakoff 1981). C. tramoserica spawns for seven months of the year, occurring between March and October (Underwood 1974). Larval development occurs rapidly from pelagic lecithotrophic trochophore to planktotrophic veliger stages, culminating with settlement after three days (Anderson 1962).

In this chapter, the population genetic structure of C. tramoserica across parts of south-eastern Australia is investigated using microsatellite markers, to examine the contemporary processes which have shaped the biogeography of C. tramoserica.

Microsatellites mutate at a much faster rate making them useful for exploring influences of contemporary processes. Given that C. tramoserica has a short PLD, it is predicted that:

i) population differences will be detected at fine-spatial scales;

ii) the level of population divergence will correspond with the degree spatial scale of separation, i.e. isolation by distance (IBD) will be evident;

iii) patterns of population genetic structure will correspond to prevailing oceanographic or ecological barriers (e.g. Ninety Mile Beach and Wilson’s

Promontory) which will be evident in the microsatellites.

3.3 Materials and methods

3.3.1 Sampling acquisition

Samples of Cellana tramoserica were collected from six sites across the species range in south-eastern Australia. These sites included three populations in New

South Wales; Little Bay (33°58’48.2”S, 151°15’8.0”E), Ulladulla (35°21’32.8”S,

150°28’43.0”E) and Bermagui (36°25’25.7”S, 150°04’26.0”E) with a separation of

<200km between each site, two populations in Victoria; Mallacoota (37°34’16.1”S,

149°45’53.3”E) and Walkerville (38°51’21.9”S, 145°59’50.7”E) with a separation of

<500km between each site and one population in Tasmania; Lauderdale

(42°54’58.7”S, 147°30’27.8”E) (see Figure 3.1). At each site ≥31 limpets were obtained from exposed rock platforms during low tide, with various size classes randomly sampled, to minimise potential cohort bias. Individuals were placed into a

50 ml sampling jar containing 95% ethanol and stored at -20°C prior to DNA extraction.

3.3.2 DNA Extractions

Genomic DNA was extracted from the foot tissue of 191 individuals using the

CTAB DNA extraction method of Doyle & Doyle (1987) modified with the addition of a two-hour proteinase K incubation @ 55°C step followed by a standard chloroform/isoamyl alcohol extraction. DNA quality was verified on a 1.5 % Agarose gel and the purity and concentration of the DNA, determined using a NanoDrop 2000

Spectrophotometer (Thermo Fisher Scientific, Waltham, Massachusetts, USA).

3.3.3 Microsatellite development

Microsatellites were obtained by shotgun sequencing a portion of the genome of a randomly selected individual on a Roche 454 platform using GS FLX Titanium reagents on a sixth of a plate in line with the protocol of Gardner et al. (2011). A total of 163,769 reads were obtained with an average read sequence length of 538 base pairs

(bp) after removal of reads with length <350 bp. Suitable sequences were identified by scanning for repeats using QDD2 ver. 2.1. (Meglécz et al. 2010) and a list of 2858 possible microsatellites regions was identified. From this list, 45 microsatellite loci were selected for further testing with a preference for di-, tetra-, penta- motif classes

(repeat between 8 and 20). All 45 primers pairs where tested on 10 randomly selected individuals (see 3.3.4 for reaction conditions and protocol), resulting in the selection of 10 pairs (see Table 3.1) deemed suitable (i.e. they were repeatable, lacked null alleles and were polymorphic) to examine differentiation between the six populations.

3.3.4 Application of markers to populations

All individuals (n=191) were genotyped across the 10 selected microsatellites using forward primers with a universal tail at the 5’ end of the sequence (see Blacket et al. (2012), tails ‘A’, ‘B’ or ‘C’), allowing incorporation of a fluorescently labelled tail into the product. Markers were amplified using Platinum Multiplex master mix

(Invitrogen, Scoresby, Australia) at 10μl reaction volumes. Each 10μl reaction consisted of 5μl of Invitrogen Multiplex master mix, 1.2μl GC Enhancer, 2μl sterile

H2O, 1μl genomic DNA and a final reverse primer concentration of 100nm and 50nm of the forward and fluorescent universal primer. Amplification of the loci was performed using the following program on a Bio-Rad C1000 thermocycler: an initial denaturation of 95°C for 120 secs, followed by 35 cycles of 30 secs at 95°C, 90 secs

36 at 60°C, 30 secs at 72°C and a final elongation step at 60° for 30 mins. To maximise efficiency reactions products were pooled, based their size and/or the fluorescent probe with a maximum of three markers per pool. Products were detected on an ABI 3730

Sequencer and scored using the Microsatellite plugin (version 1.4.6) in Geneious version 11.1.5 (http://www.geneious.com, Kearse et al. 2012).

3.3.5 Data Analysis

Genetic diversity

Measures of genetic diversity were calculated using GenAlEx 6.501

(http://biology-assets.anu.edu.au/GenAlEx/Welcome.html, Peakall & Smouse 2006,

2012). The presence of null alleles was assessed using Micro-Checker version 2.2.3

(http://www.nrp.ac.uk/nrp-strategic-alliances/elsa/software/microchecker/van

Oosterhout et al. 2004). Determination of whether populations and loci were in Hardy

Hardy-Weinberg equilibrium were made in Micro-Checker (van Oosterhout et al.

2004) and GenAlEx 6.501 (Peakall & Smouse 2006, 2012).

Population differentiation

Partitioning of genetic variation within and between populations was made using an Analysis of Molecular Variance (AMOVA) in GenAlEx version 6.501

(Peakall & Smouse 2006, 2012) for both FST and RST. Genetic differentiation between individual populations was estimated by calculating pairwise F-statistics in GenAlEx version 6.501 (Peakall & Smouse 2006, 2012). Significance values were based on

9999 permutations of the data with a Bonferroni correction applied.

Population structure

STRUCTURE (http://pritchardlab.stanford.edu/structure.html,

Pritchard et al. 2000) was used to identify groupings according to the microsatellite data. The program uses a Bayesian approach to identify groupings without consideration of origin. An admixture model with a burn-in of 50,000 steps followed by 100,000 MCMC replicates, K was set from 1 to 6, with 10 iterations for each value of K. Optimal K values were assessed using Structure Harvester

(http://taylor0.biology.ucla.edu/structureHarvester/, Earl & vonHoldt 2012) which implements the method of Evanno method (Evanno et al. 2005) and verified using Best

K in CLUMPAK (http://clumpak.tau.ac.il/, Kopelman et al. 2015). CLUMPAK (main pipeline) was also run for all runs of K to determine if any alterative clusters could be identified other than those determined by the Evanno method (Evanno et al. 2005).

Consensus plots for the data were made in CLUMPAK (http://clumpak.tau.ac.il/,

Kopelman et al. 2015) using Distruct for many K’s.

Table 3.1: Primer sequences and characteristics of ten microsatellite loci for C. tramoserica. Number of alleles (Na).

Locus Primer sequences (5' to 3') Repeat motif Na

ctr 121c F: GCCTAATCGTTAGATTGACGG AG 13

R: ACAGCGACTTCGGCACTACT

ctr 122a F: CATCATGTGGATTTCCCTGC AATG 15

R: ATAGCCTGTGATCGCTGTCC

ctr 204a F: ATAGCATGCCAATGCTCACG TGGGA 23

R: AGTCATCACGGCAGCTGAAA

ctr 102b F: CAATCGAAGCCAAGCGAAG CCAAG 16

R: GGGTTTGCGTTGAAATGTTG

ctr 213b F: TGAAGACGAAGATGGTGATGA ACT 21

R: TCTGTCGGTTGGTCTGTGTT

ctr 205b F: GACGAACGAATGAACGACG GAAT 29

R: GTGTCGGTTGCAAGTTCTCA

ctr 103c F: CATCATGTGGATTTCCCTGC AATG 24

R: GTGATCGCTGTCCAAGCAAA

ctr 217c F: ATAAGCAACACGGCCTCATC AAT 12

R: TATGCGTTTGCTATTCACCG

ctr 220a F: CAGTTCACGGGAATAGGAGC AAGT 25

R: ATGGCAGGCAATCACTATGC

ctr 306b F: TTGCTTCAGCGAATGTGTTC ACT 21

R: CGATTCCTTCAATATCATCAGC

3.4 Results

Genetic diversity

A total of 191 C. tramoserica specimens were genotyped with the ten microsatellite markers across the six populations, with 81 alleles being detected (see

Table 3.2). The number of alleles observed at any locus ranged from 12 to 29 and across all loci at any population from 11.2 to 16.4. All loci where polymorphic for all populations. Null alleles with a frequency >5 but <20 % were detected across 4 of the markers (ctr213b, ctr121c, ctr217c, ctr306b) driven by homozygote excesses. This is similar to levels found in many other studies for other organisms (reviewed in Dakin

& Avise, 2004) Accordingly, these markers deviated from Hardy-Weinberg equilibrium in four of the six populations, with Lauderdale, Mallacoota, Walkerville and Ulladulla not considered overall to be in Hardy-Weinberg equilibrium.

Population differentiation

Analysis of the data using AMOVA, revealed that some genetic variation was partitioned between the populations for both FST (11% of the total variance) and RST

(12% of the total variance) (RST =0.118, p <0.05; FST =0.112, p <0.05; see Tables 3.3 and 3.4). Pairwise comparisons between the populations revealed for FST that all populations could be considered different to each other, whereas for RST, Little Bay,

Ulladulla and Walkerville can be considered to be the same population (see Table 3.5).

Population structure

Analysis of the results produced by STRUCURE for K=1 to K=6, revealed that

K = 3 with the Evanno method (Evanno et al. 2005) using both Structure Harvester and Best K in CLUMPAK. In essence, Bermagui and Mallacoota can be considered as one population group, Little Bay and Lauderdale as a second population group and

Walkerville and Mallacoota as the third population group (see Figure 3.2). However, examination of CLUMPAK Distruct output for many K’s, reveals a further potential structure, where K = 4. Under this structure, Bermagui and Mallacoota can be considered as one population group, Walkerville and Mallacoota as a second population group and Lauderdale and Little Bay as independent populations (see

Figure 3.2). Under Distruct, mean (LnProb) = -9149.160 and mean (similarity score)

= 0.997 for K = 3 and mean (LnProb) = -8958.067, mean (similarity score) = 0.992 for

K = 4.

Bermagui Mallacoota Little Bay Lauderdale Ulladulla Walkerville

Table 3.2: Microsatellite variation across populations including the number of individuals genotyped (N), the number of alleles (Na), the effective number of alleles

(Ne), the observed heterozygosity (HO), the expected heterozygosity (HE), the unbiased expected heterozygosity (UHE) and the inbreeding coefficient (FIS).

Site N Na Ne HO HE UHE FIS

Little Bay 32 13 7 0.73 0.82 0.84 0.12

Ulladulla 32 14.2 7 0.52 0.81 0.82 0.36

Bermagui 32 16.4 8.2 0.72 0.83 0.85 0.14

Mallacoota 32 13.1 6.9 0.54 0.73 0.74 0.26

Walkerville 31 13.3 7.1 0.54 0.79 0.80 0.31

Lauderdale 32 11.2 4.2 0.47 0.71 0.72 0.34

Source df SS MS Est. Var. %

Among Pops 5 179.862 35.972 0.503 11%

Within Pops 376 1493.554 3.972 3.972 89%

Total 381 1673.416

Source df SS MS Est. Var. %

Among Pops 5 237638.555 47527.711 668.361 12%

Within Pops 376 1871195.958 4976.585 4976.585 88%

Total 381 2108834.513

Bermagui Mallacoota Little Bay Lauderdale Ulladulla Walkerville

Bermagui 0.059 0.081 0.157 0.087 0.084

Mallacoota 0.120 0.143 0.190 0.150 0.149

Little Bay 0.041 0.168 0.081 0.074 0.072

Lauderdale 0.071 0.121 0.105 0.155 0.157

Ulladulla 0.101 0.266 0.020 0.186 0.027

Walkerville 0.107 0.169 0.036 0.163 0.026

3.5 Discussion

Analysis of the data revealed different patterns according to the method.

Examination using AMOVA based of FST followed by pairwise comparison, determined that all populations could be considered distinct to each other. Analysis by

AMOVA using RST determined that the Little Bay and Bermagui populations and the

Ulladulla and Walkerville populations were not different to each other, with the Little

Bay population also showing no differentiation to the Ulladulla and Walkerville populations. Examination of the data using STRUCTURE followed by Distruct in

CLUMPAK revealed two possible solutions: i) K = 3 (in line with the output from

Structure harvester and best K in CLUMPAK), where Bermagui and Mallacoota can be as one population, Little Bay and Lauderdale as a second population and

Walkerville and Mallacoota as the third population group (see Figure 3.2); and ii) K =

4 where Bermagui and Mallacoota can be considered as a population, Walkerville and

Mallacoota as a second population and Lauderdale and Little Bay as independent populations. At first glance it would appear that all three analyses are contradicting each other. However, examination of the pairwise FST’s (see Table 3.5) along with the

Distruct plot for K = 4 (see Figure 3.2) reveals a similar pattern. The lowest pairwise

FST recorded is between Ulladulla and Walkerville (FST = 0.027) followed by that between Bermagui and Mallacoota (FST = 0.059). Although these FST’s are considered different from the AMOVA, in reality low FST’s have little biological meaning. Hartl and Clark (1997) (see also Wright 1978) considered FST values of <0.05 to show little genetic differentiation, 0.05 to 0.15 moderate genetic differentiation and >0.15 considerable genetic differentiation. Using these criteria for FST we get an identical picture to that for K = 4 in Distruct. Obtaining different pictures for FST versus RST is not as unusual as it first appears. Balloux and Lougon-Moulin (2002) have shown that

FST will seriously underestimate differentiation in highly structured populations, whilst RST fails when the single mutation model is not adhered to.

An excess of null alleles was revealed in several markers (ctr213b, ctr121c, ctr217c, ctr306b) causing some of the populations to deviate from Hardy-Weinberg equilibrium. Null alleles can lead to an overestimation of FST and genetic distance but a simulation study by Chapuis & Estoup (2007) suggests that in the presence of null alleles at < 20 %, the bias on estimations of population structure is minimal. Carlsson

(2008) also found null alleles had little impact on Bayesian assignment methods (e.g.

Structure), with only a slight reduction in power. Many authors agree (Dakin & Avise,

2004, Carlsson, 2008, Chapuis & Estoup, 2007, Dharmarajan, et al. 2013), that null alleles have serious impacts on parentage studies but should have minor impact on population genetic studies.

The observed genetic structure is unusual. A normal expectation is that the populations should exhibit some level of IBD (isolation by distance) whereas we have two populations groups separated by two populations which have formed their own population group, i.e. Ulladulla and Walkerville form one population group and

Bermagui and Mallacoota a second population group, irrespective of the method of analysis. However, Bermagui and Mallacoota fall between Ulladulla and Walkerville.

Correspondingly Little Bay (Sydney) and Lauderdale (Tasmania) are more similar to each other than Lauderdale is to any other population. The genetic structure of marine invertebrates is frequently described as ‘chaotic’ when the genetic structure is unable to be explained and barriers to gene flow and dispersal are unable to be identified

(Kesäniemi et al. 2014a). Factors such as temporal and spatial variability in cohort success, sweepstakes and selective forces (Toonen & Grosberg 2011, Teske et al.

2013) irrespective of PLD, can often results in chaotic genetic patchiness. 47

Furthermore, oceanographic factors can often override any biological considerations such as entrainment in embayments (Nicastro et al. 2008), headlands (Roughan et al.

2005) or the availability of habitat (Johnson & Black 2006b) resulting in genetic structure.

Chaotic genetic patchiness is a widespread phenomenon that occurs in many marine invertebrates, including limpets (Johnson & Black 1982, 1991), sea urchins

(Watts et al. 1990), the southern rock lobster Jasus edwardsii (Villacorta-Rath et al.

2017), the western rock lobster Panulirus cygnus (Thompson et al. 1996) and the spiny lobster Panulirus interruptus (Iacchei et al. 2013). Kesäniemi et al. (2014a) found chaotic patterns of genetic structure for the polychaete Pygospio elegans wherein there was no isolation by distance (IBD) and no barriers to geneflow or dispersal. The authors surmised that this chaotic genetic patchiness could have been a natural consequence of overlapping generations, as the individuals in their samples were at different reproductive stages thus various cohorts could have combined within the same category. Similarly, Johnson and Black (1982) using enzyme electrophoresis, discovered inconsistencies among populations of a species of Siphonaria limpet from south-western Australia, which revealed an unconformity to follow a consistent pattern, instead forming a fluctuating genetic patchiness. The authors deduced that this chaotic patchiness may have resulted from temporal and spatial variation among recruits, which lead to the conclusion that planktonic dispersal can yield fine-scale genetic patchiness. Even though Johnson and Black (1982) believe that this temporal variation resulted from temporally and spatially varying selection on planktonic larvae, an alternate explanation is sampling variance arising from sweepstakes in reproductive success (Hedgecock 1994). Sweepstakes reproductive success (SRS) postulates that there is an exceptionally large variance in the reproductive success of

48 individuals, owing to sweepstakes-like chances of pairing reproductive activity with oceanographic environments favourable to gamete maturation, fertilization, larval development, settlement and recruitment to the adult spawning population. This can explain chaotic genetic heterogeneity on small spatial scales within marine populations (Hedgecock & Pudovkin 2011). The SRS hypothesis can explain the reason for the unusual population groups formed in this chapter and the variance in genetic differentiation between specific populations. The production, dispersal and settlement of the C. tramoserica larvae along the fluctuating oceanic environment of the south-east Australian coast could have encountered variations in reproductive success (large sets of larvae may have perished whilst an adult subset survives contributing to each larval cohort), reducing the effective size of the populations, therefore increasing genetic drift and subsequently successive populations may be genetically differentiated.

In contrast, Ayre et al. (2009) found weak genetic differentiation from mitochondrial DNA (mtDNA) sequences between populations of C. tramoserica which was restricted by the biogeographic barrier of Ninety Mile Beach. In addition to an absence of habitat, the authors also explained that the EAC and Zeehan Current which merged at this barrier, could have affected planktonic larvae dispersal and survival. Mitochondrial DNA is a conservative marker, often used for phylogeny and as a historical marker rather than for population genetic studies, and the difference recorded between the results of Ayre et al. (2009) and those here, may arise from differences in the sensitivities of the markers. Many authors have found that microsatellites provide a much better resolution for fine scale structure but perform poorly for large scale divergence which is better detected by more conservative methods (see Shaw et al. 1999, Borrell et al. 2012, Yang et al. 2016 for examples).

Although results between both markers should be congruent, there can be differences arising from the sensitivities of the markers and/or historical versus contemporary events (see Yang et al. 2016 for an example). Alternately, if as postulated the chaotic genetic patchiness observed in the limpet populations arises from SRS, then there will be both spatial and temporal variation in genetic structure as existing populations are succeeded by new cohorts. It is worth noting, that Hidas et al. (2007) found little support for correlation between the dispersal potential of a species (i.e. mode of larval development) and its geographic distribution across Ninety Mile Beach, the postulated biogeographic barrier for C. tramoserica in the study of Ayre et al. (2009).

Furthermore, populations of the sympatric urchin C. rodgersii, for the same stretch of the Australian coastline, exhibited weak genetic structure (Banks et al.

2007), suggested to arise from frequent eddy formation in the vicinity of an EAC separation point (~ 32 °S), i.e. environmentally driven population differentiation.

Studies for other species in the same region have not detected any influence of the

EAC on genetic structure (Piggott et al. 2008, Sherman et al. 2008, Miller et al. 2013).

This may simply reflect biological differences between the species e.g. C. rodgersii has an extensive PLD (4 - 6 months) which may allow time for larvae to become entrained in eddies, survive and be returned in-shore (Gaylord & Gaines 2000, Shanks

2009). It is also possible that the patterns identified by Banks et al. (2007) are temporally unstable with another study by the same authors indicating that discrete recruitment pulses of C. rodgersii may be distinguished (Banks et al. 2010). Over the long-term, temporal variation is likely to be ‘evened out’ with success of new recruitment pulses and may not drive any lasting genetic signature (Selkoe et al. 2006).

For example, Selkoe et al. (2006) was able to identify temporal differences in the genetic structure of cohorts of new recruits of the kelp bass Paralabrax clathratus, but this signature was not maintained in the established assemblage.

3.6 Conclusion

Genetic differentiation was observed between populations of C. tramoserica along the coast of south-eastern Australia. The observed genetic structure did not conform to expectation, e.g. isolation by distance, and can only be explained in terms of chaotic genetic patchiness probably arising from variations in cohort supply driven by the EAC.

Chapter 4

General Discussion

The overarching aim of this thesis was to increase our understanding of some of the factors that can drive dispersal and population divergence in the marine environment. To this end, two different sets of microsatellite markers were developed and applied to two marine invertebrate species, Ophionereis schayeri and Cellana tramoserica, which are endemic to the south east coastline of Australia.

4.1 Summary of findings

In chapter 2, the genetic structure of populations of the brittle star O. schayeri was examined to infer their connectivity. Examination of its genetic structure using 11 microsatellites markers determined that the species was panmictic.

In chapter 3, the genetic structure of populations of the limpet, C. tramoserica, was examined across half of its continental distribution (New South Wales to Victoria and Tasmania). In contrast to what would be predicted based on their short PLD, populations were found to be connected at a reasonably large spatial scale (≥700 km).

However, the genetic structure observed was unusual and did not conform to known, drivers of structure in the region, e.g. IBD, homogenising effect of EAC, vicariance across the Bassian Isthmus. Instead the observed patterns where chaotic, i.e. in line with chaotic genetic patchiness, and appear to be a product of sweepstake reproductive success (SRS).

4.2 Synthesis

4.2.1 The effects of oceanography on dispersal patterns

Currents can facilitate dispersal across and between ocean basins (Cumming et al. 2014), facilitating broad geographical distributions, e.g. ‘cosmopolitan’ species,

(Zane et al. 2011), and driving regional patterns in diversity (Kerswell 2006).

However, they can also restrict dispersal (White et al. 2010). At the largest scale, circumpolar oceanic frontal systems are the transition point of water bodies which can correspond with biogeographic transitions (Compton et al. 2013) and phylogenetic and/or population genetic disjunctions (Galarza et al. 2009).

The influence of currents, i.e. the EAC, is evident by an absence of population structure amongst O. schayeri populations (chapter 2) along the New South Wales coast, which is consistent with other marine invertebrate species studies that have found no genetic difference between populations along the New South Wales coast

(Ayre et al. 2009, Coleman et al. 2011a/2011b, Coleman et al. 2013). Given the multiple lines of evidence apparent in this thesis, it is clear that ocean currents have a large influence over patterns of larval dispersal.

4.2.2 The effects of PLD on dispersal capacity

Dispersal capacity is often invoked to explain why the distribution of species differ, with the prediction that species capable of long-distance dispersal should be able to disperse and maintain a distribution over a larger area. Contrary, in the case of

C. tramoserica, a short PLD appears to assist in the maintenance of a wide range. This is similar to the findings of other studies which indicate recent and ongoing transoceanic gene flow between Australia and New Zealand (Veale & Lavery 2011,

Morgan et al. 2013) although many of these species have PLD’s more extensive than that of C. tramoserica. In addition, other studies on species with a short PLD, identified gene flow across the Tasman Sea, with rafting playing a role (e.g.

Sypharochiton pelliserpentis with a PLD of <4 days, Veale & Lavery 2011).

The comparison between O. schayeri and C. tramoserica demonstrate that patterns of recruitment at a finer scale can be very different even in cases where PLD and spawning season are similar. Although life-history can influence dispersal capacity, potential and realised capacity are determined by different factors, with ecological and historical factors capable of overriding a species dispersal potential.

From a conservation perspective, a suite of ecological and biological factors should be considered when predicting dispersal capacity, although PLD may be of indicative value for evaluating the species maximum dispersal potential.

4.2.3 The factors influencing population divergence

In chapter 2, O. schayeri showed no evidence of genetic structure which was contrary to its PLD. This is most likely because the species hop-scotches and hence in the absence of habitat breaks and because of homogenizing factors (currents), gene flow is maintained by virtue of the relative abundance and “nearness” of adjacent populations. Although the panmictic paradigm intuitively makes sense, the literature provides no clear consensus as to reliability. For instance, several meta-analyses/large studies find support for it (see Riginos & Victor 2001, Selkoe & Toonen 2011) but others find none (see Kelly & Palumbi 2010, Riginos et al. 2011).

In chapter 3, C. tramoserica showed an evidence of connectivity between populations along the south-eastern Australian coast which did not conform to its short

PLD. Furthermore, unusual genetic structure was detected between populations, which did not follow recognized drivers of population divergence. This is most likely because the species have encountered variations in reproductive success (SRS, Hedgecock &

Pudovkin 2011) along the south east Australian coastline, which reduces the effective size of populations, increasing genetic drift causing successive populations to be genetically differentiated.

The advent of next generation sequencing and specifically the ease of discover of banks of single nucleotide polymorphisms (SNPs) has substantially increased the resolution at which we can discriminate between populations. One of the potential problems with existing marker systems, is that a lack of a difference doesn’t always mean there isn’t a difference, just that we lack the resolution to detect it. N.B., we attempt to avoid this by ensuring we have sufficient and polymorphic markers. Hence, with the application of SNP’s some population structure may become observable for

O. schayeri and further population structure evident for C. tramoserica. Although this is unlikely to change the broad conclusion made, it will provide more information to better determine what is shaping population structure.

4.2.4 Future research

As only two populations of O. schayeri were sampled in New South Wales, further studies will need to be conducted to ascertain if the lack of genetic differentiation shown between populations in chapter 2, continues to persist along the south east Australian coast, or as shown with C. tramoserica populations (chapter 3), become more genetically differentiated due to the influence of other mitigating factors.

The ability of C. tramoserica to navigate through an absence of habitat (Ninety

Mile Beach) and maintain population connectivity is testament to its dispersal capacity. Given the diverse distribution of C. tramoserica, it would be very interesting to examine population connectivity at a much broader scale (>700 km). However, as unusual patterns of genetic structure appeared between C. tramoserica populations along the south east Australian coast, a finer scale study of this region is required to understand the factors influencing this outcome, i.e. is it just chaotic genetic patchiness or can we discern an underlying driver for genetic structure.

Understanding the biological processes which regulate gene flow between populations within a species is essential when developing conservation strategies. The complexity of ocean currents and the variances in life history traits within and among marine invertebrate species can become problematic when examining genetic connectivity (Weersing & Toonen 2009). Microsatellite markers are an essential tool for assessing connectivity, population sizes migration rates and kinship (Johnson &

Woollacott 2012, Lance et al. 2013). The two different sets of microsatellite markers developed in this thesis can be applied to future studies to acquire an understanding of the factors that influence connectivity within marine invertebrate populations.

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