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Alternative breeding times and evolutionary
pathways in corals in north Western Australia
Natalie L. Rosser BSc. (Hons)
This thesis is presented for the degree of Doctor of Philosophy
of the University of Western Australia
School of Animal Biology
2016
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Abstract
This thesis explores the influence of asynchronous reproductive timing on patterns of
genetic structure in coral populations, at both ecological and evolutionary scales, in the
context of understanding the evolution of seasonal breeding patterns in Western
Australia. Most broadcast-spawning corals reproduce once a year, often in highly
synchronized events known as “mass spawning”. On some Western Australian reefs
there are two mass spawning events each year, a primary event in autumn and a
secondary event in spring, but at the outset of this project it was uncertain how
widespread the two events were. Reproductive surveys from 12°S to 23°S showed that
at all latitudes, a high proportion of species spawned in autumn (82-98%); however,
there was a correlation with latitude in the spring spawning event (r2 = 0.72), with a
decrease in the proportion of species spawning from 49% at Ashmore Reef (12°S) to
7% at Ningaloo Reef (23°S).
My previous research had shown that in some biannually-spawning species of
Acropora, conspecific colonies spawned in autumn or spring but not both, but it was
unknown whether the autumn- and spring-spawning cohorts were reproductively
isolated or whether colonies switched spawning time at random. Here, population
genetic analysis of microsatellites in sympatric (Acropora samoensis) and allopatric (A.
tenuis) autumn- and spring-spawning colonies showed that the reproductive cohorts
were genetically differentiated (FST = 0.17 in both species), confirming strong isolation.
Furthermore, in both species the seasonal cohorts had highly divergent lineages of the
nuclear intron PaxC that were not present in the other DNA sequence markers. The
unexpected finding that PaxC showed a different pattern to the other phylogenetic
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markers was tested via comparative phylogenetic analysis of 20 Western Australian
Acropora species using the mitochondrial Control Region, PaxC and 10,034 genome-
wide SNPs. This analysis confirmed the atypical pattern in PaxC, and suggested a
selective connection to timing of reproduction in Acropora, raising the possibility that
the PaxC gene or intron might play a role in coral spawning. In addition, the utility of
genome-wide markers provided a finer resolution of the Acropora phylogeny than the
CR or PaxC, and a number of cryptic species were discovered.
Phylogeographic and population genetic analyses of Acropora tenuis over 12° of
latitude revealed a phylogenetic break between Ashmore Reef and all other WA reefs,
suggesting that the post-Pleistocene re-colonization of WA reefs was from two different
sources. The integration of biogeographic history, genetics and spawning time lead to
the conclusion that rather than being an inherited genetic legacy, seasonal breeding
patterns are a result of long-term natural selection.
This study has three important implications for conservation and future research.
First, because the seasonality of breeding seasons in Western Australia is influenced by
local selection it is imperative that more research is devoted to understanding exactly
which environmental factors drive reproductive schedules and how they will be affected
by rapid climate change. Second, coral biodiversity on Western Australian reefs is
higher than is typically accounted for, due to the incidence of cryptic species; moreover
reproductive timing can contribute to cryptic speciation, so it is vital that reproductive
timing is incorporated into population genetic studies of corals, and that cryptic species
are identified. Third, PaxC appears to be influenced by some selective connection to
timing of reproduction in corals, so it should be used with caution as a phylogenetic
marker.
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Table of Contents
Abstract .............................................................................................................................. i
Declaration and Publications............................................................................................. v
Acknowledgements ......................................................................................................... vii
Foreword .......................................................................................................................... xi
1. General Introduction .................................................................................................. 1
2. Biannual coral spawning decreases at higher latitudes on Western Australian
Reefs ................................................................................................................................ 9
Abstract ........................................................................................................................ 10
Introduction ................................................................................................................. 10
Methods ....................................................................................................................... 12
Results & Discussion ................................................................................................... 15
3. Asynchronous spawning in sympatric populations of a hard coral reveals cryptic
species and ancient genetic lineages ............................................................................ 21
Abstract ........................................................................................................................ 22
Introduction ................................................................................................................. 22
Methods ....................................................................................................................... 25
Results ......................................................................................................................... 33
Discussion .................................................................................................................... 43
4. Asynchronous spawning and demographic history shape genetic differentiation
among populations of the hard coral Acropora tenuis in Western Australia .......... 49
Abstract ........................................................................................................................ 50
Introduction ................................................................................................................. 50
Methods ....................................................................................................................... 52
Results ......................................................................................................................... 58
Discussion .................................................................................................................... 62
5. Phylogenomics provides new insight into evolutionary relationships and
genealogical discordance in the reef-building coral genus Acropora ....................... 71
Abstract ........................................................................................................................ 72
Introduction ................................................................................................................. 72
Methods ....................................................................................................................... 74
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Results ......................................................................................................................... 78
Discussion ................................................................................................................... 83
6. Synthesis ..................................................................................................................... 87
References ....................................................................................................................... 98
Appendices .................................................................................................................... 111
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Declaration and Publications
This thesis is presented as a series of papers. All parts of this thesis have been designed,
executed, and written by Natalie Rosser with advice from my supervisor, Michael S.
Johnson. Chapter 5 was written with five co-authors (listed below). I confirm that I
made the following contribution to that chapter: 80% of the design, 40% of the data
collection, 85% of the data analyses, 85% of the interpretation and 90% of the writing. I
have obtained permission from all co-authors of Chapter 5 to include this chapter in my
thesis.
The details of publications arising from this thesis are:
Chapter 2: Rosser NL (2013) Biannual coral spawning decreases at higher latitudes
on Western Australian reefs. Coral Reefs 32, 455-460.
Chapter 3: Rosser NL (2015) Asynchronous spawning in sympatric populations of a
hard coral reveals cryptic species and ancient genetic lineages. Molecular
Ecology 24, 5006-5019
Chapter 4: Rosser NL (2016) Asynchronous spawning and demographic history shape
genetic differentiation amoung populations of the hard coral Acropora tenuis in
Western Australia. Molecular Phylogenetics and Evolution 98, 89-96.
Chapter 5: Rosser NL, Thomas L, Stankowski S, Richards ZT, Kennington WJ,
Johnson MS (in review) Phylogenomics provides new insight into evolutionary
relationships and genealogical discordance in the reef-building coral genus
Acropora. Proceedings of the Royal Society B Biological Sciences
---------------------------------------------------
Natalie Rosser
---------------------------------------------------
Michael Johnson (coordinating supervisor)
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Acknowledgements
First and foremost I offer my sincere thanks to Mike Johnson, to whom I am
overwhelmingly grateful, for his endless patience, wisdom, advice and guidance in the
supervision of this study; thank you for taking me on, Mike. I also especially thank
Yvette Hitchen for her invaluable help, advice and cheerfulness in the lab, which was
absolutely essential to this study.
A heartfelt thanks to Brad Burzec, Inger Shimell, Ivor Bruce, Eamon Dorricott
and Pete Rosser for helping me in the field, with an extra special thanks to Eamon and
Pete for putting up with my morning sickness and ill-humor during that field trip. Many
thanks to o amilton for starting me off in the lab including teaching me how to use a
pipette; staff at RPS for collecting the Acropora samoensis samples used in Chapter 3;
Jim Underwood and the Australian Institute of Marine Science for donating the A.
tenuis DNA used in Chapter 4; Zoe Richards and the Western Australian Museum for
collecting the Kimberley samples and allowing me to use them in Chapters 4 and 5;
Luke Thomas for donating the samples from the Abrolhos Islands used in Chapter 5;
Carden Wallace for identifying the samples collected in Chapters 2 and 4; Sean
Stankowski for running RAxML in Chapter 5; Stuart Field at DPaW for inviting me on
his Montebellos field trip and giving me access to the reproductive samples in Chapter
3; my office and lab mates for sharing the long journey with me (Esther Levy, Frances
Leung, Ana Hara, Jamie Tedeschi, Kaori Yokochi, Veronica Philips, Phil Allen, Luke
Thomas and Elf); my faithful and loving cat, Scout, for all the hours you kept me
company while I wrote; the members of my thesis review panel Jason Kennington and
Jane Prince for keeping me on track and reviewing my thesis; and my external
examiners David Ayre, Allen Chen and Steve Palumbi for examining my thesis.
I gratefully acknowledge the financial support I received from the following
people/organizations which made this project possible: the Holsworth Wildlife
Endowment, UWA School of Animal Biology, UWA Convocation, the Australian Coral
Reef Society, and Prof Mike Johnson. I also received in-kind support from Australian
Customs and The Department of Parks and Wildlife. All research was undertaken with
the appropriate state and federal permits.
To my friends and family, especially Penny Bunning, Mike Forde, Jas Cullen,
Aurora Brosnan, Tennille Irvine, Pete Rosser and Sandy Rosser, thank you for your
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ongoing support, encouragement and humor at crucial times when I needed it most. My
extra special thanks to Penny and mum for all the hours you spent minding Lilly for me,
and for always being there on the end of the phone.
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I dedicate this thesis to my dad, Andrew Rosser, who sadly passed away during my
PhD. He was my greatest inspiration, and he taught me perseverance and grit. I miss
him every day.
I also dedicate this thesis to my two most important people: my long-suffering husband,
Patrick Hollingworth, whose love, kindness and support I couldn’t be without; and my
little Lilly, who makes my life so rich with happiness.
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Foreword
Why should we care about coral reefs?
Coral reefs are spectacularly beautiful with exceptionally high biodiversity and a
wide range of intrinsic, ecological and economic values. South East Asia contains the
largest area of coral reefs in the world (34% of the world’s total), and more than 60% of
the ~600 million people of South East Asia live within 60km of the coast (Wilkinson
2008). Most of these people are highly dependent on coral reefs for food security,
livelihoods, building materials, medicines and trade goods, so for many people the value
of coral reefs may be purely economic; the most recent assessment of the potential
economic value of coral reefs in south-east Asia was US$12.7 billion (Wilkinson 2008).
For other people, the value of coral reefs lies in their recreational use and
aesthetic value, with divers, snorkellers, swimmers, surfers, anglers, boating enthusiasts
and others sharing in their recreational benefits. Natural environments also provide
spiritual values, and large, natural, unmodified seascapes, otherwise known as
wilderness, and can be valued for the solitude and limited access by humans.
Yet others would argue that coral reefs have intrinsic value; that all life depends
on the functioning of natural systems to ensure the supply of energy and nutrients, and
that the ecological processes that maintain the integrity of the biosphere should be
maintained (McNeely et al. 1990).
“Because after the last open coast of Australia is tamed, polluted and
overfished, what’s left except nostalgia and the desert at our backs?”
(Tim Winton, Land’s Edge)
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Chapter 1
General Introduction
“West coasts tend to be wild coasts, final coasts to be settled, lonelier places for
being last. In Australia the east coast is the pretty side, the Establishment side,
the civilized side...As in Ireland and America, our west is seen as something of a
new frontier, remote and open.”
(Tim Winton, Land’s Edge)
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Western Australia’s coral reefs
Coral reefs in Western Australia span over 17° of latitude, from the tropical
offshore reefs near Indonesia, to the subtropical reefs at the Houtman Abrolhos Islands.
Unlike the Great Barrier Reef on the east coast of Australia, which is a series of nearly
continuous reefs that stretch for over 2,000 km, the north-west coast is characterized by
a series of discontinuous reefs, which occur on the continental slope, on the continental
shelf edge and along the coastline. The offshore systems consist of atoll-like reefs on
the continental slope that rise from deep ramp settings (e.g. Scott Reef and the Rowley
Shoals), as well as shallower reefs perched on the edge of the continental shelf (e.g.
Ashmore Reef), while the inshore reefs occur along the coastline and around inshore
islands (Fig. 1.1).
Tropical marine fauna was established on the Western Australian coast in
the early to middle Miocene, when massive evolutionary radiation and latitudinal
expansion of the fauna of the Sea of Tethys was occurring (Wilson 2013). Western
Australian coral reefs have a long history of contraction and expansion in response to
oscillating glacial cycles, and consequent cooling and warming in the Pliocene and
Quaternary saw the contraction and expansion of the tropical fauna on the north-west
coast. The most recent of these was the last Pleistocene glaciation ~20,000 years ago,
when sea level was -130 m. At that time, the wide continental shelf of the north-west
coast was exposed, and the position of the WA coastline was along the 120 m contour
of the Rowley shelf (Yokoyama et al. 2001), leaving the present-day coastal reefs,
including Dampier, the Montebello Islands, and the Kimberley coast, on dry land. While
the offshore atolls of Scott Reef and the Rowley Shoals would have existed during the
LGM, whether the lower global temperatures and contracted habitats on the atolls
supported coral populations has been widely debated (Wilson 2013).
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Fig 1.1. A bathymetric map of north-west Australia showing the location of the coral reefs included in
this study (red stars). The margin of the continental shelf lies just beyond the 200 m contour.
It was thought that the connection between the Indo-West Pacific reefs of the
Indonesian Archipelago and Western Australia’s reefs is maintained in the present day
by a series of southward-flowing currents that originate in the Pacific and Indian Oceans
(Nof et al. 2002; Domingues 2006; D'Adamo et al. 2009). The flow of these currents is
strongest during the austral autumn (March-May; Holloway & Nye 1985), which
corresponds to the time of the major coral spawning season on the west coast, and this
was thought to create significant gene flow between northern Indonesian reefs, and
Western Australian reefs (Simpson 1991). Simpson also proposed that the breeding
season of corals in Australia is the result of an inherited, endogenous rhythm from
northern ancestral populations that influences reproductive seasonality on Western
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Australian reefs (Simpson 1988, 1991). He argued that, while the adaptive value may
have been lost in the descendant populations, the spawning rhythm remains because the
new regime does not exert a selective pressure to counteract the genetic legacy
(Babcock et al. 1994), or because the genetic connection between the regions is high
enough to inhibit the ability of the population to adapt to new conditions (Simpson
1988). This hypothesis has not been tested; however, a study of connectivity in the
broadcast spawning coral Acropora tenuis (Underwood 2009a) showed significant
genetic divergence between northern-offshore reefs (Scott Reef and Rowley Shoals) and
the southern-coastal reefs (Dampier and Ningaloo), suggesting that the genetic
connectivity between Indonesia and Western Australia is probably not high. In addition,
the recent discovery of biannual spawning in Western Australia has complicated
interpretations of spawning season.
Biannual coral spawning in Western Australia
Most reef building corals are broadcast-spawners that spawn once each year
(Baird et al. 2009a). In some regions, including Australia, a large number of species
participate in multi-specific or “mass” spawning events, where 20-30 species have been
recorded spawning on the same night during a mass spawning event (Willis et al. 1985;
Babcock et al. 1986). Synchronized spawning within coral populations results in higher
concentrations of gametes and better fertilization success (Oliver & Babcock 1992;
Levitan et al. 2004), which increases the probability of successful reproduction among
corals that spawn together (Harrison 2011).
While the seasonal timing of coral spawning events varies around the globe,
at each location annual coral spawning is highly predictable (Willis et al. 1985; Vize et
al. 2005; Levitan et al. 2011). In Western Australia historical reproductive surveys
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showed that most broadcast-spawning corals have a single annual reproductive period in
autumn (Babcock et al. 1994; Simpson 1991), but more recent evidence indicates that
on some reefs in north Western Australia there is a secondary reproductive season in
spring (Rosser & Gilmour 2008; Gilmour et al. 2009; Rosser & Baird 2009). Most
broadcast-spawning corals have an annual gametogenic cycle and spawn once a year,
but there are some instances in which corals undergo two gametogenic cycles,
culminating in spawning twice a year (Stobart et al. 1992; Guest et al. 2005; Mangubhai
& Harrison 2006). It was initially thought that some corals in Western Australia were
spawning twice each year (L. Smith pers. comm.), but research showed that among
conspecific colonies that spawned in October and March, each individual had only one
gametogenic cycle, spawning in either spring or autumn but not both (Rosser &
Gilmour 2008). This raised the possibility that conspecific spawning populations may
be separate reproductive populations, possibly representing two evolutionary lineages,
or alternatively, that colonies may sometimes switch their spawning time. At the outset
of this project, the degree to which conspecific colonies spawning in spring and autumn
on Western Australian reefs were reproductively isolated and genetically differentiated
was unknown. High levels of genetic differentiation (and potentially the early stages of
speciation) among conspecific colonies of corals that spawn in different months have
been recorded at other locations in the Indo-Pacific (Dai et al. 2000; Wolstenholme
2004), and it has been hypothesized that spawning at different times (e.g. different hours
on the same night) has been the basis for reproductive isolation and speciation in several
sympatric species of Acropora (van Oppen et al. 2001; Fukami et al. 2003).
The control of reproductive timing is complex, and it is thought that multiple
environmental cycles control the season, lunar phase and hour that spawning occurs
(Babcock et al. 1986; Vize et al. 2009). The seasonal cycle that controls coral spawning
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is the least well-understood, but correlative studies suggest it is influenced by solar
insolation levels (Penland et al. 2004; van Woesik et al. 2006), water temperature
(Babcock et al. 1986; Fan & Dai 1999; Nozawa 2012) and calm weather (van Woesik
2010). The night of spawning is set by the lunar phase (Babcock et al. 1986; Hunter
1988; Oliver et al. 1988), and the hour (and even minute) of the spawning window is set
by sunset time (Knowlton et al. 1997; Levitan et al. 2004; Vize et al. 2009). There is
also a genetic component that underlies spawning time (Levitan et al. 2011), but it is
unclear how much the timing of coral spawning is entrained by biological rhythms or
regulated directly by environmental signals. Corals contain several circadian clock
genes that are likely to regulate entrained processes (Levy et al. 2007; Vize 2009;
Shoguchi et al. 2013), but their exact role in regulating reproductive behavior is not well
understood.
The importance of a genetic perspective
The use of genetics to understand the origin and consequences of seasonal
breeding in corals in Western Australia is a powerful approach that will allow me to test
for reproductive isolation between the spring and autumn spawners (hence whether
individuals retain their spawning season), and the evolutionary relationships of spring
and autumn spawners at different geographic scales. Individuals vary in the composition
of their DNA, and the fate of any given genetic variant will be influenced by the
biological and ecological circumstances of its life, such as reproductive success,
migration, population size, connectivity, natural selection and historical events
(Sunnucks 2000). Hence, by measuring the genetic variation among individuals and
applying models, we can make inferences about the biology of organisms and the
processes that have shaped their existence. Different genetic markers have different
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rates of change, and so provide information about population biology over different
time scales. Microsatellite markers, for example, can describe small genetic differences
between populations, and are useful for discerning information about population
structure, gene flow and dispersal (Estoup & Angers 1998), but high mutation rates and
constraints on allele size eliminate signature events in the distant past, so they have
limited use in inferring evolutionary history (Garza et al. 1995; Paetkau et al. 1997;
Selkoe & Toonen 2006). DNA sequences are useful for inferring evolutionary
relationships, and well-resolved phylogenetic trees are useful for understanding
evolutionary history and longer term processes such as speciation and selection (Avise
2004). Theoretical models use either gene frequencies (as in the case of microsatellites
or single-locus markers) or geneology and genetic distance (as in DNA sequences) to
measure the distribution of genetic variation.
The extent to which natural populations become genetically differentiated
depends largely upon the amount of gene flow between them (e.g. Johnson & Black
1998, 2006b; Ayre & Hughes 2004). Reproductive isolation many arise when some sort
of barrier prevents groups from exchanging gametes and genes, such as asynchronous
spawning, habitat specialization, selection, or gamete compatibility (Palumbi 1994).
Such reproductive isolation constrains gene flow between groups, and may eventually
lead to genetic divergence and speciation. Hence differences in reproductive timing that
result in genetic subdivision within populations, is potentially a powerful component of
the evolution of biological diversity and ultimately in the splitting of lineages to form
new species.
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Aims and scope of this thesis
This thesis explores the influence of asynchronous reproductive timing on
patterns of genetic structure in coral populations, at both ecological and evolutionary
levels, in the context of understanding the evolution of seasonal breeding patterns in
Western Australia. Each chapter focuses on a different set of questions that are
described in the introduction of each chapter. Chapter 2 documents the geographical
extent of coral spawning in autumn and spring on WA reefs, to understand how the
seasonality of spawning varies with latitude. Chapter 3 explores the extent to which
spring- and autumn-spawning cohorts of Acropora samoensis are reproductively
isolated and genetically diverged in sympatric populations in the Pilbara region. Chapter
4 widens the geographic scope, examining the phylogeography and population genetics
of allopatric populations of A. tenuis with different spawning patterns. Finally, Chapter
5 expands on the unexpected results from Chapters 3 and 4, which revealed divergent
selection on the PaxC marker, and compares molecular phylogenies from 20 species of
Acropora using the mtDNA control region, PaxC, and 10,034 genome-wide SNPs to
test whether PaxC distorts phylogenetic inferences.
The broad questions integrating these chapters are:
(i) Are conspecific colonies that spawn in autumn and spring reproductively
isolated and genetically differentiated, or do colonies switch spawning time, allowing
genetic mixing?
(ii) Are conspecific autumn- and spring-spawning colonies associated with
distinct phylogenetic lineages?
(iii) Are spawning patterns on WA reefs a result of an inherited legacy from
northern ancestors, or natural selection?
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Chapter 2
Biannual coral spawning decreases at higher latitudes on
Western Australian reefs
This chapter has been published in Coral Reefs:
Rosser NL (2013) Biannual coral spawning decreases at higher latitudes on Western
Australian reefs. Coral Reefs 32, 455-460.
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Abstract
Seasonal differences in the timing of multi-specific coral spawning between the eastern
and western coasts of Australia may be the result of a genetic legacy or of adaptation to
local conditions. Using estimates of the proportions of coral species that spawned in
spring and autumn at Ashmore Reef (12°S) and Ningaloo Reef (23°S) in Western
Australia, in combination with findings of previous surveys, I examined whether
reproductive seasonality varied with latitude. A consistently high proportion of species
spawned during the main reproductive season in autumn regardless of latitude.
However, there was a clear decrease in the proportion of species spawning in spring,
from an average of 49% at Ashmore Reef (12°S) to 7% at Ningaloo Reef (23°S).This
suggests that seasonality of coral reproduction in Western Australia reflects
environmental gradients and natural selection rather than an inherited genetic legacy.
Introduction
Broadcast spawning corals typically reproduce once a year in highly synchronized
events (Baird et al. 2009a). In many locations, the timing of multi-specific spawning is
remarkably predictable (Willis et al. 1985; Simpson 1991; Vize et al. 2005;), but the
factors that influence reproductive seasonality are still poorly understood. In Australia,
synchronized, multi-specific spawning occurs primarily in spring on the east coast and
in autumn on the west coast, while there is also a secondary, multi-specific spawning
period in the opposite season (in autumn on the east coast and in spring on the west
coast; Stobart 1994; Wolstenholme 2004; Rosser & Gilmour 2008).
Two hypotheses have been proposed to explain the difference in spawning
seasons on the east and west coasts of Australia. The ‘genetic legacy’ hypothesis
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(Simpson 1988, 1991) suggests that the reproductive seasonality of corals in Australia is
the result of an inherited, endogenous rhythm from northern ancestors that controls
spawning seasonality. The different coral spawning seasons on east and west coasts in
Australia result from the prevailing southward-flowing currents on each coast in
different seasons (the Leeuwin Current in autumn on the west coast, and the East
Australian Current in spring on the east coast), that selectively disperse coral larvae
down the west coast in autumn, and down the east coast in spring. This implies that
local environmental variables are not strong enough to overcome the inherited historical
constraints (Babcock et al. 1994).
An alternative hypothesis is that reproductive seasonality on each coast is
the outcome of long-term natural selection to spawn when conditions favour offspring
survival (Oliver et al. 1988). Reproductive seasonality in corals could be influenced by
water temperature (Willis et al. 1985), photoperiod (Babcock et al. 1994), monthly
rainfall (Mendes & Woodley 2002), solar insolation (van Woesik et al. 2006), and
regional wind fields (van Woesik 2010). However, none of these variables can be easily
linked to the timing of coral spawning events in Western Australia.
The occurrence of multi-specific coral spawning in spring on some Western
Australian reefs was discovered fairly recently (Rosser & Gilmour 2008; Gilmour et al.
2009), and reproductive surveys have suggested that spring-spawning patterns may vary
geographically within Western Australia (Rosser & Baird 2009). This heterogeneity
could provide insight into the factors that influence reproductive seasonality, but the
available data are limited. Here, I surveyed the reproductive state of coral assemblages
in spring and autumn at Ashmore Reef (12ºS) and Ningaloo Reef (23ºS), so extending
the latitudinal range both north and south of previous surveys (Rosser & Gilmour 2008;
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Gilmour et al. 2009; Rosser & Baird 2009), to better determine how the seasonality of
spawning varies with latitude on the Western Australian coast.
Methods
Reproductive surveys were made at Ashmore Reef (12°14’ S, 122°58’ E) and Ningaloo
Reef (22-23°10' S, 113°45' E) (Fig. 2.1), extending the latitudinal range of sites studied
in WA by 4° of latitude (600 km). Surveys were made at both Ashmore and Ningaloo
Reefs in spring 2010, at Ashmore Reef in autumn 2011, and at Ningaloo Reef in autumn
2012, on and around the full moons of October and February in each case. During each
survey every attempt was made to sample five colonies from a minimum of 20 species
(Styan & Rosser 2012) that typically participate in “mass spawning” events (i.e.,
hermaphroditic broadcast spawners; Harrison & Wallace 1990). If fewer than five
colonies per species were sampled, the species was eliminated from the data set unless
spawning was detected in at least one colony, in which case the species was included in
the data set.
Cumulative binomial probability distributions were calculated in MS Excel
using the BINOMDIST function, to estimate the probability that at least one spawning
colony would be detected when sampling five colonies per species (Appendix 2.1). This
showed that when sampling five colonies per species, spawning was only likely to be
detected in a given species when > 40% of colonies were spawning Table 2.1).
Therefore this sampling design may underestimate the proportion of species spawning
because it is only likely to detect spawning in species where a high proportion of
colonies are spawning, however, this agrees with the original definition of “mass”
synchronous spawning (Willis et al. 1985).
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Surveys were conducted in situ by breaking open a small piece of each
colony to reveal developing oocytes (Baird et al. 2009b). The reproductive state of each
colony was classified based on the visibility and colour of developing oocytes; colonies
were assumed to be going to spawn within three months if pigmented or white oocytes
were visible. I assumed that they would not spawn within three months if oocytes were
not visible (Babcock 1984; Harrison et al. 1984; Wallace 1985; Kenyon 1992; Hanafy
et al. 2010). In corals with small oocytes (e.g., faviids), broken samples were collected
and examined with a 10x hand lens out of the water. Colonies were selected
haphazardly, with the criteria that they were of reproductive size (colony diameter >
20cm; Wallace 1985) and were not visibly compromised (e.g., bleached or disease-
affected, which may affect their ability to reproduce). Corals were identified in-situ, or
skeletal samples were collected and sent to the Museum of Tropical Queensland for
identification.
Fig 2.1. Sampling locations of spring and autumn surveys. Dark circles indicate sites
surveyed in this study, light circles indicate sites surveyed previously
Ashmore Reef
Ningaloo Reef
Scott ReefBonaparte Archipelago
Dampier
Barrow Island
130°0'0"E
130°0'0"E
125°0'0"E
125°0'0"E
120°0'0"E
120°0'0"E
115°0'0"E
115°0'0"E
10°0'0"S 10°0'0"S
15°0'0"S 15°0'0"S
20°0'0"S 20°0'0"S
0 200 400 Kilometers
Indian Ocean
Timor Sea
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To test whether the proportion of species spawning in a given season
differed between sites, the frequency of species spawning or not spawning in each
season at Ashmore and Ningaloo Reefs were compared using Fisher’s exact tests
(SPSS). Species were only included if five or more colonies were sampled at both sites.
To test for a general association with latitude, the proportions of species spawning in
spring and autumn in this study were combined with data from three other sites (Fig.
2.2): Scott Reef (Gilmour et al. 2009); the Bonaparte Archipelago (Rosser and Baird
2009); and the Dampier Archipelago (Baird et al. 2011). The previous surveys used the
same in-situ reproductive assessment as in this study (i.e., reproductive state was not
classified histologically) and similar classification criteria for maturity of oocytes (the
difference being that data from Scott Reef and the Bonaparte Archipelago included only
the numbers of colonies with pigmented oocytes, and not white oocytes). Colonies were
classified as spawning in “spring” if they had mature oocytes in October, November or
December, and as spawning in “autumn” if they had mature oocytes in February, March
or April. Permutation tests were used to test for heterogeneity of spawning proportion
among sites using the 2 x N Monte Carlo contingency test in P-Stat (Bill Engels, 1993-
1997, http://engels.genetics.wisc.edu/pstat/).
Table 2.1. Relationship between proportion of colonies of a species that have ripe oocytes and
the probability of detecting ripe oocytes in a sample of 5 colonies per species
Proportion of colonies spawning within a
species
chance of detecting ≥ 1 spawning colony when
sampling 5 colonies per species
10 %
40 %
20 %
70 %
30 %
80 %
40 %
90 %
50 %
100 %
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Results & Discussion
There was a significant difference in the proportion of species spawning in spring
between Ashmore and Ningaloo Reefs (n=18, p = 0.008, 2-tailed Fisher’s Exact test). At
Ashmore Reef 49% of species (19 of 39) were estimated to spawn in spring, compared
with only 7% species (2 of 27) at Ningaloo Reef (Table 2.2, Fig. 2.2). Contrastingly in
autumn, there was no significant difference between the proportions of species
spawning at Ashmore and Ningaloo Reefs (n=22, p= 0.4): 82% species (41 of 50) at
Ashmore Reef, and 95% species (37 of 39) at Ningaloo Reef (Table 2.2, Fig. 2.2).
Combining the data from Ashmore and Ningaloo Reefs with those from the
three previously surveyed WA sites (Fig. 2.2) showed that there is a clear latitudinal
component to the level of mass spawning in spring on WA reefs. The proportions of
species spawning in spring at different latitudes ranged from 7-57% (p = 0.00005, 2 x N
Monte Carlo contingency test). In contrast, a high proportion of species spawn in the
main reproductive season in autumn at all latitudes (range 82-98%), although the
proportions did differ among the sites (p = 0.01, 2 x N Monte Carlo contingency test).
If reproductive timing in Australia is the result of an inherited rhythm from
ancestral populations to the north of Western Australia (Simpson 1991), then a
latitudinal decline in spring spawning may be a function of dispersal distance from
source populations, due to the weak flow of the Leeuwin Current in spring (Cresswell
1991). However, at least three species (Acropora. tenuis, A. millepora and A. secale)
mostly spawned in spring at Ashmore Reef in the north, but mostly in autumn at
Ningaloo Reef in the south (Table 2.2). Thus, if reproductive seasonality is a result of an
inherited rhythm, either the southern Ningaloo populations are not connected to the
northern Ashmore populations, or corals that spawn in spring conditions at Ningaloo
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Reef are selected against and removed from the contemporary population (van Woesik
2010). The predominance of autumn spawning at Ningaloo Reef seems unlikely to be
simply the result of historical constraints on which genotypes arrived from the north. In
seasonal environments, organisms that reproduce only once a year can be expected to
time their reproduction to coincide with the most suitable conditions for success.
Fig. 2.2. The proportion of species spawning in spring (Oct-Dec) and autumn (Feb-Apr) at
different latitudes in Western Australia; (a) = this study, (b) = Gilmour et al. 2009, (c) = Rosser
and Baird 2009, (d) = Baird et al. 2011, (e) = this study. Error bars are 95% confidence
intervals.
If natural selection determines the timing of reproductive seasonality in Western
Australia, then a latitudinal decline in spring spawning may be a result of environmental
gradients that limit successful reproduction in spring, i.e., successful gametogenesis,
fertilisation or larval survival do not occur in the majority of species above or below
certain values. For example, sea temperature may control timing of reproduction
because the final maturation of gametes requires a minimum water temperature (Hunter
1988; van Woesik et al. 2006; Baird et al. 2009b). Similarly wind speed can influence
fertilisation, so predictably strong winds may limit reproductive success at certain times
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in particular locations (van Woesik 2010). The latitudinal decline in the secondary
spawning season, but not in the main spawning season in this study and others (Baird et
al. 2009a) suggests that there is not a simple correlation between a single environmental
variable and spawning. It is unlikely that one climatic variable would act alone to
influence reproductive seasonality, as shown by plants and some broadcast-spawning
invertebrates that respond to changes in the combination of temperature and day length
(Olive & Pillai 1983; Simpson & Dean 2002). Hence, reproductive seasonality is likely
to depend on a combination of environmental variables.
The occurrence of multiple environmental variables acting in concert or
synergistically to influence reproductive seasonality in corals has not been fully
explored in any study. A multivariate analysis on the interactions between the number
of species spawning, latitude, and climatic variables was beyond the scope of this study
because of the small number of sites/populations (meaning any multivariate analysis of
this data would have more independent variables than the sample size and any complex
analysis would be invalid). However, this is an interesting avenue for future research
that may provide insight into the factors that influence reproductive seasonality, not
only on Australian reefs, but on coral reefs worldwide. The patterns revealed in this
study offer much scope for testing hypotheses about the factors that influence
reproductive seasonality in the hope of furthering our understanding of the evolution of
reproductive seasonality in broadcast spawning corals.
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Table 2.2. The proportion (%) of colonies in each species that spawn in spring and autumn at
Ashmore and Ningaloo Reefs as determined by in-situ observations of gamete maturity; *
indicate cases where a single colony was sampled and found to have mature gametes, but no
inference was made about the proportion of all colonies that were reproductively active.
Ashmore Ningaloo
Spring
(%)
N Autumn
(%)
N Spring
(%)
N Autumn
(%)
N
Acropora acueleus 50 2
Acropora anthocercis 71 7 57 7 100 4
Acropora austera 40 5 0 5 0 6
Acropora cerealis 0 5 100 7 0 5
Acropora cytherea 60 5 63 8 * 1
Acropora digitifera 0 5 100 6 0 5 100 8
Acropora divaricata 0 5 57 7 100 4
Acropora florida 67 6 33 9 0 5 83 6
Acropora gemmifera 29 7 40 10 100 4
Acropora grandis 0 5 33 3 0 6 86 7
Acropora hoeksemani 50 2
Acropora humilis 43 7 56 9 * 1
Acropora hyacinthis 20 5 17 6 0 5 80 5
Acropora intermedia 63 8 0 5 57 7
Acropora latistella 0 5 86 7 71 7
Acropora listeri 83 6 0 6 * 1
Acropora loripes 50 10 0 9
Acropora lutkeni 100 5 0 6
Acropora micropthalma 0 5 40 5
Acropora millepora 60 22 0 20 0 17 91 20
Acropora monticulosa 60 5 0 7
Acropora muricata 0 5 55 11 0 5 56 9
Acropora nana 25 4
Acropora nasuta 0 5 100 6
Acropora paniculata 0 5 100 3
Acropora papillare 0 6 75 8 0 9
Acropora robusta 0 5 100 5 0 5 67 6
Acropora samoensis 0 5 29 7 0 9 82 11
Acropora secale 60 5 0 8 0 5 100 6
Acropora spicifera 38 8 83 6 0 5 56 9
Acropora subulata 0 5 40 5
Acropora sukarnoi * 1 67 3
Acropora tenuis 80 20 0 20 0 16 68 15
Acropora valensinesi 83 6
Acropora valida 0 5 83 6 0 5 100 6
Acropora vaughni * 1
Echinophyllia aspera 0 5 100 7 0 5 100 5
Echinopora lamellosa 80 5 0 5 50 6
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Favia matthai 100 3 67 3
Favia pallida 0 5 100 6 100 7
Favia speciosa 80 5
Favia stelligera * 1
Favites halicora 0 6 71 7 80 5
Galaxea astreata 100 5
Goniastrea favulus 67 3
Goniastrea pectinata 57 7
Gonistrea retiformis 20 5 40 5 0 6 83 6
Hydnophora excesac 0 5 0 6 75 4
Hydnophora rigida 20 5 71 7
Leptoria Phrygia 60 5
Lobophyllia hemprichii 0 5 80 5
Montipora capricornis 0 5 80 5
Montipora spumosa 0 5 80 5
Merulina ampliata 0 5 60 10 0 5 80 5
Merulina scabricula 0 5 78 9 0 5 60 5
Montastrea curta * 1
Oulophyllia crispa 0 5
Pachyseris speciosa 0 5
Platygyra daedalea * 1 0 7 100 6
Platygyra pini 67 3 100 2
Podobacia crustacean 86 7
Total species 19 39 41 50 2 27 37 39
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Chapter 3
Asynchronous spawning in sympatric populations of a
hard coral reveals cryptic species and ancient genetic
lineages
This chapter has been published in Molecular Ecology:
Rosser NL (2015) Asynchronous spawning in sympatric populations of a hard coral
reveals cryptic species and ancient genetic lineages. Molecular Ecology 24, 5006-5019.
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Abstract
Genetic subdivision within a species is a vital component of the evolution of
biodiversity. In some species of Acropora corals in Western Australia, con-specific
individuals spawn in two seasons six months apart, which has the potential to impede
gene flow and result in genetic divergence. Genetic comparison of sympatric spring and
autumn spawners of Acropora samoensis was conducted to assess the level of
reproductive isolation and genetic divergence between the spawning groups based on
multiple loci (13 microsatellite loci, the mitochondrial control region and two nuclear
introns). Bayesian clustering and principal co-ordinates analysis of the microsatellite
loci showed a high level of genetic differentiation between the spawning groups (F’ST =
0.30; P < 0.001), as did the sequence data from PaxC and Calmodulin (ΦST = 0.97 and
0.31, respectively). At the PaxC locus the autumn and spring spawners were associated
with two divergent lineages that were separated by an evolutionary distance of 1.7%,
and statistical tests suggest divergent selection in PaxC, suggesting this gene may play a
role in coral spawning. This study indicates that the autumn and spring spawners
represent two cryptic species, and highlights the importance of asynchronous spawning
as a mechanism influencing speciation in corals.
Introduction
Genetic subdivision within populations can ultimately lead to the splitting of lineages
and the evolution of new species, and molecular genetic studies of population structure
have resulted in the discovery of many cryptic species across both plant and animal
kingdoms (Bickford et al. 2007). Genetic differences may arise when some sort of
barrier prevents cohorts from exchanging genes, and one possible mechanism
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influencing within-species diversity is asynchronous reproduction. In sympatric species,
differences in the timing of reproduction that prevent gametes from crossing are likely
to impede gene flow and result in genetic divergence between reproductive cohorts.
Studies of flowering plants, salmonid fishes, sea urchins and other invertebrates have
shown that sympatric populations with different breeding times are often significantly
and substantially differentiated at neutral loci (Hendry & Day 2005; Savolainen et al.
2006; Bird et al. 2011; Binks et al. 2012). Ultimately the degree to which populations
may become differentiated depends not only on the level of reproductive isolation
between them, but also on the nature of selection on particular genes. During the early
stages of ecological speciation, genomic regions of functional importance quickly
diverge under selection (Turner et al. 2005; Via 2009), so studying populations in which
ecotypes or races are not yet completely reproductively isolated can reveal genetic
changes that contribute to reproductive isolation before they are confounded by
additional genetic differences that accumulate after speciation (Via 2009).
In addition to molecular and morphological differentiation, cryptic species
have been identified along other lines of evidence, including habitat specificity (Warner
et al. 2015), mode of reproduction (Schmidt-Roach et al. 2012) and symbiont
association (Pinzon & LaJeunesse 2010). However, relatively few studies have
investigated the association between differences in the timing of reproduction and
cryptic speciation in corals. The importance of reproductive timing in maintaining gene
flow is evident in broadcast-spawners which spawn gametes into the water where
fertilization takes place externally within hours, so individuals that spawn more than
several hours apart are unlikely to cross-fertilize. High levels of genetic differentiation
among con-specific colonies of corals that spawn at different times have been recorded
at various locations in the Indo-Pacific (Dai et al. 2000; Wolstenholme 2004), and even
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spawning at different hours on the same night is sufficient to prevent colonies from
interbreeding, and has resulted in genetic divergence (Knowlton et al. 1997; Fukami et
al. 2003).
Genetic differentiation between the reproductive cohorts can occur only
when reproductive timing is strongly heritable and not individually flexible. For
example, snow buttercups (Ranunculus adoneus) do not show significant differentiation
between early and late-flowering sites, because the variation in flowering time is
determined by snow melt times (i.e. environmental) and does not have a genetic basis
(Stanton et al. 1997). Spawning time of corals, however, is thought to be strongly
heritable, with some studies showing that individuals of Colpophyllia and Montastrea
spawn at the same time every year (within minutes) over many years (Vize et al. 2005;
Levitan et al. 2011). Nevertheless, transplant experiments in Echinopora and
Montastrea have also shown that reproductive timing can change in response to new
environmental conditions (Fan & Dai 1999; Levitan et al. 2011), suggesting that corals
have some degree of reproductive plasticity.
Broadcast-spawning corals typically reproduce in annual, synchronized
spawning events (Harrison & Wallace 1990; Baird et al. 2009a), which generally occur
in autumn in Western Australia (Simpson 1991). Contrary to this pattern, however,
there is also a secondary spawning event in spring on some reefs in north-western
Australia (Rosser 2013). A previous survey of two species (Acropora samoensis and
Acropora cytherea) in which colonies were tagged and sampled over multiple years,
showed that adjacent, conspecific colonies spawned in different seasons (autumn and
spring), raising the question of whether this asynchrony could lead to reproductive
isolation (Rosser & Gilmour 2008). Here I examine genetic differences between
individuals of Acropora samoensis on a reef where individuals that spawn in autumn
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and spring occur adjacent to one another, and are morphologically indistinguishable.
The aim of this study was to determine: (a) the extent to which the autumn-spawning
and spring-spawning cohorts are reproductively isolated and genetically differentiated
and (b) whether the con-specific spawning groups represent independently evolving
lineages.
Methods
Population and reproductive sampling
The reproductive state of Acropora can be gauged in-situ by examining the
presence/absence of visible eggs, and colour of developing eggs in broken colonies
(Baird et al. 2002) in these hermaphroditic corals. Mature eggs in Acropora change
from white to pink approximately 1-3 weeks prior to spawning (Harrison et al. 1984;
Wallace 1985, 1999), therefore colonies containing visible, pigmented eggs in-situ are
assumed to spawn within approximately 3 weeks, while colonies with visible but white
(unpigmented) eggs are assumed to spawn within 1-3 months (Baird et al. 2002).
Reproductive and genetic sampling of Acropora samoensis was conducted
on a reef off Barrow Island, Western Australia (20.7861 °S, 115.5067 °E). To locate the
two reproductive groups of A. samoensis, 80 colonies were tagged and sampled in
November (spring) 2010, January (late summer) 2011 and October (spring) 2011.
Reproductive maturity was measured in-situ on each occasion and classified as
‘pigmented’ ‘white’ or ‘absent’ (after Baird et al. 2002). All colonies were located
along two 100 m transects in the same habitat (patch reef / lagoonal habitat) at the same
depth (approximately 6 m).
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In addition, samples were collected and fixed in 10% formalin in seawater
for preservation during each reproductive survey. Samples from a subset of the tagged
colonies (n=38) were and then decalcified in a solution of 10% formic acid and
dissected to verify the in situ assessments. Five polyps from each specimen were
dissected and viewed under a stereomicroscope (Wallace 1985); egg size was measured
using a graticule slide and calculated as the geometric mean (Wallace 1985), and the
presence/absence of testes was noted. In the Acropora, mean egg size ranges from 200-
600 µm within 8 weeks of spawning, and from 300–945 µm prior to release (Wallace
1985; Szmant 1986; Kenyon 1992; Wallace 1999; Vargas-Angel et al. 2006; Rosser &
Gilmour 2008). Testes develop and become visible in dissected samples only 4–6 weeks
prior to spawning, so the presence/absence of testes can also be used to gauge
reproductive maturity (Wallace 1985). In each colony, the time of spawning was
inferred from (a) the presence/absence of eggs and egg colour in situ, (b) the
presence/absence of testes in a subset of dissected samples and (c) egg size in a subset
of dissected samples.
Skeletal voucher specimens from sequenced colonies were bleached and
examined by a coral taxonomist, Dr Zoe Richards, from the Western Australian
Museum (WAM). All voucher specimens are housed at the WAM (registration numbers
WAMZ84437 to WAMZ84464; corresponding GenBank Accession Numbers listed on
Dryad http://dx.doi.org/10.5061/dryad.20g8r and in Appendix 3.1).
PCR, DNA sequencing and microsatellite genotyping
Tissue samples collected for genetic analysis were preserved in 95% ethanol, which was
replaced after 24 hours and again after one week. DNA was extracted from branch tips
using DNeasy DNA extraction kits for animal tissue (Qiagen, USA) according to the
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manufacturer’s instructions. To test for genetic differentiation between spawning
groups, genotypes at 13 microsatellite loci were determined for each individual using
primers developed by Wang et al. (2009) for Acropora (Appendix 3.2). Loci were
amplified using fluorescently labelled primers in 13 single-plex reactions. PCRs of 5µL
contained 1 µL of Bioline buffer, 0.5 µL of BSA, 0.1 µL each of forward and reverse
primers, 0.1 µL of Bioline Taq, 2.2 µL of water and 1 µL of DNA. PCR amplifications
were carried out in an Eppendorf Mastercycler, and consisted of an initial denaturation
at 95°C for 3 mins, followed by 35 cycles of 60 sec at 95°C, 60 sec at 49°C, and 60 sec
at 72°C, and finally 72°C for 5 mins. Fragments were analysed using an Applied
Biosystems 3730 capillary sequencer, and allele sizes scored using GeneMarker 1.91
(SoftGenetics, LLC). Microsatellite genotypes from all individuals were submitted to
Dryad ( http://dx.doi.org/10.5061/dryad.20g8r ).
DNA sequencing of a mitochondrial and two nuclear markers was used to
determine whether seasonal spawning groups represent separate evolutionary lineages.
The mtDNA control region (CR) was amplified in a PCR using primers rns (5’-
GGTTTCTAATACCTCCGAGG-3’) and Cox3 (5’- TACATAACACTGCCCACAGT-
3’;van Oppen et al. 2001). The nuclear PaxC intron was amplified using the primers
PaxC_intron-FP1 (5’- TCCAGAGCAGTTAGAGATGCTGG-3’) and PaxC_intron-
RP1 (5’-GGCGATTTGAGAACCAAACCTGTA-3’; van Oppen et al. 2000), and the
nuclear Calmodulin intron was amplified using the primers CalMf (5’-
GAGGTTGATGCTGATGGTGAG-3’) and CalMr2 (5’-
CAGGGAAGTCTATTGTGCC-3’;Vollmer & Palumbi 2002). PCRs contained 1µL
MgCl2 (50nM), 1.2 µL dNTPs (2.5nM), 0.2 µL platinum Taq, 2.5 µL 10 x PCR buffer,
1 µL each of the forward and reverse primers, 2 µL of DNA, and 17.1 µL dH2O in a 25
µL reaction. Positive (known DNA sample) and negative (no DNA) controls were
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included with each reaction. Thermocycling profiles consisted of an initial denaturation
step of 95°C for 3 min, followed by 35 cycles of 94°C for 30 sec, 59°C for 1 min and
72°C for 1 min, and finally 72°C for 10 min. Products were sequenced in both
directions at the Australian Genome Research Facility (AGRF) on an Applied
Biosystems 3730xl DNA sequencer using POP7 capillaries gel matrix. Individuals
heterozygous for a single nucleotide polymorphism (SNP) were resolved by comparing
forward and reverse sequences at variable sites, and individuals heterozygous for
multiple SNPs and indels were resolved via cloning (n=8) using TOPO-TA cloning kits
(Invitrogen USA). Sequences were edited manually in Sequencher 4.5 (Gene Codes
Corp., Ann Arbor, MI, USA) and aligned using ClustalW in MEGA6 (Tamura et al.
2013). Unique sequences were submitted to GenBank (Accession Numbers KT447642-
KT447682).
Microsatellite analyses
Potential genotyping errors and spurious allele scores caused by large allele dropout or
stuttering were assessed in the program Microchecker (van Oosterhout et al. 2004). The
program FreeNa (Chapuis & Estoup 2007) was used to estimate null allele frequencies
for each locus and spawning group. This program creates a dataset corrected for null
alleles, and uses it to calculate global and pairwise FST values across all loci and for
each locus. There was little difference between the corrected FST values and the
uncorrected values (see Results), so the original dataset was used for the remaining
analyses.
The presence of linkage disequilibrium between all pairs of loci in each
spawning group, and departures from Hardy-Weinberg Equilibrium (HWE) among loci
and spawning groups were tested using GENEPOP 4.2 (Raymond & Rousset 1995)
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with sequential Bonferroni correction (Rice 1989) for multiple comparisons. Estimates
of molecular diversity and population genetic structure were calculated in Arlequin
3.5.1.2 (Excoffier & Lischer 2010). Diversity indicies included gene diversity (HE) and
the effective number of haplotypes/alleles (Ne). FST (Weir & Cockerham 1984) was
calculated for each locus with 1000 permutations (α = 0.004 after Bonferonni
correction; Rice 1989) to determine how many loci showed differences between the
spawning groups. Standardized F’ST (Meirmans 2006) was calculated in GenALEx 6.5
(Peakall & Smouse 2006, 2012) with 1000 permutations, and ΦST (Excoffier et al.
1992) was calculated in Arlequin with 1000 permutations using the infinite allele model
(IAM). Loci were also checked for private alleles associated with spawning groups. To
illustrate the patterns of genotypic similarities, Principal Coordinates Analysis (PCoA)
was performed on a matrix of pairwise genetic distance (codom-genotypic) in
GenALEx 6.5 (Peakall & Smouse 2006, 2012).
The Bayesian clustering method of Pritchard et al. (2000) was implemented
in the program STRUCTURE 2.3.3 to estimate the number of population clusters (K) in
the microsatellite data, and to assign individuals to these clusters based on genotypic
data. The first analysis, using the simulation method described by Evanno et al. (2005),
included all individuals with no a priori information about spawning season. Ten
independent runs were performed for each value of K (1-5) using the admixture model
with correlated allele frequencies with a burn in of 100,000 followed by 106 MCMC
iterations. A subsequent STRUCTURE analysis was performed on the autumn-
spawning group to test for sub-structure in this group (but was not done on the spring-
spawning group due to the small sample size of this group).
Once the optimal number of populations had been estimated (the result of
which was K=2), STRUCTURE was run again using the ‘Locprior’ model to assign
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individuals to each cluster based on genotypic data. The ‘Locprior’ model is an
extension of the basic STRUCTURE model, that has been proposed for data sets with
low information content (i.e. relatively few loci or individuals; Hubisz et al. 2009);
hence this model was used due to the small sample size of the spring-spawning group.
This model makes use of sampling information (i.e. spawning season) by placing a
higher prior weight on clustering outcomes when they are correlated with sampling
information, but it does not uncover structure where none exists (Hubisz et al. 2009).
STRUCTURE was also used to identify any potential hybrid individuals by
identifying individuals with parental or grandparental ancestry from the other
population (Pritchard et al. 2010). STRUCTURE was run using ‘Usepopinfo’ to assign
individuals to each spawning group and the ‘Gensback’ option to test whether each
individual had an ancestor in the preceding two generations (i.e. parent or grandparent).
The output from this mode also includes posterior probabilities that each individual is
correctly assigned to the given population, and this was used as a check that each
individual had been assigned to the correct population.
DNA sequence analyses
To determine the genealogical relationships between haplotypes of each sequenced
marker, maximum parsimony haplotype networks were created in NETWORK 4.6.1.0.
(Fluxus Technology Ltd) using the median-joining algorithm. The Bayesian Information
Criterion scores calculated in MEGA6 (Tamura et al. 2013) were used to select the best
fitting models of sequence evolution, which were used to calculate evolutionary
distances between and within spawning groups in MEGA6. Measures of genetic
diversity and differentiation, including gene diversity (HE), effective number of
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alleles/haplotypes (Ne), FST (Weir & Cockerham 1984) and ΦST (Excoffier et al. 1992)
with 1000 permutations were calculated in Arlequin 3.5.1.2 (Excoffier & Lischer 2010).
To provide a more direct historical context for the evolution of the A.
samoensis spawning groups, phylogenetic analyses were conducted with a range of
other Acropora species for PaxC and CR (but not Calmodulin due to a dearth of
available sequences) from sequences downloaded from GenBank. Phylogenetic
relationships among unique haplotypes were estimated using maximum likelihood
methods (with bootstrap values conducted for 1000 replicates) implemented in MEGA6
(Tamura et al. 2013) for each gene separately. The Western Australian PaxC sequences
contained two indels (8bp and 43bp), which were coded as single base changes as
suggested by Simmons & Ochoterena (2000). The model of best fit for PaxC was the
Tamura 3 parameter model with Gamma distribution, and the alignment was trimmed to
438 bp. The model of best fit for the CR was the HKY model, and the alignment was
trimmed to 1,094 bp. In a preliminary analysis, 27 species of Acropora were included,
however, the results showed that many of these species provided little context for the
evolutionary history of the spawning groups, so the final analysis included species only
from the A. humilis group (to which A. samoensis belongs; Wallace 1999) and species
that were informative in the preliminary analysis (i.e. shown to be closely related to A.
samoensis). A. tenuis and A. intermedia were used as outgroups.
To assess whether selection has affected the loci examined, Tajima’s D
(1989) and Fu & Li’s F* test (Fu & Li 1993) of neutrality were performed in DnaSP
(Librado & Rozas 2009). These tests assume no recombination within loci, so each
locus was examined for recombination using IMgc (Woerner et al. 2007), and where
recombination was detected (in PaxC and Calmodulin), new files were generated with
the largest non-recombining block of DNA sequence which was used to conduct the
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neutrality tests. Additionally, because the PaxC intron was suspected of being under
selection, in a subset of samples (n=10) a portion of the coding region of the PaxC gene
was sequenced (GenBank Accession Numbers KT582778-KT582782; methods
described in Appendix 3.3) in order to calculate the ratio of nonsynonymous
substitutions (dN) to synonymous substitutions (dS).
Combined analyses
To determine whether the genetic variation shared between the spring and autumn
spawners is simply a remnant of variation in the common ancestor or if it is due to
ongoing gene exchange, Isolation with Migration (IM) coalescent analyses were
conducted in IMa2 (Hey & Nielsen 2007). IMa2 estimates a posterior probability
distribution for multiple demographic parameters, including divergence time, migration
rate, and effective population sizes of two current populations and an ancestral
population. The parameters are scaled by the neutral mutation rate and converted to a
population migration rate, and time since divergence. The program was run with a total
of 9 loci; 7 microsatellites (WGS112, EST016, EST254, EST063, EST097, EST181,
EST196) were run under the SMM, the mitochondrial control region was run under the
HKY model, and Calmodulin was run under the infinite sites model (using the largest
non-recombining block of DNA sequence generated in IMgc as above). PaxC was
excluded from the analysis because it potentially violates the model assumption of
selective neutrality. Several preliminary runs were conducted to optimize model settings
and prior parameter distributions, to assess mixing and adjust burn-in periods. Once all
settings had been optimized, the final M-mode runs consisted of 80 chains with a
geometric heating scheme of ha = 0.99 and hb = 0.75, priors q = 9.95, t = 5.98, m= 2.01,
using the J2 model and a generation time of 5 years. Mutation rates for the CR and
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Calmodulin were estimated following the rates published (% per million years) in Chen
et al. (2009). Final simulations were run for six million steps following a burn-in of 1.7
million steps. Each M-mode analysis was repeated three times using different random
seed numbers to assure convergence.
Results
Spawning seasonality
The combination of in situ observations and dissections of tagged colonies of Acropora
samoensis identified distinct spring and autumn reproductive cohorts. Reproductive
patterns were consistent in each colony (autumn or spring spawner) over the two years
of this study. In situ observations in November 2010 showed that one cohort had
mature, pigmented eggs (indicating spawning was imminent), while at the same time the
other cohort had no visible eggs. Dissections showed that one cohort contained large,
mature eggs and testes (500-529 µm, Table 3.1), while the other cohort had small,
immature eggs (Table 3.1), indicating spawning was not imminent in the second cohort.
In mid-January 2011, one group had no visible eggs and the other had white eggs.
Dissections confirmed that all colonies that had contained large eggs in November 2010
no longer had eggs, indicating that spawning had indeed occurred in this cohort. In the
other cohort, eggs were of medium size (200-314 µm, Table 3.1), and testes were
present, indicating that spawning was likely to occur within 4-8 weeks of sampling (i.e.
around February/March 2011). One colony (LOW_26) that had small, immature eggs in
November 2010 (suggesting it was an autumn spawner) did not appear to have eggs in
January 2011, and was therefore unlikely to have spawned in February/March 2011.
Nevertheless, this colony had small, immature eggs again the following year in October
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confirming it was not a spring spawner (Table 3.1). Consistent with this assessment,
genotypic assignment from the microsatellites and PaxC placed it in the autumn
spawning cohort (see below). Consistency between the spring samples in 2010 and 2011
applied to all individuals, whereby the same individuals either had large eggs and testes
in both years (spring spawners) or in neither year (autumn spawners).
Microsatellite analyses
Microchecker did not detect large allele dropout or stuttering in the microsatellite data
set, but found null alleles at six loci (063, 112, 245, 254, 098, 196). FreeNa indicated
the null alleles had a negligible effect on the dataset (corrected FST = 0.186 95%, CI
0.076-0.326, uncorrected FST = 0.181, 95% CI 0.066-0.324). No pairs of loci were
found to be in linkage disequilibrium in either spring or autumn groups following
Bonferroni correction. The spring-spawning group was in HWE but the autumn-
spawning group was not. Significant departures from Hardy-Weinberg equilibrium were
not detected at any loci in the spring-spawning group, but deficits of heterozygotes were
detected at three loci in the autumn-spawners (WGS112, EST098, EST196; Table 3.2).
A Wahlund effect might explain the heterozygote deficits in the autumn-spawning
group, however, there was no population structure detected in the autumn-spawning
group in the STRUCTURE analysis (see below). Two individuals had the same
genotype, indicating they could be clonemates, so one was removed from the final
dataset. Allele frequencies are provided in Appendix 3.4.
Based on all 13 loci, significant genetic differentiation was evident between the
spring and autumn-spawning groups (FST = 0.17, p < 0.0001; F’ST = 0.30, p < 0.0001;
ΦST = 0.21, p < 0.001). The PCoA illustrated two genetic clusters that correspond to the
spring and autumn-spawning groups (Fig 3.1). The bar plots in STRUCTURE mirrored
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this result showing two genetic clusters (Fig. 3.2), and clear peaks in the plot of ∆K and
LnP(K) indicated that the optimal number of genetic clusters was two (K=2). In the
subsequent Evanno et al. (2005) simulation searching for substructure within the
autumn cluster, no further substructure was found. The initial decline in log probability
estimates for K indicated that the probable number of clusters was one, and when K > 1
the proportion of individuals assigned to each cluster was fairly even as expected when
the population structure is not real (Pritchard et al. 2010).
Posterior probabilities for assignment tests in STRUCTURE showed that all
individuals were correctly assigned to their reproductive cohort as determined from the
reproductive assessments; for example there were no individuals that were classified as
a ‘spring’ spawner by the reproductive assessments but an ‘autumn’ spawner by the
genotypic data (or vice versa). Permutation tests showed that autumn- and spring-
spawning groups were significantly different at six of the 13 loci (Table 3.2). Private
alleles were found in both the spring and autumn groups, but were more common in the
autumn-spawning group (31, compared with 4 in the spring spawners), which had a
larger sample. Some “private” alleles were simply uncommon, with 7 occurring as
singletons (Table 3.2). Strikingly, at the EST196 locus 11 out of 18 alleles were unique
to the autumn-spawning group (Table 3.2; Appendix 3.4). Despite these differences, the
two spawning groups shared the same most-common allele at all but four loci (EST016,
EST196, EST234, WGS112).
Geneological analyses
The three DNA sequence markers showed varying levels of differentiation between the
reproductive cohorts. Sequences of the PaxC intron (657 bp) from 38 individuals
revealed 23 alleles, and showed two divergent lineages/clades with no alleles shared
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between the autumn- and spring-spawning cohorts (Fig. 3.3a). The two clades were
distinguished by 6 fixed differences and two informative indels (151 and 43 bp), and the
level of p-distance between the PaxC clades was 0.017 ± 0.005 SE (Table 3.3). This
distinctness represented ΦST of 0.97 (p = 0.0001; Table 3.4), indicating that almost all of
the variation occurred between seasonal groups (Table 3.4).
A noteworthy exception detected in three of the cloned individuals was one
allele which had characteristics of both autumn and spring clades; of the 6 nucleotide
differences between the autumn and spring spawners this allele had 3 of those from the
spring clade, yet it had the same two indels as the autumn clade, and 12 unique
(different from autumn or spring) differences. All individuals that had this sequence
(n=3) were heterozygotes that also contained an allele from the autumn-spawning clade,
and all of these individuals were autumn spawners. Except for the three aforementioned
individuals, all other heterozygotes had alleles within clades.
Sequences of the Calmodulin intron (359 bp) from 34 individuals revealed 17
unique alleles, and AMOVA and the haplotype network showed that the reproductive
cohorts were genetically differentiated (ΦST = 0.31, p = 0.0001; Table 4; Fig. 3.3b). The
level of divergence between sequences was much lower than for PaxC (mean p distance
= 0.0062 ± 0.0026 SE; Table 3.3). Sequences of the mtDNA control region (1236 bp)
from 36 individuals revealed seven unique haplotypes, with no differentiation between
the autumn and spring spawners (ΦST = -0.01, p = 0.5; Table 3.4; Fig. 3.3c), and the
level of divergence between sequences was low (mean p distance = 0.0003 ± 0.0002 SE;
Table 3.3). Haplotype diversity was also low, with 72% of individuals carrying a single
haplotype (Fig. 3.3c).
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Table 3.1. Results of coral reproduction surveys (of sequenced samples) from in situ
assessments and dissections. +T = testes were present; dash (-) = fixation did not work;
n/a = colony was not sampled
Colony ID
Field
Assess
Nov.
2010
Oocyte
diameter
Nov. 2010
(µm)
Field
Assess
Jan.
2011
Oocyte
diameter
Jan 2011
(µm)
Field
Assess.
Oct
2011
Oocyte
diameter
Oct 2011
(µm)
Inferred
spawning
season
PaxC
clade
LOW_4 Pink 500 + T Absent Empty White 330 + T Spring A
LOW_6 Pink 524 + T Absent Empty White 392 + T Spring A
LOW_61 n/a n/a n/a n/a White 330 + T Spring A
LOW_67 n/a n/a n/a n/a White 346 + T Spring A LOW3_11 Pink 503 + T Absent Empty White 346 + T Spring A
LOW3_13 Pink - Absent Empty White 346 + T Spring A
LOW3_15 Pink 529 + T Absent Empty White 346 + T Spring A
LOW3_17 Pink 529 + T Absent Empty Pink 291 + T Spring A
LOW3_18 Pink 529 + T Absent Empty Pink 465 + T Spring A
LOW3_3 Pink 524 + T Absent Empty Pink 387 + T Spring A
LOW3_4 Pink 503 + T Absent Empty Pink 387 + T Spring A
LOW3_5 Pink 500 + T Absent Empty White 387 + T Spring A
LOW3_7 Pink 500 + T Absent Empty White 315 + T Spring A
LOW3_8 Pink 503 + T Absent Empty White 387 + T Spring A
LOW3_G42 n/a n/a n/a n/a White 387 + T Spring A
LOW_19 Absent 100 White 314 + T Absent 50 Autumn A/B HET LOW_3 Absent 84 White 310 + T Absent 50 Autumn A/B HET
LOW3_20 Absent 71 White 200 + T Absent 74 Autumn A/B HET
LOW_1 Absent 141 White 266 + T Absent 50 Autumn B
LOW_10 Absent 138 White 305 + T Absent 74 Autumn B
LOW_20 Absent 100 White 350 + T Absent 84 Autumn B
LOW_23 Absent 85 White 266 + T Absent Empty Autumn B
LOW_24 Absent 100 White 314 + T Absent Empty Autumn B
LOW_26 Absent 98 Absent Empty Absent 84 Autumn B
LOW_28 Absent 100 White 314 + T Absent 50 Autumn B
LOW_32 Absent 138 White - Absent 74 Autumn B
LOW_33 Absent 90 White 314 + T Absent 60 Autumn B LOW_62 n/a n/a n/a n/a Absent 74 Autumn B
LOW_66 n/a n/a n/a n/a Absent 74 Autumn B
LOW_68 n/a n/a n/a n/a Absent 50 Autumn B
LOW_9 Absent 100 White 309 + T Absent 74 Autumn B
LOW3_10 Absent 100 White 309 + T Absent 74 Autumn B
LOW3_14 Absent 132 White 309 + T Absent 50 Autumn B
LOW3_16 Absent 100 White 266 + T Absent 50 Autumn B
LOW3_19 Absent 141 White 300 + T Absent 74 Autumn B
LOW3_2 Absent 132 White 314 + T Absent 74 Autumn B
LOW3_6 Absent 100 White 266 + T Absent 50 Autumn B
LOW3_9 Absent 84 White 266 + T Absent 50 Autumn B
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Table 3.2. Measures of genetic diversity and variation in spring- and autumn-spawning
cohorts: number of alleles/haplotypes (n), number of private alleles (priv), expected
heterozygosity (HE), effective number of alleles/haplotypes (Ne) and FST (Weir and
Cockerham 1984). Private alleles in bold indicate that at least one had a frequency of >
5%. * = significant results of permutation tests (p < 0.0001); ŧ = significant deviations
from HWE (p < 0.01).
Spring Autumn FST
Locus n S priv. HE Ne n A priv. HE Ne
Microsatellites
EST016 4 0 0.61 2.56 6 2 0.58 2.39 *0.29
EST032 2 0 0.27 1.37 3 1 0.04 1.05 *0.28
EST063 3 1 0.21 1.28 5 3 0.41 1.72 0.03
EST097 3 0 0.53 2.14 4 1 0.63 2.70 *0.11
EST098 4 1 0.61 2.60 5 2 ŧ 0.66 2.92 -0.009
EST149 1 0 0.00 1.00 2 1 0.03 1.03 -0.15
EST181 4 0 0.72 3.62 6 2 0.46 1.86 *0.18
EST196 7 0 0.88 7.87 18 11 ŧ 0.92 12.20 0.02
EST245 3 1 0.16 1.19 4 2 0.23 1.39 -0.01
EST254 2 0 0.41 1.69 2 0 0.11 1.13 *0.65
WGS112 8 1 0.80 5.21 11 4 ŧ 0.70 3.30 *0.22
WGS153 2 0 0.08 1.08 2 0 0.04 1.05 -0.19
WGS211 1 0 0.00 1.00 3 2 0.20 1.26 0.04
PaxC 12 12 0.90 10.00 3 3 0.51 2.04 *0.30
Calmodulin 3 0 0.32 1.47 15 14 0.97 33.33 *0.26
mtDNA 4 0 0.65 2.86 5 0 0.35 1.54 0.06
Fig 3.1. Scores on the first two principal co-ordinates of pairwise genetic distance of
microsatellite loci between autumn and spring spawners. The percentage of variation
explained by the first three axes were 13%, 9% and 8% respectively.
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Fig 3.2. Bayesian population assignment from the software program STRUCTURE
when K=2. Cluster 1 = spring spawners, cluster 2 = autumn spawners. Each column
represents a single individual, and the proportions of each individual’s genome that
come from each cluster are shown by different colours.
Fig 3.3. Haplotype networks estimated for (a) PaxC (b) Calmodulin and (c) mtDNA
control region in NETWORK using the median joining algorithm. Size of circles are
proportional to number of individuals in that haplotype. Links represent character
differences, and small colourless ovals are ancestral sequences required to connect
existing sequences. Dark shading = autumn spawners, light shading = spring spawners.
Spring Autumn
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Table 3.3. Average pairwise sequence distances (± SE) within and between spawning
groups in mtDNA CR, PaxC and Calmodulin, and Tajima’s D and Fu & Li’s F*
neutrality test values; significant values are indicated by * (p < 0.05).
bp Within spring Within autumn Between groups Tajima’s D Fu & Li’s F*
mtDNA 1236 0.0002 ± 0.0002 0.0003 ± 0.0002 0.0003 ± 0.0002 -1.25 -0.86
PaxC 515 0.0047 ± 0.0017 0.0018 ± 0.0010 0.1268 ± 0.0197 2.32* 1.81*
Calmodulin 360 0.0023 ± 0.0014 0.0067 ± 0.0025 0.0062 ± 0.0026 -0.87 -0.27
Table 3.4. Analysis of Molecular Variance (AMOVA) from distance-based pairwise
differences for all loci.
Skeletal examination of the reproductive cohorts revealed that the spring-
spawning cohort had flared corallites more similar in appearance to Acropora digitifera
than A. samoensis, raising the question of whether the spring-spawners were actually A.
digitifera. Phylogenetic analysis of the PaxC intron among the A. humilis group
indicates that the spring spawners are indeed closely related A. digitifera (Fig. 3.4a),
however, phylogenetic analysis of the Control Region, suggests that the spring-
spawning group is not A. digitifera (Fig. 3.4b). This also agrees with observed
Source of variation DF SS Variance
component
% variation ΦST p
Microsats
Among populations 1 23.1 0.47 21.1 0.21 0.0001
Within populations 154 266.5 1.73 78.8
Total 155 289.6 2.20
PaxC
Among populations 1 590.7 29.8 96.7 0.97 0.0001
Within populations 38 38.2 1.0 3.2
Total 39 629.0 30.8
Calmodulin
Among populations 1 7.8 0.44 31.1 0.31 0.0001
Within populations 32 31.2 0.98 68.9
Total 33 39.0 1.42
mtDNA
Among populations 1 0.47 -0.01 -1.35 -0.01 0.50
Within populations 34 20.8 0.60 101.3
Total 35 21.3
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differences in colony morphology and habitat preference between the spring-spawners
and A. digitifera (author’s observations).
The evolutionary history of the spring- and autumn-spawning groups was
somewhat difficult to interpret, due to the discordance between the PaxC and CR
phylogenetic trees. Phylogenetic analysis of PaxC showed that the autumn spawners of
A. samoensis formed a clade with A. florida while the spring spawners were more
closely related to A. digitifera (Fig. 3.4a). Phylogenetic analysis of the CR showed that
the autumn and spring spawners combined formed a clade with A. aspera, A. florida, A.
sarmentosa, A. humilis and A. gemmifera, and a subclade with A. florida, while the
other two members of the A. humilis group, A. digitifera and A. monticulosa, were
distinct from the other species (Fig. 3.4b).
A subset of individuals was sequenced for the PaxC protein coding region,
and the primers amplified an 882 bp sequence that included a 429 bp-segment of the
coding region (including part of the ‘paired domain’) and a 453 bp-segment of an
adjacent intron. The 429 bp segment of the coding region was highly conserved and did
not differ between the spawning groups, nor did it differ from the published sequence of
another species (Acropora millepora Accession number AF053459). However the
adjacent intron (a different intron to the previously mentioned PaxC intron) mirrored the
other PaxC intron results, revealing two clades with mean p-distance = 0.08 which were
separated by 35 point mutations and three indels.
Tests for selection
Tajima’s D and Fu & Li’s F* tests of neutrality were not significant for Calmodulin or
the CR but were significant for PaxC (Table 3.3). The positive values of the PaxC test
(Table 3.3) suggest PaxC has been the target of positive selection. In addition, in the
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second intron sequenced (adjacent to the paired domain, described above) Tajima’s D
and Fu & Li’s F* neutrality tests were also significant (2.13 and 1.68 respectively, p <
0.05).
Fig 3.4. Acropora species maximum-likelihood phylogenetic trees for (a) PaxC intron
and (b) mtDNA CR. Codes after species names are GenBank Accession numbers. A.
samoensis spring and autumn spawners from this study are shown in bold. Bootstrap
values from 1000 replicates are shown above branches.
Hybridization and time since divergence
The STRUCTURE analyses for the identification of hybrid individuals found that the
highest probability of any individual having a parent from the other spawning group
was 0.13, and the highest probability of an individual having a grandparent from the
other spawning group was 0.18.
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The IMa2 analyses confirmed that shared polymorphism of the
microsatellites, CR and Calmodulin between the spring and autumn spawners was due
to gene flow between the groups and not due to ancestral polymorphism, as the
goodness of fit test in IMa2 rejected the null model of zero migration (p < 0.001).
Repeat runs of the IMa2 program revealed marginal posterior probability distribution
curves of migration rates with a peak at 0.8 (2Nm = 0.6 migrant gene copies per
generation).
While the coalescent analyses in IMa2 consistently revealed a migration rate
of 0.8, the analyses were unable to provide a reliable estimate of divergence time. This
could be due to either high migration rates obscuring divergence time, or lack of
information in the data (J. Hey pers. comm.), and since migration estimates are low, it is
assumed that the small sample size of the spring population (n=13) accounts for this
lack of resolution (small sample sizes will affect some parameters and not others, so
migration rate estimates should still be valid; J Hey pers. comm.).
Discussion
Cryptic speciation
Reproductive assessments confirmed the existence of two reproductive cohorts in the
population of Acropora samoensis, one that spawned in autumn and one that spawned
in spring. The combined results from the analyses of microsatellites, PaxC and
Calmodulin indicate that the two cohorts are highly genetically differentiated. The
STRUCTURE analyses and PCoA separated the spawning groups into two clearly
defined genetic clusters, and the AMOVA showed significant and substantial genetic
differentiation between the groups in three of four markers. Most remarkably of all,
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PaxC showed two genetic lineages associated with spawning time that were separated
by an evolutionary distance of 1.7%. Following reproductive and genetic identification
of the colonies, small differences in radial corallite shape between spring- and autumn-
spawners were observed in skeletal samples. Hence, the combined evidence of genetic,
morphological and reproductive differences between the autumn and spring spawners of
Acropora samoensis observed in this study indicates that these reproductive cohorts
represent two cryptic species (cryptic species defined as two or more species that have
been classified as a single nominal species because they are at least superficially
morphologically indistinguishable; Bickford et al. 2007).
An explosion of cryptic species discoveries in scleractinian corals indicates
cryptic speciation is widespread across many genera (Knowlton et al. 1992; Flot et al.
2011; Ladner & Palumbi 2012; Schmidt-Roach et al. 2014; Warner et al. 2015), and
here is another example of the molecular identification of cryptic species. In this case
cryptic species are associated with asynchronous reproduction, and this may well be a
common mechanism in other cases (e.g. Ladner & Palumbi 2012), and should be
considered more widely in studies of population genetic structure in corals.
Asynchronous reproduction in corals is increasingly recognized, and biannual spawning
is widespread across Western Australia (Rosser 2013), Indonesia (Baird et al. 2009a),
the Great Barrier Reef (Stobart 1994; Wolstenholme 2004) and some areas of the south
Pacific (Mildner 1991), and it is crucial that possible cryptic species within populations
are considered in experimental design, analysis and interpretation of gene flow across
these regions (Warner et al. 2015).
Although the mitochondrial CR did not show any differentiation between
the autumn and spring spawners, this is not unusual, because mitochondrial
differentiation is often absent between both recognized and cryptic coral species
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(Wolstenholme 2004; Flot et al. 2008; Ladner & Palumbi 2012), mainly due to the
slowly evolving mitochondrial genome in corals (Shearer et al. 2002; Hellberg 2006)
and introgression between species (van Oppen et al. 2001; Vollmer & Palumbi 2002). A
previous phylogenetic study of the Acropora humilis group based on molecular,
morphometric and reproductive evidence showed that reproductive timing provided the
greatest level of taxonomic resolution between species in this group (Wolstenholme
2003).
In the cryptic species reported here, reproductive isolation between autumn
and spring spawners has not been absolute, as indicated by low levels of introgression
detected by the IMa2 analysis. Whether this is ongoing is unclear, as the STRUCTURE
analysis did not convincingly detect any first- or second-generation hybrids, although
this might simply be an artefact of small sample size. The reproductive assessments of
A. samoensis in this study and previously (Rosser & Gilmour 2008) found that
spawning season was consistent over a number of years, yet for introgression to occur
the timing of reproduction must occasionally be switched in a colony to allow gene flow
between the seasonal reproductive cohorts. The high degree of genetic differentiation
between the spawning groups and low level of introgression detected here suggests that
reproductive times are largely heritable, but are occasionally influenced by
environmental or physiological effects.
Evolutionary history
Interpreting the history of the spring- and autumn-spawners in A. samoensis
is complicated by the discordance in the species trees between PaxC and the CR,
indicating either a very old or a recent origin of the cryptic species, or introgression . A
rudimentary estimate of the divergence time between the two highly divergent lineages
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in the PaxC intron (based on the divergence rate of the PaxC intron in Acropora of
0.138-0.550 % per Ma; Chen et al. 2009) is between 32 and 8 mya. The Acropora
humilis group is an old species group with the earliest recorded fossils from the Eocene,
around 41 mya (Wallace & Rosen 2006). Such an ancient speciation is inconsistent with
the low levels of sequence divergence in Calmodulin and the CR, and the many shared
alleles among the microsatellites. Thus, the most parsimonious interpretation of the
divergent PaxC lineages is that, rather than indicating ancient cryptic species, the PaxC
lineages date to an old, ancestral polymorphism that has been retained in the evolution
of A. samoensis. In addition, the occurrence of introgression at some point in the past,
perhaps during the initial contact of two allopatrically diverged populations, could also
explain the homogeneity in the mitochondrial CR and the discordance between the CR
and PaxC gene trees.
In the PaxC intron the average level of sequence divergence between the
autumn- and spring-spawning groups (1.7%) is over three times the level of divergence
in Calmodulin (0.5%) and the CR (0.03%). The retention of these divergent PaxC
clades in the lineage of A. samoensis suggests selection on this gene, which is supported
by the results of the neutrality tests. In general, regions of the genome that are under
selection must be of functional importance (Nielson 2005), and the function of Pax
genes suggest possible mechanisms of selection on this gene in corals. The PaxC gene
in cnidarians is a precursor of Pax6 (Miller et al. 2000; de Jong 2005), which plays a
central role in eye development in vertebrates and Drosophila (Hill et al. 1991; Quiring
et al. 1994). While not all cnidarians have eyes, all sense light, and gametogenesis and
spawning are cued by seasonal, lunar and daily changes in light intensity and spectral
quality (Hunter 1988; Reitzel et al. 2013). Thus, it may well be that the PaxC gene
plays a role in spawning time (alongside other photoreceptor genes e.g. Shoguchi et al.
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2013). Sequencing of a small part of the protein (31%) showed that it was highly
conserved among the spawning groups and among other species, which may indicate
that divergent selection is acting upon aspects of the regulation of PaxC, rather than the
protein itself. Genomic regions that contain quantitative trait loci quickly diverge under
selection and become resistant to gene exchange (Turner et al. 2005; Via 2009), which
would explain the much higher level of divergence in PaxC compared to Calmodulin.
Further analysis of other genes (e.g. cryptochromes, which are thought to mediate coral
spawning; Levy et al. 2007) may find a set of linked loci diverged among spring and
autumn spawners that are under joint selection. Although speculative, this possibility
indicates that PaxC is a very interesting gene in cnidarians, and the potential for it to
influence spawning in corals is an exciting avenue for future research.
Divergence between the PaxC clades could have begun in sympatry or
allopatry during the Miocene. Allopatric divergence among populations is the most
common explanation of the origin of large differences in breeding time (Coyne & Orr
2004), but allochronic divergence (a mode of sympatric divergence resulting from a
phenological shift; Alexander & Bigelow 1960) is considered responsible for genetic
diversification in other broadcast-spawning marine invertebrates (Bird et al. 2011).
Regardless of whether divergence began in sympatry or allopatry, it appears that
divergent selection has resulted in genetically-based differences in spawning season.
This study highlights the importance of incorporating observations of reproductive
timing into studies of genetic structure in corals, because at both the population level
and the species level, differences in reproductive timing play a significant role in their
evolution.
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Chapter 4
Asynchronous spawning and demographic history shape
genetic differentiation among populations of the hard coral
Acropora tenuis in Western Australia
This chapter has been published in Molecular Phylogenetics and Evolution:
Rosser NL (2016) Demographic history and asynchronous spawning shape genetic
differentiation among populations of the hard coral Acropora tenuis in Western
Australia. Molecular Phylogenetics and Evolution 98, 89-96.
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Abstract
Reproductive isolation can facilitate genetic subdivision between populations, and a
significant facilitator of isolation is reproductive asynchrony, such as seen in broadcast-
spawning corals. However, the factors that shape genetic variation in marine systems
are complex and ambiguous, and ecological genetic structure may be influenced by the
overriding signature of past demographic events. Here, the relative roles of the timing of
reproduction and historical geography on the partitioning of genetic variation were
examined in seven populations of the broadcast-spawning coral Acropora tenuis over
12° of latitude. The analysis of multiple loci (mitochondrial control region, two nuclear
introns and six microsatellites) revealed significant genetic division between the most
northern reef and all other reefs, suggesting that WA reefs were re-colonized from two
different sources after the Pleistocene glaciation. Accompanying this pattern was
significant genetic differentiation associated with different breeding seasons (spring and
autumn), which was greatest in the PaxC intron, in which there were two divergent
lineages (ΦST = 0.98). This is the second study to find divergent clades of PaxC
associated with spring and autumn spawners, strengthening the suggestion of some
selective connection to timing of reproduction in corals. This study reiterates the need to
incorporate reproductive timing into population genetic studies of corals because it
facilitates genetic differentiation; however, careful analysis of population genetic data is
required to separate ecological and evolutionary processes.
Introduction
A fundamental component of population genetics is the level of connectivity
among populations of a species, because local adaptation and eventually speciation
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depend upon patterns of gene flow. In populations of broadcast-spawners a potential
facilitator of genetic subdivision is asynchronous reproduction, as opportunities for
fertilization and gene exchange are extremely low between individuals that spawn more
than several hours apart (Levitan et al. 2011), so differenes in reproductive timing can
enforce assortative mating and genetic divergence (Knowlton et al. 1997; Dai et al.
2000; Fukami et al. 2003; Hendry & Day 2005; Bird et al. 2011; Binks et al. 2012). In
contrast to most other regions, north-western Australian and Indonesian reefs have two
major coral spawning events each year, one in autumn and one in spring (Rosser &
Gilmour 2008; Permata et al. 2012). A recent study of sympatric populations of
autumn- and spring-spawning cohorts of Acropora samoensis in Western Australia
showed that the reproductive cohorts were genetically differentiated on an ecological
scale (F’ST = 0.3), and had divergent lineages of the phylogenetic marker PaxC which
were not present in other DNA sequences markers, raising the possibility that PaxC may
be under selection (Rosser 2015).
The factors that shape genetic variation in marine systems, however, are
complex and ambiguous, and one difficulty in interpreting patterns of genetic
subdivision is that ecological patterns may be confounded by the overriding signature of
past demographic events, so it is necessary to separate population history from
population structure. This is particularly relevant in the marine realm, where high larval
dispersal has the potential to connect populations over large distances (Palumbi 1992;
Ayre & Hughes 2000; Johnson & Black 2006a), and geographic distance alone can be a
poor predictor of genetic structure (Johnson & Black 2006b).
Climatic oscillations in glacial and non-glacial cycles cause expansions and
retractions of species’ ranges, involving local extinction, migration, drift and adaptation,
and these processes leave different genetic signatures on populations (Hewitt 1996). The
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coral reefs of Western Australia have a long history of expansion and contraction of
their geographic range in response to oscillating glacial cycles. The north-west coast is
characterized by a series of discontinuous reefs, which occur on the continental slope,
on the continental shelf edge, and along the coastline. The offshore reef systems consist
of atoll-like reefs on the continental slope that rise from deep-ramp settings (e.g. Scott
Reef and the Rowley Shoals), as well as shallower reefs perched on the edge of the
continental shelf (e.g. Ashmore Reef), while the inshore/coastal reefs occur along the
modern-day coastline and around inshore islands. At the height of the Last Glacial
Maximum ~ 18,000 years ago when sea level was -120 m, the present-day coastal reefs,
including the Kimberley coast, the Montebello Islands, Dampier and Ningaloo Reef,
were on dry land (Yokoyama et al. 2001). These coastal reefs were recolonized in the
Holocene transgression, but where they were recolonized from is uncertain; while the
offshore atolls of Scott Reef and the Rowley Shoals would have existed during the
LGM, whether refuge coral populations survived lower temperatures and contracted
habitats during the LGM has been widely debated (Wilson 2013).
To investigate the roles of historical geography and the timing of reproduction
on the partitioning of genetic variation, this study examines phylogeographic and
population genetic variation over a large geographic range in the scleractinian coral
Acropora tenuis, which reproduces in spring and autumn in Western Australia.
Methods
Study sites and spawning patterns of A. tenuis
Genetic samples of Acropora tenuis were collected from seven locations spanning
1600km and 12° of latitude, from Ashmore Reef to Ningaloo Reef, over which there is a
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latitudinal gradient of spawning time. Spawning is in spring at the most northern
location of Ashmore Reef (Rosser 2013), in autumn at the most southern locations of
Dampier and Ningaloo Reef (Baird et al. 2011; Rosser 2013), and in both spring and
autumn at the geographically intermediate Scott Reef (Gilmour et al. 2009). Tissue
samples were collected by snapping off small branches (1-3 cm) from individual
colonies, which were sampled at least 5m apart to reduce the likelihood of collecting
clonemates. Samples were preserved in 95% ethanol, which was replaced after 24 hours
and again after one week. Sample sizes at each location ranged from 3 to 49 (Table 4.1).
Colonies of A. tenuis at Ashmore Reef were visibly different from other locations and
difficult to identify, so skeletal samples from each colony were sent to the Museum of
Tropical Queensland for species verification.
DNA extraction, PCR and DNA sequencing
DNA sequencing of a mitochondrial gene and two nuclear genes was used to explore
phylogeographic patterns. DNA was extracted from branch tips using DNeasy DNA
extraction kits for animal tissue (Qiagen, USA) according to the manufacturer’s
instructions. The mtDNA control region, the nuclear PaxC intron and a microsatellite
flanking region were amplified in polymerase chain reactions (PCR). The mtDNA
control region was amplified using primers rns (5’-GGTTTCTAATACCTCCGAGG-
3’) and Cox3 (5’- TACATAACACTGCCCACAGT-3’) after van Oppen et al. (2001).
The PaxC intron was amplified using the primers PaxC_intron-FP1 (5’-
TCCAGAGCAGTTAGAGATGCTGG-3’) and PaxC_intron-RP1 (5’-
GGCGATTTGAGAACCAAACCTGTA-3’) after van Oppen et al. (2000).
Microsatellite flanking regions have been shown to be phylogenetically informative
(Chatrou et al. 2009), so primers were developed for the flanking region of one of the
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microsatellites used in this study, Amil02_018 (sequence downloaded from Genbank
Accession No.: EF989161.1). The software OLIGO was used to develop the primers
FL-FP (5’-GGAAAGCCCTCCTTAGGTGAT-3’) and FL-RP (5’-
CAAATTGAAGGCAAATGTCGG-3’). PCR reactions contained 1µL MgCl2 (50nM),
1.2 µL dNTPs (2.5nM), 0.2 µL platinum Taq, 2.5 µL 10 x PCR buffer, 1 µL each of the
forward and reverse primers, 2 µL of DNA, and 17.1 µL dH2O in a 25 µL reaction.
Positive (known DNA sample) and negative (no DNA) controls were included with
each reaction. Thermocycling profiles consisted of an initial denaturation step of 95°C
for 3 min, followed by 35 cycles of 94°C for 30 sec, 50°C for 1 min and 72°C for 1 min
and a final cycle of 72°C for 10 min. Products were sequenced in both directions at the
Australian Genome Research Facility in Perth. Individuals heterozygous for a single
nucleotide polymorphism (SNP) were resolved by comparing forward and reverse
sequences at variable sites. Individuals with multiple SNPs, or individuals that were
heterozygous for two informative indels in the PaxC gene, were resolved via cloning
using TOPO-TA cloning kits (Invitrogen, USA) and standard cloning procedures.
Sequences were edited manually in Sequencher v 4.5 (Gene Codes Corp., Ann Arbor,
MI, USA) and aligned using ClustalW in MEGA version 5 (Tamura et al. 2011).
Microsatellite genotyping
New samples from Ashmore Reef and the Montebello Islands were combined with a
subset of published microsatellite data (Underwood 2009a; Underwood 2009b) to test
for population genetic structure. One population from each of the four geographic
locations in Underwood’s dataset (Scott Reef, Rowley Shoals, Dampier and Ningaloo)
was randomly selected from his complete dataset for inclusion in this study (Table 4.1).
To standardize the microsatellite data from Ashmore Reef and the Montebello Islands
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with Underwood’s (2009a) data, 10 tissue samples that were amplified and scored by
Underwood (2009a) were also amplified and scored in the present study (using
GeneMarker v 1.9; SoftGenetics LLC), to provide direct calibration. Underwood
(2009b) amplified seven loci, all of which were repeated in this study (Appendix 4.1),
but one locus (Amil2_006) could not be successfully calibrated so was excluded from
this study.
Table 4.1. Location information and number of individuals used in analyses of the
Control Region, PaxC and microsatellites. * denotes microsatellite data used from
Underwood (2009a,b). Location CR PaxC Flank Msats
Ashmore Reef 12 11 10 40
Scott Reef 10 9 9 *47
Rowley Shoals 8 11 9 *49
Kimberley 7 4 4 -
Dampier 10 10 9 *42
Montebello Is 12 11 8 27
Ningaloo 11 9 10 *48
Total 66 65 58 253
Microsatellites from Ashmore Reef and the Montebellos were amplified in 13 µL
containing 11µL of Platinum PCR mix (Invitrogen: 22 U/mL complexed recombinant
Taq DNA polymerase with Platinum Taq antibody, 22 mM Tris-HCl, 55 mM KCl, i.65
mM MgCl2, 220 µm dGTP, 220 µm dATP, 220 µm dTTP , 220 µm dCTP), 1 µL DNA
and 0.25 – 1 µL of 3.3 µmol florescent-tagged forward primer for each locus. PCR
amplifications consisted of an initial denaturation step at 94 °C for 2 min followed by
30 cycles of 45 s at 94°C, 45 s at annealing temperature (50°C), 45 s at 72°C and finally
72°C for 5 min. Each individual was genotyped for six microsatellite loci. Any
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individuals that failed to amplify at ≥5 loci were excluded from analyses (3% of
individuals).
Phylogeographic analyses of DNA sequences
Maximum parsimony haplotype networks were created in NETWORK v 4.6.1.0.
(Fluxus Technology Ltd) using the median-joining algorithm. Each locus was examined
for recombination using IMgc (Woerner et al. 2007), and where recombination was
detected (in the microsatellite flanking region sequences, but not PaxC), new files were
generated with the largest non-recombining block of DNA sequence, which was used to
construct haplotype networks. In some individuals in which significant recombination
was detected, IMgc was unable to generate a non-recombining block, so these
individuals were excluded from all further analyses (n= 9, distributed across all sites).
Analysis of PaxC revealed two divergent clades (labeled A and B), and the
frequency of clade A appeared to be associated with latitude, and hence with spawning
season, so the Spearman rank correlation coefficient (rs) was used to test this
association. To decouple PaxC-associated variation from geography-associated
variation, a series of AMOVA tests was performed in Arlequin (Excoffier & Lischer
2010) to determine where the greatest level of genetic differentiation occurred. For this
analysis individuals were grouped according to: (a) the main groups identified in the
STRUCTURE analysis of the microsatellites (see below), which corresponded to
“Ashmore” and “non-Ashmore” populations (ΦRT), (b) PaxC clades A and B (ΦCT), and
(c) individual populations (ΦST). Evolutionary distances between populations and clades
were calculated in MEGA5 (Tamura et al. 2011) using the most appropriate model of
DNA evolution, as determined by the Bayesian Information Criterion. Diversity indices
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including gene diversity (HE), nucleotide diversity (π) and number of effective alleles
(Neff) were calculated for each population using Arlequin (Excoffier & Lischer 2010).
Population genetic analyses of microsatellites
Linkage disequilibrium and departures from Hardy-Weinberg Equilibrium were
tested within sites using FSTAT (with sequential Bonferroni correction applied; Rice
1989). The dataset was checked for individuals that had the same multi-locus genotypes,
and replicates were removed. The program FreeNa was used to estimate the frequency
of null alleles and to generate a dataset corrected for null alleles to determine pairwise
FST values (Weir & Cockerham 1984) across all loci and for each locus. The FreeNa
dataset yielded similar levels of pairwise FST and overall FST as the original dataset
(uncorrected FST = 0.117, 95% CI 0.05-0.21; corrected FST = 0.114, 95% CI 0.06-0.20),
so the original dataset was used for the remaining analyses. FST (Weir & Cockerham
1984) and standardized F’ST (Meirmans 2006) were calculated in GenAlEx 6.5 (Peakall
& Smouse 2012).
The Bayesian clustering method of Pritchard et al. (2000) was implemented in
the program STRUCTURE v2.3.3 to estimate the number of population clusters (K) in
the microsatellite data, using the simulation method described by Evanno et al. (2005).
For datasets with relatively few loci, the ‘LocPrior’ model uses the sampling locations,
and places a higher prior weight on clustering outcomes when they are correlated with
sampling locations. This helps to find subtle genetic structure, without tending to
uncover structure where none exists (Hubisz et al. 2009). This model was used here,
due to the low number of loci in the microsatellite dataset. Simulations were based on
the admixture model with correlated allele frequencies, and sampling included 500,000
repetitions following a burn-in of 200,000. Simulations were performed for K=1-10
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genotypic clusters, running five replicates for each scenario. Results were processed in
the program STRUCTURE HARVESTER (Earl & vonHoldt 2012) to determine the
most likely value of K. Results of the five STRUCTURE runs were merged with
CLUMPP (Jakobsson & Rosenberg 2007) and visualized with DISTRUCT (Rosenberg
2004). Estimates of K differed between plots of ∆K and mean L(K), suggesting
hierarchical population structure was present, so subsequent STRUCTURE analyses
were also conducted to test for substructure.
As with the DNA sequence analyses, a series of AMOVA tests was performed in
Arlequin (Excoffier & Lischer 2010) to determine where the greatest level of genetic
differentiation occurred, and individuals were grouped in the same way (see above). In
addition, a Principal Co-odinates Analysis (PCoA) was conducted in GenAlEx 6.5
(Peakall & Smouse 2012), separating individuals into their PaxC clades, to examine the
relationships visually. The correlation between log-transformed pairwise FST and log-
transformed geographic distance between populations was assessed using regression
analyses in Arlequin, to test for patterns of isolation by distance (IBD), with
significance assessed using a Mantel test (10,000 permutations). The same diversity
indices mentioned above were calculated in Arlequin, and a two-way t-test comparing
HE (Appendix 4.2) between Ashmore Reef, Scott Reef and Rowley Shoals vs
Montebello, Dampier and Ningaloo was conducted in MS Excel.
Results
Phylogeographic structure
Sequences of the PaxC intron (509 bp) showed the greatest amount of genetic
differentiation among the DNA sequence markers. Twelve unique haplotypes were
detected, which formed two distinct clades (Fig. 4.1). The two clades were distinguished
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by 15 fixed differences and two indels (4bp and 13bp), which co-segregated with the
fixed SNPs. The average evolutionary divergence between the clades using the Tamura
3-parameter model was 0.014 SE ± 0.005 (maximum between haplotypes = 0.019 SE ±
0.006), and this distinctness was represented by ΦCT of 0.98. While most individuals
were homozygous for either clade A or clade B, cloning revealed that six individuals
were heterozygous for both clades (9% of individuals sequenced). These heterozygotes
occurred at Ashmore Reef and the Kimberley.
The distribution of the PaxC clades varied with latitude (rs = 0.957, P < 0.01;
Fig. 4.2), paralleling variation of breeding seasons in A. tenuis. On Ashmore Reef,
where spawning occurs in spring, clade A predominated, while at Dampier and
Ningaloo, where spawning occurs in autumn, clade B predominated, while at Scott
Reef, where spawning occurs in both seasons, both clades occurred (Fig. 4.2).
Sequences of the mtDNA control region (1077 bp) revealed 20 unique haplotypes, and
the haplotype network had a ‘star-like’ form (Fig. 4.1). The central haplotype was most
common, and was found in every population, while the majority of the derived
haplotypes were singletons linked by just one mutational step. The average evolutionary
divergence among all sequence pairs using the Tamura-Nei model was 0.0018 ± 0.0005
SE (maximum between haplotypes = 0.0047 ± 0.0019 SE). Sequences of the
microsatellite flanking region (565 bp) revealed 15 unique haplotypes, also with no
large gaps in the haplotype network (Fig. 4.1). The average evolutionary divergence
among all sequence pairs using the Kimura 2-parameter model was 0.007 ± 0.0017
(maximum between haplotypes = 0.018 ± 0.005 SE).
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Fig 4.1. Haplotype networks for the mtDNA control region (CR), Flanking Region (FL) and PaxC
intron (PaxC). Each circle represents a different haplotype, and its relative size is proportional to its
frequency as indicated by circular diagram. Colours represent geographic locations. Small cross-
hatched lines represent the number of additional mutational changes.
Fig 4.2. Map of sampling locations, with pie-charts showing the frequencies of PaxC clades A
(white) and B (black) in each population of A. tenuis. The dotted line represents the 100m contour
and the approximate location of the Australian coastline during the LGM. The bar graph shows the
proportion of colonies (%) spawning in spring (grey) and autumn (black) in A. tenuis documented in
previous studies (Gilmour et al. 2009; Baird et al. 2011; Rosser 2013).
Ashmore Reef
Scott Reef
Rowley Shoals
Kimberley Coast
Montebello Is.
Dampier
Ningaloo Reef
1
5
10
40
CR
PaxC
FL
Clade A Clade B
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Demographic structure
All populations had deficits of heterozygotes at the microsatellite loci (Appendix
4.2), which were not accompanied by linkage disequilibrium among loci. FreeNa
indicated null alleles had a negligible effect on the dataset, suggesting that heterozygote
deficits are more likely to be due to admixture or inbreeding. FST values indicated
significant genetic structure in the microsatellites (FST = 0.11; F’ST = 0.23).
Plots of ∆K (Evanno et al. 2005) from STRUCTURE indicated that the most
likely number of genetic clusters (K) estimated from the microsatellite loci was two.
Subsequent STRUCTURE analyses of the ‘Ashmore’ group found no further
substructure, while in cluster analyses of the ‘other’ group the greatest ∆K occurred at K
= 4. The four genetic clusters identified in this ‘non-Ashmore’ sub-group were the same
clusters that occurred in the whole data set at K = 4 (Fig. 4.3). At K = 2 the greatest
level of genetic differentiation was between Ashmore Reef and all other localities (Fig.
4.3). Scott Reef showed a high level of admixture between the Ashmore cluster and the
‘other’ cluster. At K = 4 the genetic clusters were distributed between (i) Ashmore and
Scott Reefs (ii) Scott Reef and Rowley Shoals (iii) Ningaloo and the Montebello Is. and
(iv) Dampier (Fig. 4.3).
In line with the Bayesian clustering results, population pairwise F’ST values were
greatest between Ashmore and other populations (Table 4.2). A Mantel test found no
significant association between genetic and geographic distance in the microsatellite
loci (r2 = 0.09, P = 0.6), ruling out IBD over the entire region.
A series of AMOVAs was conducted to determine where the greatest level of
genetic variation occurred, to disentangle geographic genetic structure from potential
structure associated with spawning season. In both the microsatellites and the flanking
region sequences, genetic variation was highest between the STRUCTURE-identified
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groups (“Ashmore” and “non-Ashmore” populations), followed by the PaxC-clade
groups, and then by populations, although the difference was small for the
microsatellites (0.20 compared with 0.17; Table 4.3). As expected in PaxC, genetic
variation was higher between the clades than between STRUCTURE-identified groups
(Table 4.3). The control region was the least informative marker, and did not detect any
significant variation at any level (Table 4.3). A PCoA conducted on the microsatellite
loci also suggested that genetic variation was higher between the Ashmore and non-
Ashmore populations than between PaxC clades A and B, because the individuals with
Clade A from Scott Reef and Rowley Shoals did not cluster together with Ashmore
Reef, regardless of clade (Fig. 4.4).
Patterns of genetic diversity varied across markers, but generally, Ashmore
Reef, Scott Reef and Rowley Shoals had higher genetic diversity than the Montebello
Islands, Dampier and Ningaloo Reef (P = 0.01, 2 tailed t-test; Appendix 4.2).
Discussion
The combination of molecular markers used in this study provided insight into
patterns of genetic variation on both an ecological and an evolutionary scale, and
presented two key findings. First, all genetic markers (except CR, which was
uninformative) revealed a phylogeographic break between Ashmore Reef and all other
reefs. Second, there were two clades in the PaxC gene tree separated by an evolutionary
distance of 1.4% that are not present in the other genetic markers, and these clades are
geographically associated with timing of reproduction.
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Fig. 4.3. Results of Bayesian cluster analysis of A. tenuis populations in Western Australia as
identified by STRUCTURE showing K = 2 to K = 4 in descending order. Each individual is
shown as a vertical bar and indicates the relative membership proportion to each genetic cluster
(blue, yellow, pink and purple).
Fig. 4.4 Principal Co-ordinates Analysis (PCoA) of A. tenuis microsatellite loci.
Individuals are grouped into (i) PaxC clades (clade A = large circles; clade B = small
circles), and (ii) geographic region (Ashmore = white; non-Ashmore = black).
Heterozygous individuals for clades A and B are represented by large, grey circles.
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Table 4.2. Pairwise F ’ST values (Meirmans 2006) for microsatellite loci (below
diagonal) and geographic distance (km) (above diagonal, shaded). All F ’ST values were
significant (p < 0.01).
Ashmore Scott Rowley Montebello Dampier Ningaloo
Ashmore 0 219 683 1150 1163 1667
Scott 0.27 0 468 898 975 1300
Rowley 0.49 0.07 0 435 525 893
Montebello 0.43 0.10 0.09 0 137 474
Dampier 0.58 0.22 0.15 0.07 0 369
Ningaloo 0.40 0.10 0.09 0.07 0.16 0
Table 4.3. Summary of results from Analysis of Molecular Variance (AMOVA) when
variation was partitioned among groups (‘Ashmore’ and ‘non-Ashmore’ as per the
Bayesian cluster analysis), among PaxC clades, and among populations. Tests were
conducted in Arlequin on pairwise ΦST for the DNA sequence loci, and FST for the
microsatellites. The highest values for each marker are shown in bold. Only significant
results (P < 0.001) from 1000 permutations are presented (ns = non-significant).
Locus
Among groups
(Φ/FRT )
Among PaxC clades
(Φ/FCT )
Among populations
(Φ/FST )
mtDNA CR ns ns ns
PaxC 0.65 0.98 0.39
Flank 0.34 0.18 0.21
Microsats 0.20 0.17 0.12
Phylogeographic structure and reproductive timing
The Bayesian cluster analysis of the microsatellites, the pairwise FST values,
and the AMOVAs of the microsatellites and the flanking region all showed that the
greatest genetic split is between Ashmore Reef and all other populations, indicative of a
phylogeographic break in this region. This pattern is a likely result of allopatric
divergence in two glacial refugia during the Pleistocene, from which WA reefs were
subsequently re-colonized in the Holocene transgression. At the height of the last
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Pleistocene glaciation the wide continental shelf was exposed, the coastline was at the
edge of the Sahul shelf along the 130 m isobaths (Yokoyama et al. 2001), and the
present-day coastal reefs were on dry land (Fig 4.2). During this time there was possibly
a glacial refugium along the convoluted Sahul shelf, from which Ashmore reef may
have been re-colonized during the Holocene transgression, while the other possible
source was Scott Reef. The higher genetic diversity of Scott Reef compared to the
inshore populations suggests Scott Reef is an older population, in which more time has
elapsed for mutations to accumulate (Hewitt 1996; Grant & Bowen 1998).
Alternatively, if the Sahul shelf refugium was south of Ashmore Reef, it could have
recolonized Scott Reef and the other WA reefs, while Ashmore Reef was recolonized
from a northern source, most likely Indonesia.
Given the discontinuous, stepping-stone nature of coral reefs in Western
Australia, and the fact that most coral recruitment occurs over 10s to 100s of km (Treml
et al. 2008), a pattern of IBD might be expected. However, IBD was not detected, as
illustrated by the much stronger genetic divergence between Ashmore and Scott Reefs
(F ’ST = 0.27, 219 km) compared to Scott Reef and Ningaloo Reef (F ’ST = 0.10, 1300
km), despite the much larger geographic distance between the latter pair. This finding
further supports the view that WA reefs were re-colonized from two different sources in
the Holocene transgression.
Phylogeographic breaks can provide a starting point for speciation (Hewitt 2000;
Avise 2004), and several studies have revealed cryptic species among marine taxa that
have experienced population fragmentation during glacial periods (reviewed in Provan
& Bennett 2008). Differentiation between Ashmore Reef and the reefs south of Scott
Reef was substantial (F’ST of 0.40 to 0.58), and was accompanied by subtle differences
in colony morphology (author’s observations), suggesting they may even be cryptic
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species according to the ‘genotypic cluster’ definition of species (Mallet 1995). Cryptic
species are increasingly being discovered in scleractinian corals (reviewed in Warner et
al. 2015), and their detection is important for accurately estimating biodiversity and
population connectivity. If cryptic diversity is uncharacterized, multiple species may be
unwittingly pooled, which can result in the incorrect interpretation of population
connectivity, gene flow, dispersal, biodiversity and ecological patterns (Ladner &
Palumbi 2012; Warner et al. 2015).
Surprisingly, gene flow in contemporary populations has not homogenized the
population and erased this signature of colonization. A likely explanation is that
reproductive isolation maintains the genetic difference between Ashmore Reef and the
other WA reefs. Acropora tenuis spawns predominantly in spring at Ashmore Reef
(Rosser, 2013) and predominantly in autumn on other WA reefs (Gilmour et al., 2009;
Baird et al., 2011; Rosser, 2013). Because spawning time is largely heritable and
consistent amoung years (Vize et al. 2005; Levitan et al. 2011; Rosser 2015) spawning
in different seasons would inhibit gene flow between the populations and promote
genetic divergence (e.g. Dai et al. 2000; Wolstenholme 2004; Rosser 2015).
In both the microsatellites and flanking region sequences the AMOVA tests
indicated that genetic differentiation between Ashmore and non-Ashmore reefs was
greater than the differentiation between PaxC clades. However, the absence of
spawning data on specific individuals means I was unable to test the classification as a
‘spring’ or ‘autumn’ spawner based on PaxC clade, and some evidence suggests that it
is not absolute. A previous study found evidence of introgression between spring and
autumn-spawning cohorts of A. samoensis (Rosser 2015) implying that occasionally a
colony must switch spawning time to facilitate gene flow between the cohorts, so that a
spring-spawner harboring PaxC-clade A becomes an autumn-spawner harboring PaxC-
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clade A (or vice versa). Assignment tests in STRUCTURE based on microsatellites
suggested that three individuals that harbored PaxC clade A and were classified as
‘spring’ spawners may have been miss-classified; in other words while they harbored
PaxC clade A, they clustered more closely with the ‘autumn’ spawners. The
STRUCTURE plots suggest there are two cohorts at both Ashmore and Scott (blue and
red; Fig. 4.3) which are likely to correspond to spring spawners (blue) and autumn
spawners (red), but reproductive data are required to confirm this.
Divergent PaxC lineages
Phylogeographic analysis of PaxC revealed two divergent lineages with a
divergence distance of 1.4%, in comparison to the absence of distinctive phylogenetic
lineages and much less differentiation in the flanking region (average distance = 0.7%)
and CR (average distance = 0.2%). A genealogical discordant pattern such as this could
be due to a number of possibilities such as a recent selective sweep, differential
introgression, incomplete lineage sorting or selection. The distribution of the PaxC
clades in A. tenuis mirrors the geographic spawning patterns in this species, whereby on
reefs where spawning occurs in spring clade A predominates, on reefs where spawning
occurs in spring and autumn both clades occur, and on reefs where spawning occurs in
autumn clade B predominates (Fig 4.2). This observation, together with the same
finding of divergent PaxC clades associated with spring- and autumn-spawning cohorts
in Acropora samoensis (Rosser 2015), suggests an association between PaxC and
spawning seasonality and a role for natural selection. PaxC is an ancestor of Pax6
which plays a central role in eye specification in vertebrates (Catmull et al. 1998), and it
has been hypothesized that PaxC may function as a type of photoreceptor in corals that
cues spawning (Rosser 2015). However, the PaxC marker examined here is an intron,
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an untranslated, non-coding region of the genome, which raises the question of why an
intron would be associated with spawning seasonality and /or under divergent selection.
One explanation is that the PaxC intron (or gene) is simply a hitchhiker that is closely
linked to some other speciation gene that is contributing to reproductive isolation. A
second explanation is that the PaxC intron plays a functional role; introns have a variety
of functions such as transcription initiation, transcription elongation and transcription
termination (reviewed in Chorev & Carmel 2012). Some introns also have an important
role in increasing gene expression through intron-mediated enhancement (IME)
(Mascarenhas et al. 1990), a phenomenon that has been observed in plants, invertebrates
and vertebrates (reviewed in Rose et al. 2008). PaxC is a homeobox gene that encodes
transcription factors (Miller et al. 2000), and interestingly, IME has been observed in
another homeobox gene in Drosophila (Haerry & Gehring 1996). These ideas are
speculative, of course, and further studies of transcriptome sequencing, and gene
expression and regulation are required to test whether the PaxC coding region, or intron,
play a role in reproductive timing in Acropora.
Conclusions
The combination of molecular evidence presented here illustrates the complexity
of genetic structure that can result from a combination of factors such as selection,
asynchronous reproduction and demographic history. Here, genetic differentiation was
confounded by different spawning seasons and different demographic histories.
Unraveling intricate genetic structure from multiple sources is required in the analysis
and interpretation of population genetic structure in corals, because management
agencies rely on accurate estimates of population connectivity and genetic diversity for
adequate conservation planning. This study reiterates the need to incorporate
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observations of reproductive timing into population genetic studies of corals, because
biological, ecological and evolutionary processes all play significant roles in the genetic
structuring of coral populations.
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Chapter 5
Phylogenomics provides new insight into evolutionary
relationships and genealogical discordance in the reef-building
coral genus Acropora
This chapter is in review at Proceedings of the Royal Society B Biological Sciences:
Rosser NL, Thomas L, Stankowski S, Richards ZT, Kennington WJ, Johnson MS
(2016) Phylogenomics provides new insight into evolutionary relationships and
genealogical discordance in the reef-building coral genus Acropora
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Abstract
Understanding the genetic basis of reproductive isolation is a long-standing goal of
speciation research. In recently diverged populations, genealogical discordance may
reveal genes and genomic regions that contribute to the speciation process. Previous
work has shown that conspecific colonies of Acropora that spawn in different seasons
(spring and autumn) are associated with highly diverged lineages of the phylogenetic
marker PaxC. Here, we used 10,034 single nucleotide polymorphisms (SNPs) to
generate a genome-wide phylogeny and compared it to gene geneologies from the PaxC
intron and the mtDNA Control Region (CR) in 20 species of Acropora, including three
species with spring- and autumn-spawning cohorts. The PaxC phylogeny separated
conspecific autumn and spring spawners into different genetic clusters in all three
species; however this pattern was not supported in two of the three species at the
genome-level, suggesting a selective connection between PaxC and reproductive timing
in Acropora corals. This genome-wide phylogeny provides an improved foundation for
resolving phylogenetic relationships in Acropora, and combined with PaxC, provides a
fascinating platform for future research into regions of the genome that influence
reproductive isolation and speciation in corals.
Introduction
Molecular phylogenies are archival road maps of biodiversity, providing the
fundamental framework for interpreting evolutionary history and adaptation.
Accurately inferring evolutionary relationships, however, is made complicated by
discordant trees from different markers, as a result of gene duplication, incomplete
lineage sorting, selective sweeps, and introgression/hybridization (Maddison 1997;
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Hahn & Nakhleh 2015; Mallet et al. 2015). Conversely, genealogical incongruence can
also provide important insight into the regions of the genome that contribute to
adaptation, reproductive isolation and speciation (Fontaine et al. 2015; Lamichhaney et
al. 2015; Stankowski & Streisfield 2015). In the initial stages of speciation, regions of
the genome that generate reproductive isolation may diverge quickly, as they experience
reduced effective recombination compared to other regions (Wu 2001; Dopman et al.
2005; Via 2009). Thus, in recently diverged populations where reproductive isolation is
incomplete, genealogical discordance may reveal genes and genomic regions that
contribute to speciation.
An important trait affecting reproductive isolation in scleractinian corals is the
timing of reproduction (Knowlton et al. 1997; van Oppen et al. 2001; Fukami et al.
2003; Wolstenholme 2004; Nakajima et al. 2012). Timing of reproduction in broadcast
spawners is particularly important because gametes are viable for only a few hours, so
individuals that spawn more than a few hours apart are unlikely to cross-fertilize
(Levitan et al. 2011). In Western Australia there are two spawning seasons (spring and
autumn), and in some species there are two seasonal reproductive cohorts in the
population (Gilmour et al. 2016; Rosser 2015; Rosser & Gilmour 2008).
To date, phylogenetic studies of Acropora have focused on the mitochondrial
DNA Control Region (CR) and the nuclear PaxC intron (van Oppen et al. 2000; van
Oppen et al. 2001; Marquez et al. 2002; van Oppen et al. 2004; Vollmer & Palumbi
2007; Richards et al. 2013). Incongruence between these regions has been attributed
primarily to introgressive hybridization (van Oppen et al. 2001; Marquez et al. 2002;
van Oppen et al. 2004; Richards et al. 2008), but recent evidence suggests that PaxC is
under selection, and is associated with differences in spawning time (Rosser 2015,
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2016). Specifically, PaxC revealed two highly diverged lineages (ΦST = 0.98) for
different seasonal spawning cohorts in A. samoensis and A. tenuis.
Next generation sequencing technologies provide affordable, genome-wide
resolution to resolve phylogenetic discordance (e.g. Nadeau & Jiggins 2010; Rubin et
al. 2012; McCormack et al. 2013; Escudero et al. 2014; Rivers et al. 2016), avoiding
the earlier restriction to a few genes that could be reliably amplified with sufficient
variation to resolve phylogenetic relationships. Here, we construct the first genome-
wide phylogeny for the scleractinian coral genus Acropora using a genotyping by
sequencing (GBS) approach, and compare it to molecular phylogenies from the PaxC
46/47 intron and the mtDNA Control Region. Tests of congruence among these
phylogenies provide a clearer understanding of the evolution of PaxC and an improved
foundation for resolving phylogenetic relationships and patterns of speciation in corals.
Materials and Methods
Sample collection
Specimens of twenty species of Acropora were collected from a wide latitudinal
range in Western Australia (Fig 5.1). Three individuals were sampled for each of 17
species (Appendix 5.1; except A. stoddarti and A. gemmifera with n=2) for which
accompanying reproductive data were not collected. In the other three species,
reproductive data were collected in the field by examining the size and colour of
oocytes in broken branches and classifying the colonies as autumn or spring spawners
(following protocols in Rosser 2013, 2015). These included eight colonies of A.
millepora (4 autumn-spawners from Ningaloo Reef and 4 spring-spawners from
Ashmore Reef; two of each were included in the GBS dataset), and 16 colonies (eight
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spring-spawners and eight autumn-spawners) each of A. samoensis (from Rosser 2015)
and A. tenuis (from Rosser 2016). Voucher specimens were retained for most samples
and are housed at the Western Australian Museum (Appendix 5.1).
Fig 5.1. Geographical locations in Western Australia from where the 20 Acropora species were
collected (blue boxes) for this study.
Sequencing
DNA used for Sanger sequencing was extracted from branch tips using DNeasy
extraction kits for animal tissue (Qiagen, USA). Partial sequences of the mtDNA CR
and the PaxC 46/47 intron were amplified using the primers and protocols described in
(Rosser 2015). We attempted to include three replicates from each species, but some
samples could not be amplified across both genes (see Appendix 5.1). DNA fragments
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were sequenced in both directions at BGI Hong Kong, edited manually in Sequencher v
4.5 (Gene Codes Corp., Ann Arbor, MI, USA), and aligned using ClustalW and Muscle
in MEGA6 (Tamura et al. 2013). Heterozygotes were identified in the PaxC sequences,
and IUPAC nucleotide ambiguity codes were assigned to heterozygous bases.
Genome-wide single nucleotide polymorphism (SNP) data were generated at
Diversity Arrays Technology (DArT P/L http://www.diversityarrays.com). DArTseq™
is genotyping-by-sequencing technology which represents a combination of a DArT
complexity reduction methods and next generation sequencing platforms and is similar
to the widely applied RADseq methodology (see Appendix 5.2 for details of DArTseq
marker development). Briefly, genomic DNA was processed in digestion/ligation
reactions principally as per (Kilian et al. 2012) but replacing a single PstI-compatible
adaptor with two different adaptors corresponding to two different restriction enzyme
overhangs. Sequencing was carried out on a single lane of an Illumina Hiseq2500 and
processed using proprietary DArT analytical pipelines.
Data analysis
Phylogenetic relationships were estimated for PaxC and the CR using a
Bayesian statistical framework implemented in MrBayes 3.1.2 (Ronquist &
Huelsenbeck 2003), and Maximum Likelihood (ML) analyses in PhyML 3.0 (Guindon
et al. 2010) (detail in Appendix 5.2). Both genes contained numerous indels, which
were coded as single base changes, and trees were rooted with the sister genus Isopora
as an outgroup, using sequences of I. cuneata obtained from GenBank (Accession No.s
EU918925 and AY026429). P-distances between autumn- and spring-spawning cohorts
were calculated in MEGA6 (Tamura et al. 2013). For the SNP analyses, the SNP
markers were extracted from the sequences (read length ~100 bp) and concatenated into
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supermatrices using IUPAC codes for heterozygous loci. ML analyses were conducted
in RAxML (Stamatakis 2014) using the GTR + gamma model of sequence evolution
and support for each node was assessed with 100 bootstrap replicates. Because the
concatenation of variable SNPs artificially inflates branch lengths (Leache et al. 2015),
we used the acquisition bias correction implemented in RAxML to generate the final
topology. In addition to the tree-based methods, we also conducted a Principal
Coordinate Analysis (PCoA) in GenAlEx (Peakall & Smouse 2006, 2012) for both the
entire dataset and on subsets of the data comprising sympatric spring and autumn A.
samoensis colonies (n = 16) and allopatric A. tenuis colonies (n=16).
Incongruence between gene trees can occur simply because the phylogenetic
signal is weak, and the tree topologies differ by chance, or because the genes have not
shared the same evolutionary history (Planet 2006). To eliminate the first possibility, we
used two likelihood-based tests, a Shimodaira-Hasegawa test (SH) (Shimodaira &
Hasegawa 1999) and a one-tailed Kishino-Hasegawa test (KH) (Goldman et al. 2000) to
compare tree topologies among the SNP, CR and PaxC trees and assess whether all
trees were equally good explanations of the data. Only samples that were successfully
amplified across all three of the datasets were included in the tests, which were
conducted in Tree-Puzzle (Schmidt et al. 2002) using nonparametric bootstrap with re-
estimated log likelihoods (RELL) approximation for re-sampling the log likelihood with
1,000 replicates.
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Results
Sequencing
In total, 85 individuals were sequenced for PaxC and 84 were sequenced for CR.
After the indels were coded as single base changes and sequences were aligned and
trimmed, segments of the CR and PaxC sequences consisted of 1,036 bp and 357 bp
respectively from 20 species of Acropora. Eighty-six individuals were genotyped using
DArT-seq methodologies across a total of 44,356 loci. We filtered DArT loci for a
minimum genotype call rate of 0.70 and minimum 8X coverage, leaving 10,034 (23%)
loci remaining for phylogenetic analyses. Within each species, approximately 18% of
loci were polymorphic, and the average frequency of homozygotes for the reference
allele was 0.716 (+/- 004) (Appendix 5.3). The resolution of the inferred tree topology
substantially increased as the SNP data matrix increased in size; using genotype call rate
of 1.00 and 0.90 produced topologies with much lower bootstrap support in the internal
branches than when using a call rate of 0.70 (Appendix 5.4).
Data analysis
Tests of congruence between the trees of the CR, PaxC and the SNP datasets
showed that the SNP tree was the optimal tree (Table 5.1), and the results of the KH and
SH tests indicated that the CR and PaxC trees were significantly worse representations
of the data than the SNP tree (P < 0.005, Table 5.1). High posterior probabilities
extended to finer relationships in the SNP tree, offering greater resolution of
evolutionary relationships than the CR or PaxC (Fig. 5.2). All methods of analysis
recovered three of the four phylogenetically discrete Acropora clades from the original
Acropora phylogeny in van Oppen et al. (2001) (the monotypic fourth clade was not
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recovered because A. latistella was not included in this study), although clade III in
PaxC and clade IV in CR were weakly supported by bootstrap values (Fig. 5.2). PaxC
was a better fit to the SNP tree than the CR (PaxC -ln L = 3831.7 compared to CR –ln L
= 4183.72; Table 5.1) and contained three discrepancies with the SNP tree, while the
CR contained seven discrepancies with the SNP tree (Appendix 5.5). Three species
were polyphyletic with colonies split between clades III and IV in all of the CR, PaxC
and the SNP trees (A. digitifera, A. aspera and A. lutkeni; Fig 5.2). Many species in the
SNP tree were polyphyletic with colonies split within clades (A. subulata, A. pulchra, A.
stoddarti, A. gemmifera, A. muricata, A. tenuis, A. selago, A. florida, A. samoensis and
A. divaricata; Fig. 5.2), and one species that was monophyletic in the SNP tree was
polyphyletic in the CR tree (A. spicifera; Fig. 5.2).
Table 5.1. Test results obtained from the likelihood-based Shimodaira-Hasegawa (SH)
and Kishino-Hasegawa (KH) tests for the three phylogenetic trees (SNP, PaxC and CR).
P values (P) were calculated from 1,000 permutations using the RELL method and were
significant at 0.05*.
Trees -ln[L] ∆ln[L] KH (P) SH (P)
SNP 3586.08 - 1.0000 1.0000
PaxC 3831.70 245.61 0.0000* 0.0010*
CR 4183.72 597.63 0.0000* 0.0000*
The PaxC tree placed the spring- and autumn-spawners of all three species (A.
millepora, A. samoensis and A. tenuis) into different clusters within the major clades
(Fig. 5.2). Contrastingly, the SNP tree split the spring- and autumn-spawners of A.
samoensis but not of A. millepora or A. tenuis (Fig. 2). The spring- and autumn-
spawners were not separated in any species in the CR tree, which had low phylogenetic
signal. The differences in PaxC sequences between spring- and autumn-spawners in
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Figure 5.2. Comparison of phylogenetic trees in the genus Acropora using 10,034 SNPs, mtDNA CR and PaxC, with collapsed nodes to illustrate
major patters (see Appendix 5.4 for uncollapsed SNP trees). Branch support values are maximum likelihood bootstrap values and Bayesian posterior
probabilities (outgroups are not shown). Major Acropora clades are indicated by Roman numerals; spring and autumn spawners are shown in blue
and red; symbols after species indicate polyphyletic lineages.
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A. millepora, A. samoensis and A. tenuis were characterized by multiple fixed
differences and phylogenetically informative indels (Fig. 5.3). The p-distance between
the autumn and spring spawners was highest in A. samoensis (p = 0.017; Fig. 5.3a) and
lowest in A. tenuis (p = 0.011; Fig. 5.3c).
Figure 5.3. Comparisons between haplotypes of PaxC in autumn- and spring-spawners
in (a) A. samoensis, (b) A. millepora and (c) A. tenuis. Each box represents a nucleotide
base; the top row represents the most common haplotype in each species (dark shading),
and light shading illustrates a different nucleotide. Only unique haplotypes within each
reproductive cohort are shown. Fixed differences between the spring- and autumn-
spawners are shown by black circles; the numbers indicate the presence/absence of an
indel and detail the length (number of base pairs) in fixed indels. p = mean p distance
between autumn- and spring- spawners in each species.
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The PCoA based on the SNP dataset showed a very similar pattern to the
phylogenetic analyses; the genus was split into three clusters that corresponded to the
three phylogenetic clades, but none of the species showed a clear split between the
autumn and spring spawners (Fig. 5.4a). Limiting the SNP dataset to A. samoensis and
A. tenuis with genotype call rates of 1.0 (3,554 SNPs for A. samoensis and 1,458 SNPs
for A. tenuis), revealed clear clustering between sympatric spring and autumn spawners
in A. samoensis (Fig. 5.4c), but not a clear split between the autumn and spring
spawners in A. tenuis (Fig. 5.4b).
Figure 5.4. Plots of the first two axes from Principal Co-ordinates Analyses (PCoA); (a)
all Acropora species examined (10,034 loci), (b) A. tenuis seasonal cohorts (1,458 loci)
and (c) A. samoensis seasonal cohorts (3,554 loci). Dashed eclipses enclose species
within the major Acropora clades; triangles = A. tenuis, circles = A. samoensis,
diamonds = all other Acropora species; within A. tenuis and A. samoensis white shading
= autumn-spawners, grey shading = spring-spawners.
Coord 1 (28%)
Coord
2 (
10%
)I
III
IV
(a)
(b) (c)
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Discussion
This study represents a significant step forward in unravelling the evolutionary
relationships among Acropora corals, and provides insight into the influence of
reproductive timing on the evolutionary patterns in this group of important reef-building
species. There are three possible explanations for incongruence between the
phylogenies presented in this study. First, PaxC could be a rapidly evolving gene that
presents a highly resolved phylogeny, with which other loci will eventually become
phylogenetically concordant. However published mutation rates of the PaxC intron are
less than those of the CR (Chen et al. 2009), making this explanation unlikely.
Furthermore, the SH test suggests lower phylogenetic signal in the PaxC tree, and there
is less resolution at branch tips in the PaxC tree than the SNP tree, indicating that the
PaxC phylogeny is not more highly resolved. A second possible explanation is that the
PaxC intron and/or coding region are under selection associated with coral spawning
season. The PaxC gene in anthozoans is closely related to Pax6 in higher order animals,
which is involved in developing eyes (Callaerts et al. 1997; de Jong 2005; Matus et al.
2007; Miller et al. 2000). While anthozoans do not have eyes, they are sensitive to light,
and they use light cues to control spawning on a range of time scales (Brady et al. 2009;
Reitzel et al. 2013; Sweeney et al. 2011; van Woesik et al. 2006); hence PaxC might
function as a type of photoreceptor that cues spawning. A third possible explanation is
that PaxC is simply a hitchhiker linked to other aspects of reproductive isolation, as
quantitative trait loci for different traits under divergent selection can co-localize on the
genetic linkage map (Hawthorne & Via 2001). Further investigation is required to
separate these two possibilities, and provides a fascinating avenue for future research
into the genes that influence reproductive isolation and speciation in corals.
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Irrespective of whether it is functional or a hitchhiker, our analysis suggests that
PaxC is located in a genomic region that contributes to reproductive isolation in
Acropora corals. Identifying genes and genomic regions that confer isolation is a major
goal of speciation research, because they can provide insight into ecological settings,
evolutionary forces and molecular mechanisms that drive the divergence of populations
(Orr et al. 2004; Rieseberg & Blackman 2010). Genes that confer reproductive isolation
may very well be leading indicators of evolutionary relationships and define the
branches of what will ultimately become the species tree. In A. samoensis, the PaxC
lineages are clearly separated in the SNP phylogeny, but in A. tenuis and A. millepora
they are not. The level of genetic differentiation between PaxC sequences associated
with autumn- and spring-spawning cohorts is lower in A. tenuis and A. millepora than in
A. samoensis (Fig. 5.3), suggesting incomplete reproductive barriers or recent
polymorphism in these species. Greater divergence in PaxC in A. samoensis could
reflect a longer period of temporal isolation between autumn and spring spawners in this
species. The A. humilis group is an old species group, with fossil discoveries in
Indonesia dating A. samoensis to 9.4-9.8 MY old (Santodomingo 2014), and fossils of
A. slovenica (also in the A. humilis group and very similar to A. samoensis) to the
Oligocene – approximately 28.1 to 33.9 MY old (Wallace & Bosellini 2014). If so,
these patterns suggest that genetic divergence associated with the timing of reproduction
may take a long time to evolve to the stage of reciprocal monophyly.
Despite the incongruence associated with PaxC and reproduction, all three gene
trees revealed numerous polyphyletic species split across the major clades. In three
polyphyletic species in particular (A. aspera, A. digitifera and A. lutkeni), conspecifics
occur in the same position in all phylogenetic analyses, they were well supported in all
trees, and patterns consistent between the mitochondrial and nuclear genes. This
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combined evidence suggests that these are indeed phylogenetic lineages, and represent
morphologically cryptic species. Advances in molecular techniques in the past two
decades have revealed a plethora of cryptic species that are widespread across
scleractinian coral genera (Flot et al. 2011; Ladner & Palumbi 2012; Schmidt-Roach et
al. 2013; Warner et al. 2015; Richards et al. 2016), and their continued identification is
critical for the successful conservation and management of these ecologically important
and globally threatened group of corals.
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Chapter 6
Synthesis
“Because we have much more landscape and coastline than people, our shores
and shallows are still rich in life, diversity and strangeness. We have perhaps
more than our fair share of shoreline miracles, of visitations and wonders…”
(Tim Winton, on Western Australia, in Land’s Edge)
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This thesis explored the influence of asynchronous reproductive timing on
patterns of genetic structure in coral populations, at both ecological and evolutionary
levels, in the context of understanding the evolution of seasonal breeding patterns in
Western Australia. Following documentation of the geographical pattern of seasonal
breeding in Acropora, this thesis aimed to answer three questions:
(i) Are conspecific colonies that spawn in autumn and spring reproductively
isolated and genetically differentiated, or do colonies switch spawning time, allowing
genetic mixing?
(ii) Are conspecific autumn- and spring-spawning colonies associated with
distinct phylogenetic lineages?
(iii) Are spawning patterns on WA reefs a result of an inherited legacy from
northern ancestors, or natural selection?
Here, I integrate the results from each chapter to draw together the key findings
and answer these questions. I also review the limitations of this study, and outline the
main implications, including areas for future research.
Asynchronous spawning influences reproductive isolation
Population genetic analysis of microsatellites in sympatric (Acropora
samoensis) and allopatric (A. tenuis) populations in Chapters 3 and 4 showed that
autumn- and spring-spawning cohorts are genetically differentiated, (FST = 0.17 in both
species), confirming strong isolation. In A. samoensis, this differentiation was
accompanied by subtle morphological differences, hence the combination of genetic,
phylogenetic, morphological and reproductive differences between the autumn- and
spring-spawners indicate that the reproductive cohorts represent cryptic species (i.e.
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distinct genotypic clusters; Mallet 1995). However, in A. tenuis, while the autumn- and
spring-spawning cohorts were genetically differentiated, this differentiation was
confounded by geographic separation, and the lack of seasonal spawning data for
specific genotypes made it difficult to isolate the contribution of spawning-related
variation from geographic variation. Nevertheless, FST between populations at Ashmore
Reef and populations south of Scott Reef was substantial (range 0.40-0.58), and was
accompanied by subtle morphological differences and differences in reproductive
timing, suggesting the population of A. tenuis at Ashmore Reef may be also be a cryptic
or incipient species. The identification of cryptic species is important not only for
estimates of biodiversity, but also for correct interpretation of population connectivity,
gene flow, dispersal and ecological patterns (Bickford et al. 2007). Conservation
management relies on accurate estimates of these processes, and the recognition that
asynchronous reproduction can be associated with cryptic speciation indicates the
importance of incorporating observations of reproductive timing into population genetic
studies of corals and that cryptic species are identified.
The level of reproductive isolation between the sympatric reproductive cohorts
of A. samoensis reflects the findings from other coral species, that seasonal spawning
time is strongly heritable (Levitan et al. 2011; Vize et al. 2005), and does not routinely
switch between seasons. Nevertheless, spawning time is not etched in stone, as shown
by the IMa2 analysis of A. samoensis in Chapter 3, which detected some introgression
between the reproductive cohorts, indicating some individuals have switched spawning
season in the past. Experimental studies suggest that, while there is a strong genetic
component to reproductive timing, it is also influenced by local environmental variation
(Fan & Dai 1999; Levitan et al. 2011).
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The origin of autumn and spring spawners on WA reefs
Phylogeographic analysis of A. tenuis in Chapter 4 provides a clue to the
evolutionary origin of autumn and spring spawners on Western Australian reefs. That
chapter showed that in terms of the distribution of the PaxC clades, Clade B (associated
with autumn spawners) was ubiquitous, but Clade A (associated with spring spawners)
was found only on northern reefs. This clinal variation of PaxC clade A suggests that (i)
its origin was most likely north of the study area, and (ii) that a gene-environment
interaction may have evolved in allopatric populations with different climatic
conditions, where selection pressure resulted in successful breeding in different seasons.
Repeated glacial cycles have resulted in the emergence and subsidence of the Sunda
Shelf in the Indonesian Archipelago (Voris 2000), potentially isolating coral
populations on either side of the shelf during periods of low sea level, and
phylogeographic breaks along the Sunda Shelf are common among invertebrates in this
region (reviewed in Carpenter et al. 2011). Hence the two PaxC clades (and associated
spring and autumn spawners) on WA reefs are likely to be in secondary contact
following allopatric divergence in Indonesia. However, a fuller understanding of the
phylogenetic origin of the lineages of A. tenuis in Western Australia will require
examination of samples from Indonesia.
While distinct phylogenetic lineages of PaxC were evident in autumn- and
spring-spawning cohorts of A. samoensis, A. tenuis and A. millepora, this did not
translate into distinct phylogenetic lineages within each species (except perhaps in A.
samoensis). This suggests that the autumn- and spring-spawning cohorts have not been
reproductively isolated long enough or completely enough for genome-wide
evolutionary divergence to occur. Conversely at the PaxC locus, divergent selection
means that this locus is resistant to gene exchange (Turner et al. 2005; Via 2009).
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Studying barriers to gene flow in populations that are not yet completely reproductively
isolated can reveal important aspects of the evolutionary process that have never been
seen before (Via 2009), as was the case with PaxC.
The evolution of seasonal breeding times on WA reefs is a result of natural selection
The spawning surveys in Chapter 2 showed that the major coral spawning
season is in autumn at all locations in Western Australia, but the magnitude of coral
spawning in spring is correlated with latitude, with a decrease in the proportion of
species spawning in spring from 49% at Ashmore Reef (12°S) to 7% at Ningaloo Reef
(23°S). Two possibilities could explain the lack of spring spawners on high latitude
reefs: (a) seasonal filtering of larval recruits from northern reefs, or (b) localized natural
selection. The results of this study suggest that spawning patterns on WA reefs are a
result of local selection, and not the seasonal filtering of recruits from northern reefs.
Chapter 2 showed that some species that were not found to spawn in autumn at
Ashmore Reef do spawn in autumn at Ningaloo Reef (e.g. A. tenuis, A. millepora, A.
secale), suggesting that the populations at Ningaloo Reef have not inherited their
spawning time from their northern relatives. Nevertheless, Ashmore Reef has a different
evolutionary history to other WA reefs, and was probably re-colonized from a different
source during the Holocene, so perhaps corals on southern reefs inherited their
spawning time from Scott Reef and not Ashmore. However, these same patterns occur
on other reefs: A. millepora spawns predominantly in spring at Scott Reef but in autumn
at Ningaloo (Gilmour et al. 2009; Rosser 2013); A. samoensis and A. valida spawn in
spring at the Montebello Islands but only in autumn at Ningaloo (Rosser unpublished
data). Therefore, this pattern suggests it is highly unlikely that these species do not
spawn in spring at Ningaloo on account of the filtering of recruits, but rather, that
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spawning patterns on WA reefs are a result of local selection that drives corals to
reproduce at a time that coincides with the most suitable conditions for fertilization
success and offspring survival.
Reproductive surveys on the east coast of Australia have shown latitudinal
variation in spawning season among conspecific populations in numerous Acropora
species, with the date of mass spawning ranging from October at 15°S to January at
30°S, in association with the timing of the maximum sea surface temperature in each
region (Babcock et al. 1986; Baird et al. 2015; Wilson & Harrison 2003). It is an old
paradigm that variation in seasonal water temperatures accounts for latitudinal variation
in the breeding season of marine benthic invertebrates (Orton 1920). In Western
Australia, however, mass spawning occurs in the same month (March) at both tropical
and subtropical latitudes, despite marked differences in the temperature regimes, and
this was one of the arguments in support of an inherited genetic legacy determining
coral spawning seasonality (Babcock et al. 1994; Simpson 1991). Nonetheless, this
merely suggests that while temperature is undoubtedly important in determining the
time of coral spawning (reviewed in Nozawa 2012), temperature is not the only factor
involved, and multiple environmental variables are likely to play an important role in
influencing the timing of coral spawning, but these require further investigation.
The association between PaxC and spawning time
Perhaps the most unexpected and interesting finding of this study was the
association between divergent PaxC lineages and different spawning seasons, that was
unique to this marker and was not present in other DNA sequence markers (CR,
Calmodulin, flanking region or 10,034 SNPs). This result suggests there is a selective
connection of some sort between PaxC and reproductive timing. All of the PaxC
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markers that showed an association with spawning in this study were introns, which are
non-coding regions of the genome, raising the question of why an intron would be
associated with spawning seasonality and /or under divergent selection. However, some
introns are functional and play a role in enhancing gene expression, or
initiating/terminating transcription (Mascarenhas et al. 1990; Haerry & Gehring 1996;
Rose et al. 2008; Chorev & Carmel 2012). The introns in PaxC are very ancient,
predating the cnidarian/triploblast split at least 543 million years ago (Grotzinger et al.
1995; Miller et al. 2000) indicating they are highly conserved, and evolutionary
conservation is often indicative of biological function (Chorev & Carmel 2012).
Alternatively, the PaxC intron, or gene, may simply be a hitchhiker, and it may be that
reproductive timing is linked to other aspects of selection and that both evolve
sympatrically. Further studies of transcriptome sequencing, and gene expression and
regulation are required to test these ideas, and provide intriguing avenues for future
research.
If PaxC does have a role in reproductive timing in corals, it is particularly
fascinating owing to the relationship of this gene to eye development in higher order
animals. The control of reproductive timing in corals is complex, and is influenced by
multiple environmental cycles that control the season, lunar phase and hour that
spawning occurs, but the extent to which each of these cycles is regulated by
environmental signals, or entrained and regulated by circadian, circalunar or circanual
rhythms is unknown. Acroporids have several circadian clock genes (e.g. clock,
timeless, cry2) which are orthologs to mammalian clock genes (Shoguchi et al. 2013;
Vize 2009), and several photoreceptor genes (e.g. opsins, melanopsin and
cryptochromes; Levy et al. 2007; Shoguchi et al. 2013), and exhibit photoreception in
the blue region of the light spectrum, with particular sensitivity to blue moonlight
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irradiance levels (Gorbunov & Falkowski 2003; Levy et al. 2003). Photo-responsive
cells of corals detect and respond to light by altering cytoplasmic calcium levels,
similarly to the way transduction pathways occur in complex invertebrate eyes (Hilton
et al. 2012). If PaxC is acting as a photoreceptor, it raises the question of why a
photoreceptor gene would be associated with seasonal spawning. One explanation is
that it could have a dual role in light perception and endogenous timekeeping in a
similar manner to cryptochromes. Cryptochromes are blue-light activated proteins that
transduce the light input into the clock mechanism via signaling cascades and
interaction with other clock genes (Oliveri et al. 2014); so they effectively play a role in
both photoreception and circadian regulation in corals. If this were also the case in
PaxC it could explain the link between photoreception (as in Pax6) and the association
with seasonal reproductive timing. Seasonal cycles are often entrained and controlled by
long-term circannual rhythms, such as plant flowering and bird migration (Dunlap et al.
2004), but the genetic basis of circannual rhythms and the genes that contribute to the
circannual clock are currently unknown (Visser et al. 2010). These ideas are highly
speculative, of course, and require much further investigation to determine whether
PaxC plays any role in photoreception, circadian regulation or reproductive timing in
Acropora.
Limitations of this study
The main limitations of this study were the small sample sizes, and the absence
of detailed spawning data for all of the populations surveyed. Western Australian reefs
are remote, and many are hard to access, and most of the field work in this study was
piggy-backed onto other expeditions with different agendas, which often precluded
collecting ideal data. For example, the A. samoensis colonies studied in Chapter 3 were
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tagged and monitored as part of a coral spawning monitoring program for another
project, and the population was randomly chosen with no preference for spring or
autumn spawners. This resulted in only a small sample of spring-spawners (n=15) of A.
samoensis, which limited the capacity of some advanced statistical analyses, such as
estimating the divergence time between the spring and autumn cohorts in the IMa2
analysis.
In Chapter 4, most of the populations of A. tenuis were sampled by a former
PhD student, who was studying dispersal and not coral reproduction, so data on
spawning season were not collected for those samples. The absence of spawning data
for specific individuals of A. tenuis meant that hypotheses about spawning time and
PaxC clade could only be inferred, rather than specifically tested. For example, analyses
in Chapter 3 showed that introgression has occurred between the spring and autumn
cohorts in A. samoensis, implying that occasionally a colony must switch spawning time
to facilitate gene flow between the cohorts, so that a spring-spawner harboring PaxC-
clade A becomes an autumn-spawner harboring PaxC-clade A (or vice versa). While I
did not detect any individuals where this was the case in A. samoensis, the same cannot
be said for A. tenuis. When individuals were assigned to spring and autumn spawning
groups according to the PaxC clade they harbored, assignment tests in STRUCTURE
based on the microsatellites suggested that several individuals were misclassified.
Therefore, the type of PaxC clade could not be used to classify colonies as spring or
autumn spawners with certainty, so I could not determine the level of differentiation
between the non-Ashmore spring and autumn spawners, or compare the similarity
between the spring-spawners at Ashmore with the spring-spawners at non-Ashmore
Reefs. These comparisons may have provided further information about the age and
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colonization history of Ashmore and Scott Reef populations, and the extent of local
adaptation in these populations.
Implications and future directions for research
The results of this study have three important implications. First, the seasonality
of breeding seasons in Western Australia is not due to an inherited “genetic legacy”, but
rather is influenced by local natural selection, presumably to spawn when conditions
favour offspring survival. Therefore, in the face of rapid climate change more research
is needed to understand exactly which environmental factors drive reproductive
schedules, and how these will be influenced by changing climatic conditions. The
finding that introgression occurs between spring and autumn reproductive cohorts in A.
samoensis indicates that spawning time is not fixed in stone, and occasionally a colony
switches from spawning in autumn to spawning in spring, presumably due to
environmental effects. But what are these environmental effects, and how important are
they? This line of enquiry needs further research.
The second major implication is that coral diversity on Western Australian reefs
is higher than is typically accounted for, due to the incidence of cryptic species. The
phylogenetic analysis of Western Australian acroporids identified three species that
were polyphyletic and contained cryptic species. Spring- and autumn-spawning cohorts
of A. samoensis are also cryptic species, which takes the number of new cryptic species
identified in this study alone to four. These findings have obvious implications for
estimates of biodiversity on Western Australia’s coral reefs, but moreover, the
identification of cryptic species is also important for correct interpretation of population
connectivity, gene flow, dispersal and ecological patterns to inform conservation and
management planning. This study illustrated that differences in reproductive timing can
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contribute to genetic subdivision within populations, therefore it is vital that
observations of reproductive timing is incorporated into the population genetic studies
of corals.
The third major implication is that there is an association between PaxC and
spawning time, so it should be used with caution as a phylogenetic marker. More
importantly this finding opens up exciting avenues of research into the genes that
influence coral spawning, and this field of knowledge is in its infancy. If PaxC is
somehow involved in coral reproduction, the clinal variation in the distribution of PaxC
clades in A. tenuis suggests potential genetic-environmental interactions, and the extent
to which coral reproductive schedules are influenced by genetics or environmental
factors is another fascinating and important avenue for future research.
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Appendices
Appendix 2.1
Explanation of the Cumulative Binomial Probability Distribution
I used the binomial probability distribution because I was most interested in the
probability of failing to detect spawning when sampling 5 colonies, to evaluate the error
rate in my sampling design. As this probability will depend upon the proportion of
colonies in each species that are actually spawning (which could be anywhere between
1 and 99%), I used the binomial probability distribution (see formula below). However,
because I was interested in the probability of failing to detect spawning in at least one
colony when sampling five colonies (rather than exactly one), I instead used the
cumulative binomial distribution mathematical model (see formula below). This
calculation was performed in MS Excel using the BINOMDIST function.
The formula for the binomial probability distribution from Fowler et al. (1999) is:
Where
P = probability of outcome
k = number of events (in this case 5)
x = the probability of a stated outcome (in this case 1 colony detected)
p = probability of a particular outcome (based on the proportion of colonies in the
population that are spawning)
While the formula I used to calculate the cumulative binomial distribution was:
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Appendix 3.1
Registration numbers for skeletal voucher specimens at the Western Australian Museum and
corresponding GenBank Accession numbers for sequences. Only unique sequences were
uploaded to GenBank, so repeat accession numbers indicate the same haplotype/allele.
Colony ID WAM
voucher
number
Inferred
spawning
season
PaxC clade Access.
Number
PaxC-
intron
Access.
Number
Control
Region
Access.
Number
Calmod.
LOW_4 Z84437 Spring A KT447648 KT447675 KT447661
LOW_6 Z84438 Spring A KT447651 KT447677 KT447664
LOW_61 n/a Spring A KT447643 KT447675 KT447660
LOW_67 n/a Spring A KT447644 KT447675 KT447665
LOW3_11 Z84443 Spring A KT447652 KT447675 KT447665
LOW3_13 Z84444 Spring A KT447642 KT447675 KT447665
LOW3_15 Z84445 Spring A KT447648 KT447675 KT447665
LOW3_17 Z84446 Spring A KT447645 n/a KT447665
LOW3_18 Z84447 Spring A KT447645 KT447675 KT447665
LOW3_3 n/a Spring A KT447645 KT447675 KT447665
LOW3_4 Z84439 Spring A KT447649 KT447677 KT447665
LOW3_5 Z84440 Spring A KT447645 KT447675 KT447661
LOW3_7 Z84441 Spring A KT447645 KT447678 n/a
LOW3_8 Z84442 Spring A KT447653 KT447678 KT447665
LOW3_42 n/a Spring A KT447647 KT447682 KT447664
LOW_19 n/a Autumn A/B HET KT447657 KT447675 n/a
LOW_3 Z84449 Autumn A/B HET KT447657 KT447675 n/a
LOW3_20 Z84460 Autumn A/B HET KT447657 KT447675 n/a
LOW_1 Z84448 Autumn B KT447654 n/a KT447658
LOW_10 Z84450 Autumn B KT447655 KT447681 KT447669
LOW_20 Z84451 Autumn B KT447655 KT447681 KT447670
LOW_23 n/a Autumn B KT447655 KT447678 KT447670
LOW_24 Z84461 Autumn B KT447654 KT447675 KT447665
LOW_26 Z84452 Autumn B KT447655 KT447679 n/a
LOW_28 Z84463 Autumn B KT447656 KT447675 n/a
LOW_32 Z84453 Autumn B KT447655 KT447675 KT447665
LOW_33 Z84462 Autumn B KT447655 KT447676 KT447674
LOW_62 n/a Autumn B KT447655 KT447675 KT447662
LOW_66 n/a Autumn B KT447655 KT447675 KT447667
LOW_68 Z84464 Autumn B KT447655 KT447675 KT447667
LOW_9 n/a Autumn B KT447655 KT447675 KT447665
LOW3_10 Z84456 Autumn B KT447655 KT447675 KT447671
LOW3_14 Z84457 Autumn B KT447655 KT447675 KT447672
LOW3_16 Z84458 Autumn B KT447655 KT447675 KT447671
LOW3_19 Z84459 Autumn B KT447655 n/a KT447672
LOW3_2 n/a Autumn B KT447655 KT447675 KT447662
LOW3_6 Z84454 Autumn B KT447655 KT447675 KT447674
LOW3_9 Z84455 Autumn B KT447655 KT447675 KT447669
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Appendix 3.2
Characteristics of 13 microsatellite primers used in this study, developed by Wang et al.
2009. All primers annealed at 49°C
Locus Primer sequence (5'-3')
Reference
repeat motif
Fluorescent
label
No.
alleles
Product
size (bp)
EST_016 CTATCTGTGTATGATCAGGACTA (AAC)7 FAM 2 97-111
TCCATCTGTTGTGGAAACTGGT
EST_032 AGGCACAAGAAAGTGGAAAACAA (TAA)21 FAM 3 119-125
TGAAGGGATGTGAAGCATGGT
EST_063 TATTGTAGTCGTTACGTAGGCT (TC)8 VIC 6 97-114
AACAATCGTGCATACTAGCTCA
EST_097 TGACAACGACATCAATCATGGT (TGA)7 PET 4 127-136
ACAGCAGGAGCTGTCAGCACT
EST_098 ACAAATTGCGCTCAAGTTGATG (TG)12 VIC 2 96-129
ACGGCTGCGAAGGAGTCTAGT
EST_149 ACGTCAAATGGATTTTCACATGA (GAT)9 PET 2 117-123
AGGTGCTTCTCTTTCCTCAGA
EST_181 TGATTGCTGAGAAAGCTAGAGAT (ATG)10 VIC 6 152-170
GCCTCACCTTGCCTTGTACA
EST_196 GTGTTGGCTATCTCATGTATAGT (TAA)9 VIC 19 131-179
ACAACACATCATCAACAACAGCA
EST_245 CAGAATGATATTTCTGCAGCACT (CA)10 FAM 5 116-123
CGCAATCGAGATTATAGGAAGA
EST_254 GGTGACCAATCAGAGTCTTGA (CA)12 PET 2 92-94
TACACTTGCTATAGTAACTTGCT
WGS_112 ACTCCACTCAGTCCTATTACCA (AAT)9 VIC 12 164-197
ACACTTCCAAGAGTCCCTACA
WGS_153 TTTCCAAGTTGCTGTGAGTACA (AATC)7 VIC 2 103-110
CGGGTGCTAAGCTTGCTCAA
WGS_211 TGACGACGAAACGTTGGCTAT (TAA)8 PET 3 181-191
AGACCGTTTCCTTTAACCAGAA
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Appendix 3.3
Methods for primer design and sequencing of the protein-coding region of PaxC
A sequence of the PaxC transcription factor from Acropora millepora published on
GenBank (Accession No. AF053459.2) was used to design forward and reverse primers
in the program OLIGO version 7. Two sets of primers were trialed in the PCR process
and the second set was used for final sequencing (Table 1). PCRs contained 21 µL
Platinum PCR Supermix (Invitrogen), 1 µL each of the forward and reverse primers and
2 µL of DNA. Thermocycling profiles consisted of an initial denaturation step of 95°C
for 3 min, followed by 35 cycles of 94°C for 30 sec, 49°C for 1 min and 72°C for 1 min,
and finally 72°C for 10 min. Products were sequenced in both directions at BGI Hong
Kong. Sequences were edited manually in Sequencher 4.5 and aligned using ClustalW
in MEGA6. Unique sequences were uploaded to GenBank (Accession Numbers
KT582778-KT582782).
Table 1. Primer sequences used for PCR and sequencing
Primer 5’ to 3’ sequence
PaxC-cds2_FP TTG CTG CAT GTG GCG TAA G
PaxC-cds2_RF TTC CTG GAT TGC CTC GGT C
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Appendix 3.4
Allele frequencies for 13 microsatellite loci in the spring- and autumn-spawning populations of
Acropora samoensis. Sample size for each locus/population is given in brackets.
Locus-allele Spring Autumn
EST245 (12) (64)
116 0 0.031
117 0.042 0
120 0.917 0.844
121 0.042 0.094
123 0 0.031
WGS153 (13) (65)
103 0.962 0.977
110 0.038 0.023
WGS112 (13) (65)
164 0.115 0.015
167 0.115 0.038
170 0 0.015
173 0.192 0.023
176 0.385 0.046
179 0 0.454
182 0.038 0.308
185 0 0.015
188 0.077 0.008
191 0.038 0.069
194 0 0.008
197 0.038 0
EST149 (12) (63)
117 0 0.016
123 1 0.984
EST016 (13) (64)
96 0.115 0.031
99 0 0.586
102 0.577 0.273
105 0.077 0.047
108 0.231 0.039
111 0 0.023
EST098 (13) (64)
96 0 0.016
99 0.231 0.195
114 0.154 0.172
118 0.577 0.523
128 0 0.086
129 0.038 0.008
Page 130
116
EST254 (13) (65)
92 0.269 0.938
94 0.731 0.062
EST196 (11) (64)
132 0 0.07
135 0.227 0.047
138 0.045 0.008
141 0.091 0.055
144 0 0.016
147 0.091 0.117
150 0.091 0.039
153 0.227 0.156
156 0.182 0.109
159 0 0.109
161 0 0.094
164 0 0.023
167 0 0.078
170 0 0.008
173 0 0.016
179 0 0.031
185 0 0.008
188 0 0.016
WGS211 (13) (53)
181 0 0.094
187 0 0.019
191 1 0.887
EST032 (13) (63)
119 0.154 0.008
122 0.846 0.976
125 0 0.016
EST063 (13) (63)
97 0 0.024
102 0 0.008
104 0.885 0.73
106 0.077 0.23
110 0.038 0
114 0 0.008
EST181 (13) (61)
152 0 0.09
158 0.269 0.041
161 0.077 0.016
164 0.385 0.721
167 0.269 0.025
170 0 0.107
EST097 (13) (65)
Page 131
117
127 0.038 0.377
130 0.577 0.462
133 0.385 0.138
136 0 0.023
Page 132
118
Appendix 4.1
Characteristics of microsatellite primers used in this study after Underwood 2009a
Locus Primer sequence (5'-3') Reference repeat motif
Fluoro
label
Product size
(bp)
Amil2_006 CTTGACCTAAAAAACTGTCGTACAA (CA)4TA(CA)4 Vic -
GTTATTACTAAAAAGGACGAGAATAACTTT
Amil2_010 CAGCGATTAATATTTTAGAACAGTTTT TA(TG)11 Fam 147-151
CGTATAAACAAATTCCATGGTCTG
Amil2_011 CACTCCTTACGCTGCTAGAT (CA)2GA(CA)6CT Ned 147-155
CTCGCTAAAATGAGAGACCA
Amil2_012 TTTTAAAATGTGAAATGCATATGACA GA(CA)6GA(CA)2 Pet 106-111
TCACCTGGGTCCCATTTCT
Amil2_018 GCCCTCCTTAGGTGATTTAC (CA)9 Fam 355-370
ATCGTTTTGAGCAATCAGAC
Amil2_022 CTGTGGCCTTGTTAGATAGC (AC)10 Vic 161-180
AGATTTGTGTTGTCCTGCTT
Amil5_028 GGTCGAAAAATTGAAAAGTG (TCACA)7TCAC(TCA
CA)4
Ned 101-117
ATCACGAGTCCTTTTGACTG
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Appendix 4.2
Summary of molecular diversity across loci showing sample size (n), gene diversity (HE),
nucleotide diversity (π) and number of effective alleles (Neff). Heterozygote deficits in the
microsatellite loci are indicated by *
Population n HE ± SD π ± SD Neff
Control Region
Ashmore 12 0.44 ± 0.16 0.0004 ± 0.0004 1.79
Scott Reef 10 0.87 ± 0.09 0.0012 ± 0.0009 7.69
Rowley Shoals 8 0.89 ± 0.11 0.0020 ± 0.0014 9.09
Kimberley 7 0.71 ± 0.18 0.0013 ± 0.0011 3.45
Montebello Is 12 0.76 ± 0.12 0.0026 ± 0.0016 4.17
Dampier 10 0.35 ± 0.16 0.0003 ± 0.0004 1.54
Ningaloo 11 0.49 ± 0.18 0.0006 ± 0.0006 1.96
PaxC
Ashmore 11 0.89 ± 0.09 0.0069 ± 0.0043 9.09
Scott Reef 9 0.56 ± 0.17 0.0064 ± 0.0042 2.27
Rowley Shoals 11 0.60 ± 0.15 0.0047 ± 0.0031 2.50
Montebello Is 11 0.18 ± 0.14 0.0004 ± 0.0005 1.22
Dampier 10 0.20 ± 0.15 0.0004 ± 0.0006 1.25
Ningaloo 9 0.00 ± 0.00 0.0000 ± 0.0000 1.00
Flank
Ashmore 8 0.86 ± 0.11 0.0070 ± 0.0045 7.14
Scott Reef 7 0.86 ± 0.14 0.0056 ± 0.0037 7.14
Rowley Shoals 8 0.54 ± 0.12 0.0020 ± 0.0016 2.17
Montebello Is 7 0.60 ± 0.18 0.0011 ± 0.0012 2.50
Dampier 7 0.67 ± 0.16 0.0030 ± 0.0023 3.03
Ningaloo 9 0.78 ± 0.11 0.0039 ± 0.0027 4.55
Microsats
Ashmore 40 0.51 ± 0.21 n/a 2.04*
Scott Reef 47 0.61 ± 0.10 n/a 2.56*
Rowley Shoals 49 0.52 ± 0.16 n/a 2.08*
Montebello Is 27 0.43 ± 0.14 n/a 1.75*
Dampier 42 0.39 ± 0.15 n/a 1.64*
Ningaloo 48 0.47 ± 0.14 n/a 1.89*
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Appendix 5.1
GPS co-ordinates of sites from which each sample was collected in this study, and
registration numbers for the Western Australian Museum where samples are housed.
Samples annotated with _A = autumn spawner and _S = spring spawner. * = sample
couldn’t be amplified in CR; ^ = sample couldn’t be amplified in PaxC; # = sample not
included in SNP analysis. Sample
name
Species Sample
code
Region Site name Lat Long Registration
Number
asp1 A. aspera K11 #29 Kimberley Mavis Reef S15.50517 E123.6082 Z65722
asp2 A. aspera K11 #75 Kimberley Fraser Is S16.05467 E123.3504 Z65735
asp3 A. aspera 49 Abrolhos Easter Group S28.681521 E113.8607
cyt1 A. cytherea K10 #2 Kimberley Cassini Island S13.95603 E125.6231 Z65641
cyt2 A. cytherea K12 #39 Kimberley Browse Is S14.11754 E123.5389 Z65792
cyt3 A. cytherea 363 Montebello S17 S20.7861 E115.5067
dig1 A. digitifera K11 #33a Kimberley Wild Cat Reef S15.28229 E124.105 Z65717a
dig2 A. digitifera K11 # 33b Kimberley Wild Cat Reef S15.28229 E124.105 Z65717a
dig3 A. digitifera K10 #73 Kimberley Cassini Island S13.95603 E125.6231 Z65671
div1 A. divaricata K10 #13 Kimberley Cassini Island S13.95603 E125.6231 Z65637
div2 A. divaricata K9 # 61 Kimberley Adele Is S15.32.429 E123.0766 Z65609
div3 A. divaricata K10 #80 Kimberley Cassini Island S13.95603 E125.6231 Z65673
don1 A. donei K10 #12 Kimberley Cassini Island S13.95603 E125.6231 Z65636
don2 A. donei K10 #253 Kimberley Long Reef S13.85676 E125.8248 Z65697
don3 A. donei K12 #386 Kimberley Browse Is S14.11754 E123.5389 Z65759
flo1 A. florida K12 #7 Kimberley Jameison Reef S14.06194 E125.3667 Z65765
flo2 A. florida K13 #78 Kimberley Ashmore S12.23728 E123.1600 Z66287
flo3 A. florida K11 #36 Kimberley Black Rocks S15.03889 E124.4278 Z65712
gem1 A. gemmifera K12 #33 Kimberley Browse Is S14.11754 E123.5389 Z65750
gem2 A. gemmifera K12 #381 Kimberley Browse Is S14.11754 E123.5389 Z65756
hum1 A. humilis 601 Montebello S6 S20.40527 E115.5818
hum2*^ A. humilis K12 #23 Kimberley Browse Is S14.11754 E123.5389 Z65748
hum3 A. humilis 388 Montebello S28 Dugong S20.9077 E115.4627
int1 A. intermedia K10 #134 Kimberley Cassini Island S13.95603 E125.6231 Z65689
int2*^ A. intermedia 27 Abrolhos Pelsaert Group S28.85259 E114.0120
int3 A. intermedia 161 Abrolhos Pelsaert Group S28.85259 E114.0120
los1* A. loisetteae 169 Abrolhos Pelsaert Group S28.85259 E114.0120
los2 A. loisetteae 63 Abrolhos Easter Group S28.681521 E113.8607
los3 A. loisetteae 78 Abrolhos Easter Group S28.681521 E113.8607
lut1 A. lutkeni K10 #44 Kimberley Cassini Island S13.95603 E125.6231 Z65647
lut2 A. lutkeni K10 #46 Kimberley Cassini Island S13.95603 E125.6231 Z65649
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lut3*^ A. lutkeni K9 #160 Kimberley Montgomery Reef S15.52588 E124.1773 Z65624
mil1 A. millepora_A 501 Ningaloo North Ningaloo S22.168611 E113.865
mil2 A. millepora_A 502 Ningaloo North Ningaloo S22.168611 E113.865
mil3# A. millepora_A 500 Ningaloo North Ningaloo S22.1686 E113.865
mil4# A. millepora_A 503 Ningaloo North Ningaloo S22.1686 E113.865
mil5 A. millepora_S 215 Ashmore S2 S12.2455 E122.9867
mil6 A. millepora_S 216 Ashmore S2 S12.2455 E122.9867
mil7# A. millepora_S 201 Ashmore S2 S12.2455 E122.9867
mil8# A. millepora_S 207 Ashmore S2 S12.2455 E122.9867
mur1 A. muricata K10 #45 Kimberley Cassini Island S13.9560 E125.6231 Z65648
mur2 A. muricata K12 #439 Kimberley Heritage Reef S14.2545 E125.1596 Z65775
mur3 A. muricata K10 #129 Kimberley Cassini Island S13.9560 E125.6231 Z65684
pul1 A. pulchra K10 #79 Kimberley Cassini Island S13.9560 E125.6231 Z65654
pul2*^ A. pulchra K9 #121 Kimberley Montgomery Reef S15.5514 E124.1773 Z65621
pul3 A. pulchra K10 #128 Kimberley Cassini Island S13.9560 E125.6231 Z65683
sam1 A. samoensis_S LOW_61 Barrow Lowendal Shelf S20.7861 E115.5067
sam2 A. samoensis_S LOW_67 Barrow Lowendal Shelf S20.7861 E115.5067
sam3 A. samoensis_S LOW3_5 Barrow Lowendal Shelf S20.7861 E115.5067 Z84440
sam4 A. samoensis_S LOW3_7 Barrow Lowendal Shelf S20.7861 E115.5067 Z84441
sam5 A. samoensis_S LOW3_8 Barrow Lowendal Shelf S20.7861 E115.5067 Z84442
sam6 A. samoensis_S LOW3_11 Barrow Lowendal Shelf S20.7861 E115.5067 Z84443
sam7 A. samoensis_S LOW3_17 Barrow Lowendal Shelf S20.7861 E115.5067 Z84446
sam8 A. samoensis_S LOW3_18 Barrow Lowendal Shelf S20.7861 E115.5067 Z84447
sam9 A. samoensis_A LOW_20 Barrow Lowendal Shelf S20.7861 E115.5067 Z84451
sam10 A. samoensis_A LOW_23 Barrow Lowendal Shelf S20.7861 E115.5067
sam11 A. samoensis_A LOW_24 Barrow Lowendal Shelf S20.7861 E115.5067 Z84461
sam12 A. samoensis_A LOW_26 Barrow Lowendal Shelf S20.7861 E115.5067 Z84452
sam13 A. samoensis_A LOW_28 Barrow Lowendal Shelf S20.7861 E115.5067 Z84463
sam14 A. samoensis_A LOW_32 Barrow Lowendal Shelf S20.7861 E115.5067 Z84453
sam15 A. samoensis_A LOW3_6 Barrow Lowendal Shelf S20.7861 E115.5067 Z84454
sam16 A. samoensis_A LOW3_9 Barrow Lowendal Shelf S20.7861 E115.5067 Z84455
sel1 A. selago K10 #57 Kimberley Cassini Island S13.9560 E125.6231 Z65665
sel2 A. selago K10 #255 Kimberley Long Reef S13.8567 E125.8248 Z65698
sel3 A. selago 366 Montebello S31 S21.0444 E115.4702
spi1 A. spicifera K09 #90 Kimberley Adele Is S15.3242 E123.0766 Z65612
spi2 A. spicifera 70 Abrolhos Pelsaert Group S 28.8525 E114.0120
spi3 A. spicifera 343 Montebello S19 S20.5161 E115.4666
sto1*^ A. stoddarti K9 #113 Kimberley Adele Is S15.3242 E123.0766 Z65616
sto2 A. stoddarti K10 #158 Kimberley Cassini Island S13.9560 E125.6231 Z65677
sto3 A. stoddarti K10 #295 Kimberley Long Reef S13.8567 E125.8248 Z65706
sub1 A. subulata K10 #127 Kimberley Cassini Island S13.9560 E125.6231 Z65682
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sub2 A. subulata K11 #215 Kimberley Fraser Is S16.0546 E123.3504 Z65738
sub3 A. subulata K10 #257 Kimberley Long Reef S13.8567 E125.8248 Z65700
ten1 A. tenuis_A 309a Montebello S19 S20.5161 E115.4666
ten2 A. tenuis_A 389a Montebello S19 S20.516111 E115.4666
ten3 A. tenuis_A 448a Montebello S19 S20.516111 E115.4666
ten4 A. tenuis_A 445a Montebello S19 S20.516111 E115.4666
ten5 A. tenuis_A Z327 Kimberley East Cassini Island S13.95603 E125.6231 Z65663
ten6 A. tenuis_A Z212 Kimberley Long Reef S13.85676 E125.8248 Z65695
ten7 A. tenuis_S Z59 Kimberley West Cassini Island S13.95603 E125.6231 Z65667
ten8 A. tenuis_S Z144 Kimberley SW Cassini Island S13.95603 E125.6231 Z65655
ten9 A. tenuis_A 427a Montebello S19 S20.516111 E115.4666
ten10 A. tenuis_S E6 Ashmore Lagoon S12.240833 E122.9805
ten11 A. tenuis_S F43 Ashmore Lagoon S12.240833 E122.9805
ten12 A. tenuis_S E48 Ashmore Lagoon S12.240833 E122.9805
ten13 A. tenuis_S E33 Ashmore Lagoon S12.240833 E122.9805
ten14 A. tenuis_A A32 Ashmore NE corner S12.184444 E123.1091
ten15 A. tenuis_S E16 Ashmore Lagoon S12.240833 E122.9805
ten16 A. tenuis_S 169 Ashmore Lagoon S12.240833 E122.9805
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Appendix 5.2
Additional detail on methods described in Chapter 5
DArTseq
Genome-wide single nucleotide polymorphism (SNP) data was generated at Diversity
Arrays Technology (DArT P/L http://www.diversityarrays.com). DArTseq™ represents
a combination of a DArT complexity reduction methods and next generation sequencing
platforms. DArTseq methods are optimized for different organisms and applications by
selecting the most appropriate complexity reduction method (size of the representation
and the fraction of a genome selected for the assays). Four methods of complexity
reduction were tested and the PstI-HpaII method was selected. Genomic DNA was
processed in digestion/ligation reactions principally as per Kilian et al. (2012) but
replacing a single PstI-compatible adaptor with two different adaptors corresponding to
two different Restriction Enzyme (RE) overhangs. The PstI-compatible adapter was
designed to include Illumina flowcell attachment sequence, sequencing primer sequence
and “staggered”, varying length barcode region, similar to the sequence reported by
Elshire et al. (2011). Reverse adapter contained the flowcell attachment region and
HpaII-compatible overhang sequence. Sequencing was carried out on a single lane of an
Illumina Hiseq2500 and processed using proprietary DArT analytical pipelines. In the
primary pipeline, the FASTQ files were first processed to filter away poor quality
sequences. Approximately 2,500,000 sequences per barcode/sample were identified and
used in marker calling. Identical sequences were collapsed into fastqcall files and these
files were used in the secondary pipeline for DArT’s proprietary SNP calling algorithms
(DArTsoft14). Sequences were blasted against a Symbiodinium reference genome to
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ensure that only sequences belonging to the coral host and not the symbiont were
included in the dataset.
DaRTseq generates two types of data: (a) “Silico DaRT” which comprises
presence/absence dominant markers based on a range of DNA variation types such as
SNPs, indels and methylation variation, and (b) SNPs in fragments of approximately
100 bp. In this study only the SNP data in fragments was used, and SNPs were extracted
from each fragment and concatenated into supermatrices using IUPAC codes for
heterozygous loci.
Phylogenetic analyses
For the CR and PaxC, the most appropriate model of DNA substitution was
determined in MEGA6 (Tamura et al. 2013) using the Bayesian Information Criterion
(CR = HKY model, PaxC = K80), and these models were used in phylogenetic analyses
run in PhyML 3.0 (Guindon et al. 2010). Support for each node was based upon 1000
bootstrap replicates. For the Bayesian analyses, MCMC chains were run for 3,000,000
generations (for each gene/SNP matrix) and sampled every 100th
generation, with the
first 7,500 runs discarded as burn-in. The PaxC alignment contained 14 indels
(including several large indels up to 390 bp), and the CR contained three large indels,
and many of the indels were phylogenetically informative. Nevertheless, to take a
conservative approach, each indel was coded as a single base change. The CR and PaxC
phylogenetic trees were rooted with sequences from the sister genus Isopora, with
sequences of I. cuneata obtained from GenBank (Accession No.s EU918925 and
AY026429).
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References
Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE
2011. A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High
Diversity Species. PLoS One, 6 , 19379-10.1371/journal.pone.0019379.
Kilian A, Wenz lP, Huttner E, Carling J, Xia L, et al.. 2012 Diversity Arrays
Technology (DArT) - a generic genome profiling technology on open platforms.
Methods in Molecular Biology Edited by Francois Pompanon and Aurelie
Bonin, Humana Press: 67–91
Tamura K, Stecher G, Peterson D, Filipski A, Kumar S 2013 MEGA6: Molecular
Evolutionary Genetics Analysis version 6.0. . Mol Biol Evol 30, 2725-2729.
Guindon S., Dufayard J.F., Lefort V., Anisimova M., Hordijk W., Gascuel O. 2010 New
algorithms and methods to estimate maximum-likelihood phylogenies: assessing
the performance of PhyML 3.0. Syst Biol 59(3), 307-321.
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Appendix 5.3
Descriptive statistics of DArT single nucleotide polymorphism dataset for each Acropora
species sampled (± SE).
Species N
Freq.
heterozygotes
Freq.
homozygote ref.
Freq.
missing data
Freq.
polymorphic loci
A. aspera 3 0.028 0.736 0.160 0.182
A. cytherea 3 0.040 0.719 0.172 0.138
A. digitifera 3 0.037 0.731 0.118 0.163
A. divaricata 3 0.048 0.702 0.206 0.169
A. donei 3 0.036 0.721 0.171 0.155
A. florida 3 0.077 0.702 0.060 0.203
A. gemmifera 2 0.052 0.729 0.127 0.078
A. humilis 3 0.075 0.709 0.041 0.150
A. intermedia 3 0.047 0.719 0.094 0.134
A. loisetteae 3 0.026 0.735 0.151 0.078
A. lutkeni 3 0.023 0.732 0.166 0.166
A. millepora 4 0.044 0.725 0.104 0.115
A. muricata 3 0.020 0.727 0.211 0.180
A. pulchra 3 0.025 0.734 0.187 0.226
A. samoensis 16 0.049 0.716 0.088 0.226
A. selago 3 0.024 0.711 0.378 0.226
A. spicifera 3 0.029 0.723 0.150 0.226
A. stoddarti 3 0.100 0.681 0.071 0.226
A. subulata 3 0.042 0.720 0.128 0.226
A. tenuis 16 0.045 0.694 0.387 0.226
Min
0.020 0.681 0.041 0.078
Max
0.100 0.736 0.387 0.226
Mean 0.046 (± 0.006) 0.716 (± 0.004) 0.160 (± 0.019) 0.179 (± 0.010)
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Appendix 5.4
Maximum Likelihood trees generated in RAxML from SNP matrices with genotype call
rate of 100%, 90% and 70% and a minimum coverage of 8X. Blue stars represent spring
spawners and red stars represent autumn spawners.
ML - 100% call rate
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ML - 90% call rate
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ML - 70% call rate
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Appendix 5.5
Comparison of trees showing the placement of species within major clades (clades I, III
or IV) and the placement of individuals within species (tight or split; MS = major split
between clades; ms = minor split within a clade), and where discrepancies in topologies
between CR or PaxC and the SNP tree occurred. The criteria for a discrepancy is where
there is support for different topologies on the two trees, but does not apply where one
tree is more highly resolved than another.
Species PaxC CR SNPs
Discrepancy
in topology
A. aspera III+IV: split = MS+ms III+IV: split = MS+ms III+IV: split = MS+ms -
A. cytherea III: tight IV: tight III: tight CR ≠ SNP
A. digitifera III+IV: split = MS III+IV: split = MS III+IV: split = MS -
A. divaricata III: split = ms III: split = ms III: split = ms CR ≠ SNP
A. donei I+III: split = MS I+II+IV: split = MS I+III: split = MS CR ≠ SNP
A. florida IV: split = ms IV: tight IV: split = ms -
A. gemmifera IV: split = ms IV: tight IV: split = ms -
A. humilis IV: tight IV: tight IV: tight -
A. intermedia IV: tight IV: tight IV: tight -
A. loisetteae III: tight III: tight III: tight -
A. lutkeni III+IV: split = MS III+IV: split = MS III+IV: split = MS -
A. millepora III: split = ms III: tight III: tight PaxC ≠ SNP
A. muricata III: split = ms III+IV: split = MS III: split = ms CR ≠ SNP
A. pulchra III: tight III+IV: split = MS III: tight CR ≠ SNP
A. samoensis IV: split = ms IV: tight IV: split = ms -
A. selago I: split = ms I: tight I: split = ms PaxC ≠ SNP
A. spicifera III: tight III+IV: split = MS III: split = ms CR ≠ SNP
A. stoddarti III: tight III: tight III: split = ms -
A. subulata III: split = ms III+IV: split = MS III: split = ms CR ≠ SNP
A. tenuis I: split = ms I: tight I: split = ms PaxC ≠ SNP