1 Pour l`obtention du Grade de DOCTEUR DE L`UNIVERSITE DE

Transcription

THESE
Pour l’obtention du Grade de
DOCTEUR DE L’UNIVERSITE DE POITIERS
(Faculté des Sciences Fondamentales et Appliquées)
(Diplôme National - Arrêté du 7 août 2006)
Ecole Doctorale : Sciences pour l’Environnement Gay Lussac.
Secteur de Recherche :
Présentée par :
Benoit GANGLOFF
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Systematics and Phylogeography in gadfly petrels (Aves: Procellariiformes) and implications
for conservation
Systématique et phylogeographie chez les ptérodromes (Aves: Procellariiformes) et
implications pour la conservation
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Directeur de Thèse :
Vincent BRETAGNOLLE
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Soutenue le 17 décembre 2010
devant la Commission d’Examen
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JURY
,
Président
Richard CORDAUX, Université de Poitiers
Examinateur
Michael de L. BROOKE, Cambridge University Rapporteur
Alice CIBOIS, Muséum d’Histoire Naturelle de Genève Examinatrice
Pierre-André CROCHET, CEFE, Montpellier
Examinateur
Vincent BRETAGNOLLE, CNRS Chizé Directeur de thèse
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RESUME
Les Procellariiformes sont des oiseaux marins présentant des caractéristiques les rendant
particulièrement attractifs pour la recherche. Ces oiseaux constituent le groupe d’oiseaux marins le
plus diversifié et probablement le plus menacé, 44.7% des espèces de Procellariiformes étant
classées Vulnérables ou En Danger d’Extinction par l’UICN. Comme d’autres oiseaux marins, les
Procellariiformes constituent des modèles fascinants pour l’étude des patrons de différentiation des
populations et des espèces puisqu’étant confrontés à l’action de forces évolutives opposées : d’un
côté leur extrême mobilité leur permet de disperser très loin, ce qui est supposé permettre et
augmenter le flux de gènes entre populations et donc atténuer la différentiation des populations ; de
l’autre côté, étant extrêmement philopatriques ils reviennent généralement se reproduire dans leur
colonie de naissance, une caractéristique censée promouvoir la différentiation des populations.
Ajoutées au fait qu’ils vivent dans l’océan, ces caractéristiques les rendent particulièrement
intéressants pour étudier les processus de différentiation en l’absence de barrières physiques aux
flux de gènes. Par ailleurs, la taxonomie et les relations phylogénétiques chez les Procellariiformes
sont complexes et mal établies, entre autres en raison de l’action opposée des forces évolutives
citées précédemment. Les limites d’espèces et la taxonomie de cet ordre ont donc continuellement
changé au fil des années. Au cours des 20 dernières années, les développements observés dans le
domaine de la biologie moléculaire ont fournis des outils de plus en plus puissants pour élucider
certaines incertitudes phylogénétiques, comprendre les patrons phylogéographiques observés et
avoir une meilleure compréhension des processus promouvant la différentiation des populations
chez ces oiseaux. Ces outils permettent aussi de mieux comprendre la structuration des populations
et les relations entre ces dernières ce qui peut grandement aider à la définition des actions de
conservation entreprises pour ces organismes et à leur ordre de priorité.
Dans cette thèse, à l’aide d’outils de biologie moléculaire, j’ai étudié les relations
phylogénétiques et la phylogéographie de plusieurs taxons de la famille des Procellariidae, la plus
riche en espèce chez les Procellariiformes. Cette étude à portée sur deux niveaux taxonomiques :
premièrement, au niveau générique, cette étude décrit les relations phylogénétiques du genre
Pseudobulweria, probablement le genre d’oiseaux marins le plus menacé au monde ;
deuxièmement, au niveau du taxon et des populations l’accent a été porté sur les ptérodromes de
Macaronésie et sur le pétrel de Gould, en particulier concernant les relations entre ses sous-espèces
australiennes et néo-calédoniennes.
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L’utilisation d’analyses multilocus pour chacune de ces investigations a permis d’améliorer
notre compréhension et connaissances des ces taxons : chez Pseudobulweria la construction d’un
« arbre d’espèce » a permis de définir les relations phylogénétiques au sein du genre et de résoudre
la question du statut taxonomique du pétrel de Beck ; chez les ptérodromes de Macaronésie, j’ai
montré que la séparation des trois taxons est beaucoup plus récente que précédemment estimée et
que ces populations doivent être considérées comme des Unités Evolutives Significatives ; chez le
pétrel de Gould, cette étude a montré que les deux sous espèces ne sont pas différenciées
génétiquement et que ces deux lignées ne sont pas séparées. L’ensemble de ces résultats obtenus par
l’utilisation de plusieurs gènes nucléaires en plus de gènes mitochondriaux souligne l’importance de
ce type d’approche pour l’étude des patrons phylogénétiques et phylogéographiques pour les
comprendre dans toute leur complexité.
MOTS CLES
Taxonomie, phylogéographie, phylogénie, pétrel, Procellariiforme, conservation, biologie
moléculaire, Pterodroma, Pseudobulweria,
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ABSTRACT
Procellariiformes are seabirds showing a set of characters rendering them particularly
attractive to research. They are the most diverse seabird group, and probably also the most
threatened, with 44.7% species classified Vulnerable or worse under IUCN criteria. As many
seabirds, they are fascinating models to study patterns and processes of population and species
differentiation, being under contradictory evolutionary forces: on the one hand being extremely
vagile allow them to disperse very far, which is supposed to enhance gene flow between
populations, thus reducing their differentiation and diversification; on the other hand, being
extremely philopatric, they often return to breed in their natal colony, a pattern supposed to enhance
population differentiation. Living in the ocean, they also constitute good models to investigate
differentiation processes in the absence of physical barrier to gene flow. In addition, partly as a
result of the opposite evolutionary forces just described, their taxonomy and phylogenetic
relationships are complex, have proved very frustrating over the decades and have therefore been in
a state of flux over the years. The development of molecular ecology in the last two decades have
provided some new powerful tools to elucidate some of the phylogenetic uncertainties, to
understand the observed phylogeographic patterns and have a better grasp at the underlying
processes promoting diversification in these birds. These tools also allow a better understanding of
population structure and relationships and can greatly help to the prioritisation and design of
conservation actions directed at conserving these organisms.
In this thesis, by means of molecular ecology tools, I investigated the phylogenetic
relationships and phylogeography of several taxa belonging to family Procellariidae, the most
speciose in order Procellariiformes. I studied these at two levels: first at the genus level by
describing phylogenetic relationships in genus Pseudobulweria, probably the most endangered
seabird genus in the world; and second at the taxon and population level I focused on the
Macaronesian group of gadfly petrels and on the Gould’s petrel complex, in particular regarding the
relationships between its Australian and New Caledonian subspecies.
Using a multiloci approach for each of these investigations provided a mean to improve our
understandings: in Pseudobulweria the species tree approach used allowed inferring the
phylogenetic relationships between all the taxa in the genus for the first time and to solve a
taxonomic issue regarding the status of Beck’s petrel; in Northeast Atlantic gadfly petrels, I showed
that the divergence of the three taxa living in that region is much more recent than previously
thought and that the three populations deserve at least the status of Evolutionary Significant Units;
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in Gould’s petrel I showed that the two population currently recognised as subspecies are not
structured genetically and the lineages have not diverged. These investigations underline the
necessity to incorporate new methods and multiple loci when investigating the phylogenetic and
phylogeographic patterns in organisms to fully capture their complexity.
KEYWORDS
Taxonomy, phylogeography, phylogeny, petrel, Procellariiforme, Pterodroma, Pseudobulweria,
conservation biology, molecular ecology
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ACKNOWLEDGEMENTS
My thanks obviously go to Vincent Bretagnolle, for allowing me to do this work in Centre d’Etudes
Biologiques de Chizé, as the director of this lab and as my supervisor during these three years.
Many thanks for accepting to give me the chance to undertake this work under your supervision.
There was a part of risk in this choice and I am glad you took it.
Many thanks also to Eric Pasquet for accepting to be my co-supervisor knowing that I would spend
most of the time in the “chizean” woods rather than in the MNHN. Thanks also for giving me
access to the Service de Systématique Moléculaire and to the bird collections.
My thoughts then go to Kerry-Jayne Wilson, who quite far back in the past, in 2000, in a lift at
Lincoln University in New Zealand asked me if I would be interested in camping a few months on
an island to study petrels for my MSc research. Well, Kerry-Jayne, I’m very glad I accepted and
very grateful to you for giving me this opportunity to discover the fantastic world of petrels that, 10
years later, allows me to write this manuscript about these marvellous birds.
I would also like to acknowledge the members of the jury who accepted to evaluate my work and to
come all the way to Chizé: Michael de L. Brooke, whose book on albatrosses and petrels is a
brilliant piece of work; Alice Cibois, who was always keen to help with my questions or to provide
samples from Polynesia.; Pierre-André Crochet who managed to come despite a very demanding
timetable; Richard Cordaux from Université de Poitiers.
My thanks go to Annie Tillier and all the staff of the SSM. Thank you so much for your help,
conviviality and for helping me discovering the “wonderful” world of molecular biology. I was a
complete stranger to this world when I first came in the SSM; in a sense I still am, but thanks to you
I guess I start to have a good glimpse at this world.
And if I talk of molecular biology, then obviously I want to thank Stéphanie Dano for sharing her
lab with me (it musn’t have been easy all the time I guess…) and sharing also all the
disappointments or joys linked with PCRs failures and successes. Thank you also for your
receptiveness and technical help. Many thanks also to Colette Trouvé for your conviviality and for
the dry-ice.
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I also want to thank Delphine Bonnet and Evelyse Rouby for all the administrative side of these
three years.
Obviously, much of these investigations would not have been possible without samples. All my
gratitude therefore goes to the many people who provided those, Hadoram Shirihai, Nicholas
Carlile, Francis Zino, Ruth Brown, Jacob Gonzales-Solis, Dick Watling, Alice Cibois, Jean-Claude
Thibault.
Various Museums also kindly accepted to provide samples: American Museum of Natural History,
Museum National d’Histoire Naturelle, Te Papa Tongarewa-New Zealand Museum, Sydney
Museum, Museum de Tahiti et des iles. I also acknowledge a Collection Study grant from the
AMNH that allowed me to collect samples in this Museum with the kind help of Paul Sweet and
Margaret Hart.
I am also very grateful to Philippe Raust and the Société d’Ornithologie de Polynésie for inviting
me to take part to the expedition in the Marquesas (Eiao and Hatuta’a islands) and for organising
my stay on Raivavae. I hope I will have the opportunity to work with you again in the future.
Many many thanks to all the people who accepted to read all, or parts of this manuscript to improve
it. In particular Bertrand Gauffre, Alex Villers and Janos Hennicke. Thanks to Thomas Cornullier
also for insightful comments on some parts of the introduction.
Thanks to all the students and contractors with whom I shared some time in the field following the
harriers and bustards. Without you guys I wouldn’t have managed…: thousands of thank you to
Alex, Léo, Olivier, Steve, Victor, Audrey, Jessica and the others whose names I forget now due to
lack of sleep.
I am grateful to all the students I met here in Chizé and want to thank you all. I apologise for I will
not write all your names down but I do thank you all for the great working environment you
provided! A special thought for the “ECCB 2009” and “Oxford 2010” teams: these were good times
I spent with you guys! Hope you weren’t too scared by my driving on snowy English roads and in
Prague’s traffic…
And “Boeni” good luck with your new office mates! And with your last year!...It was fun and
pleasant to share my office with you.
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A special thought also to all the “cat keepers” when I was away in Paris or further away: Alex, Léo
& Camille (a special thank you Camille for your kindness all the time; you’re incredible, don’t
change a thing!; and Léo for all the time shared in Kerguelen and Chizé), Mirabelle (I hope you
finish your thesis at some stage and that Rackam is fine), Alban & Nadège (good luck in Britanny!
And thanks Alban for coping with me in the office… must have been tough sometimes…), Audrey
(I hope your time in Spain will be rich in meetings and experience!).
I am obviously grateful to my parents who gave me their support all the time, even though you
didn’t understand all of what I was doing here. You were there as always and that’s already more
that I could ask for.
Last but obviously not least, thanks to my ET for simply being here and helping me finish this work
and removing some of my impatience to grab my backpack and leave to explore the world.
vii
TABLE OF CONTENTS
RESUME
I
MOTS CLES
II
ABSTRACT
III
KEYWORDS
IV
ACKNOWLEDGEMENTS
V
TABLE OF CONTENTS
1
CHAPTER 1: INTRODUCTION
4
PART 1 THEORETICAL AND METHODOLOGICAL CONTEXT
I SPECIES CONCEPTS AND MODES OF SPECIATION
II MOLECULAR PHYLOGENETICS
THESIS PROBLEMATIC
PART 2. STUDY ORGANISM
I SEABIRDS
II PROCELLARIIFORMES
III GADFLY PETRELS
THESIS OBJECTIVES
REFERENCES
7
9
23
36
39
39
40
48
50
53
CHAPTER 2: GENERAL METHODS
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2.I MITOCHONDRIAL VERSUS NUCLEAR DNA
67
2.II SAMPLES: ORIGINS AND PROCESSING
70
2.II.1 FRESH SAMPLES
70
2.II.2 MUSEUM SAMPLES
70
2.II.3 DNA EXTRACTION
71
2.II.4 MITOCHONDRIAL AND NUCLEAR DNA AMPLIFICATION AND SEQUENCING
71
2.III ESTIMATION OF GENE FLOW, POPULATION DIVERGENCE TIME AND EFFECTIVE POPULATION SIZES
73
REFERENCES
76
CHAPTER 3: THE COMPLETE PHYLOGENY OF PSEUDOBULWERIA THE MOST
ENDANGERED SEABIRD GENUS: SYSTEMATICS, SPECIES STATUS AND CONSERVATION
IMPLICATIONS
80
ABSTRACT
KEYWORDS
INTRODUCTION
MATERIAL AND METHODS
SAMPLES
PHYLOGENETIC ANALYSES
RESULTS
THE GENUS PSEUDOBULWERIA: MONOPHYLY, AND RELATIONSHIPS WITH OTHER PETRELS
80
80
81
83
83
86
88
88
1
GENETIC DISTANCES AND TAXA RELATIONSHIPS WITHIN GENUS PSEUDOBULWERIA
DISCUSSION
POSITION OF PSEUDOBULWERIA WITHIN PROCELLARIIFORMES
SUBSPECIES OF TAHITI PETREL
BECK’S PETREL TAXONOMIC STATUS
FIJI PETREL
TIMING OF DIVERGENCE
WHY ARE THESE PETRELS SO RARE?
CONSERVATION IMPLICATIONS
ACKNOWLEDGMENTS
REFERENCES
APPENDIX 1
APPENDIX 2
89
94
94
94
95
95
96
97
97
98
98
104
106
CHAPTER 4: TRACKING THE PHYLOGEOGRAPHIC HISTORY IN NORTH EAST ATLANTIC
GADFLY PETRELS REVEALS MITOCHONDRIAL AND NUCLEAR DNA INCONGRUENCE
AND COMPLEX SCENARIOS
110
ABSTRACT
KEYWORDS :
INTRODUCTION
SAMPLE COLLECTION AND LABORATORY METHODS
GENETIC DIVERSITY, GENETIC STRUCTURE AND HISTORICAL DEMOGRAPHY
EVOLUTIONARY RELATIONSHIPS ESTIMATION
ESTIMATION OF GENE FLOW, POPULATION DIVERGENCE TIME AND EFFECTIVE POPULATION SIZES
RESULTS
PHYLOGENETIC RELATIONSHIPS
GENE FLOW, POPULATION DIVERGENCE TIME AND EFFECTIVE POPULATION SIZES
DISCUSSION
GENETIC DIVERSITY
POPULATION STRUCTURE
POPULATION DIVERGENCE
AKNOWLEDGEMENTS
REFERENCES
110
110
111
115
115
116
116
117
118
121
125
127
127
128
129
131
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CHAPTER 5: PHYLOGEOGRAPHY OF GOULD’S PETREL (PTERODROMA LEUCOPTERA)
AND PRELIMINARY TAXONOMIC INVESTIGATIONS IN GOULD’S (PTERODROMA
LEUCOPTERA) AND COLLARED PETREL (PTERODROMA BREVIPES)
141
PART 1: PHYLOGEOGRAPHY OF A THREATENED SEABIRD TAXON, THE GOULD’S PETREL (PTERODROMA
LEUCOPTERA)
142
ABSTRACT
142
KEYWORDS
142
INTRODUCTION
143
146
146
147
ESTIMATE OF GENETIC DIVERSITY, GENETIC STRUCTURE AND HISTORICAL DEMOGRAPHY
147
ESTIMATION OF GENE FLOW, POPULATION DIVERGENCE TIME AND EFFECTIVE POPULATION SIZES
148
RESULTS
149
PHYLOGENETIC RELATIONSHIPS
149
GENETIC DIVERSITY AND POPULATION STRUCTURE AND HISTORY
151
GENE FLOW, POPULATION DIVERGENCE TIME AND EFFECTIVE POPULATION SIZES
155
DISCUSSION
156
2
ACKNOWLEDGEMENTS
160
REFERENCES
160
PART 2: PRELIMINARY TAXONOMIC INVESTIGATIONS IN GOULD’S (PTERODROMA LEUCOPTERA) AND
COLLARED (PTERODROMA BREVIPES) PETRELS
166
ABSTRACT
166
KEYWORDS
166
INTRODUCTION
167
170
170
171
RESULTS
172
DISCUSSION
177
ACKNOWLEDGEMENTS
179
REFERENCES
179
CHAPTER 6: DISCUSSION AND PERSPECTIVES
183
6.I METHODOLOGICAL ASPECTS
6.II TAXONOMIC ASPECTS
6.III GENETIC DIVERSITY IN PROCELLARIIFORMES
6.IV POPULATION DIFFERENTIATION IN PROCELLARIIFORMES
6.IV.1 TIMING OF DIVERGENCE
6.IV.2 DIFFERENTIATION
6.IV.3 PLEISTOCENE AND OCEANOGRAPHIC CONDITIONS
6.V IMPLICATION OF THIS RESEARCH FOR CONSERVATION
6.VI PERSPECTIVES
REFERENCES
183
188
190
193
193
194
196
198
199
203
APPENDIX 1: PAPER NOT DIRECTLY RELATED TO WORK PRESENTED IN THIS
MANUSCRIPT PUBLISHED IN WATERBIRDS (2009)
210
3
CHAPTER 1: INTRODUCTION
The planet is currently experiencing an intense episode of extinction similar to prehistoric
mass extinctions. The main differences between the latter and this 6th extinction event are (i) loss of
biodiversity is much quicker now than in the past, with much higher extinction rates than
background extinction rates found in fossils and (ii) this mass extinction is caused by anthropogenic
actions. This current loss of biodiversity and the global environmental changes that are emerging
give an ever more important role to conservation biology in the 21st century (Hedrick, 2001). The
objectives of this discipline can be defined as the conservation of biological diversity and of the
processes that sustain it (Moritz, 2002). As such, conservation biology is a multidisciplinary field
that is made of subjects as diverse as ecology, biology, environmental monitoring, captive breeding,
epidemiology, historical biogeography, land management, genetics, anthropology, economics,
politics to cite but a few.
Biodiversity can be approached at various levels (Féral, 2002): first at the population level,
where different populations within a species can exhibit different degrees of genetic diversity and
differentiation whose loss contribute to the overall decline of diversity in an area; second, at the
specific level where abundance and number of species describe the diversity of a region; third at the
communities and ecosystems level in a region. In the last decades, progresses in molecular biology
techniques have rendered the use of genetics and molecular phylogenetics feasible at all these three
levels (Moritz, 1995, Hedrick, 2001). Hence, use of genetics and molecular phylogenetics has
gained a crucial importance for the long term persistence of biodiversity (Ehrlich & Wilson, 1991;
Hedrick, 2001), and Daugherty et al. (1990) stated, based on their molecular reappraisal of Tuatara
phylogeny and taxonomy, that “taxonomies are not irrelevant abstractions, but the essential
foundations of conservation practice”.
Within species, at the population level, population viability can be assessed by use of
population genetics and molecular phylogenetics tools (Moritz, 1995; Haig, 1998; Feral et al., 2002;
Storfer et al., 2009). Indeed, estimating the effective population size (Ne) can be extremely
important to evaluate the overall health of a population and prioritise conservation actions among
populations and is made possible much more rapidly than traditional demographic studies by
molecular ecology techniques. Additionally, associated with Ne, estimations of genetic diversity
and gene flow within and amongst populations can help evaluate which one are in most urgent need
of conservation action or are the most important target of actions to maintain long-term genetic
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diversity within a species. For instance, in a context of limited financial, technical or human
resources, it might be more important to protect a population that acts as a source of genetic
diversity for other populations rather than a peripheral population acting as a sink in terms of
diversity thus loosely contributing to overall genetic diversity. Conversely, identifying a population
concentrating much of the overall species genetic diversity by receiving individuals from many
others less genetically diverse populations can be of crucial importance for the long term
conservation of the species.
Such considerations of inter-population genetic diversity and relationships led to the definition of
different categories of populations relevant to conservation biology: Management Units (MU), i.e.
“populations that are genealogically close but demographically autonomous” (Avise, 2000) and can
be identified through allelic frequencies divergence whatever the depth of the gene tree considered
(Moritz, 1994), and Evolutionary Significant Units (ESU), i.e. “sets of populations with a distinct,
long-term evolutionary history mostly separate from other such units” (Ryder, 1986).
Use of molecular tools in conservation of populations can be illustrated with two examples.
Friesen et al. (2006) investigated relationships between different populations of the critically
endangered Galapagos petrel (Pterodroma phaeopygia) with microsatellite and mitochondrial DNA
data. They showed that populations on five islands should be considered as distinct MUs for the
long-term conservation of genetic diversity within the species and, as a consequence, within the
archipelago as a whole. On the other hand, investigating molecular relationships between species of
the Wandering albatross complex (Diomedea sp.), Burg & Croxall (2004) concluded that two taxa
(Diomedea antipodensis and Diomedea gibsoni), formerly considered subspecies of Diomedea
exulans then later elevated to specific rank, were not differentiated enough and should therefore be
considered together as a single unit in terms of conservation.
Furthermore, to understand and maintain evolutionary processes that sustain biological
diversity (Moritz, 2002), understanding processes acting at the genetic level within and among
populations is essential. At the specific level, taxonomic definition or reinterpretation can affect
prioritisation of conservation efforts. Indeed, although in an evolutionary perspective this can be
controversial, resources, being either financial, human or technical, are allocated and prioritised
based on taxonomic status of targeted organisms. Thus, various organisations (e.g. IUCN) or
governments use specific or subspecific statuses to protect taxa, and therefore, recognition that a
species, subspecies or population is different or not from a non-threatened related taxon, will deeply
affect resources allocations. It should be noted however that various pieces of legislation, such as
the Endangered Species Act in the USA, or the UK Wildlife and Countryside Act now incorporate
5
levels of diversity under the species level to take into account the importance of intra-specific
diversity. Given the relative facility with which it is now possible to collect genetic material from
organisms and use molecular phylogenetic tools to infer phylogenetic relationships, taxonomic
recommendations more and more rely on such genetic information. This explains the importance
taken by the field of molecular phylogenetics in terms of conservation resources allocations through
the definition of new phylogenetic relationships between taxa or reinterpretation of traditional
taxonomies. For instance, the genus Diomedea, that comprises the great albatrosses, was for a long
time considered to be made of 12 species by traditional taxonomies based on morphological
characters. However, when Nunn et al. (1996) investigated the phylogenetic relationship of these
taxa with mitochondrial DNA (cytochrome b) they found the genus to be paraphyletic and split it
into four different genera, genus Diomedea remaining with only three species D. epomophora, D.
exulans and D. amsterdamensis. Further investigations incorporating genetic, morphological and
ecological data by Robertson & Nunn (1998), Nunn & Stanley (1998) and Cuthbert et al. (2003) led
to the division of these three species into seven, of which six are recognised (Brooke, 2004). Of
these six species, several have small populations and restricted ranges hence a great increase in the
number of albatross species considered threatened (Gaston, 2004).
At the community level, molecular tools developed notably for phylogeographic studies can
be used to identify regions with highest number of ESUs, regions that can then take advantage of
greater conservation priority (Avise, 2000). Furthermore, it has been shown that Phylogenetic
Diversity, which measures biodiversity taking into account the phylogenetic relationships of taxa
(Faith, 1992, 1994), is being lost faster than expected from species extinctions (Purvis et al., 2000;
vonEuler, 2001), and that its distribution is not constant over space (Sechrest et al., 2002). Hence
the idea that evolutionary history of taxa within different regions need be taken into account by
conservation planning (Rodrigues et al, 2005). These latter authors showed that although in most
cases species richness measures are adequate to pursue this goal (albeit with a slightly lower
efficiency; Purvis et al., 2005), there can be some situations in which this is not true, for instance if
old species are endemic to relatively species-poor areas (such as the Tuatara, Sphenodon sp. in New
Zealand).
In this context where evolutionary issues need be taken into consideration, taxonomic issues
solved and biodiversity described before being lost, it is important to start with a brief reminder
about the theoretical background surrounding evolution and the concept of species, as well as the
methodological background that have arisen from the development of molecular phylogenetics.
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Part 1 Theoretical and methodological context
"Nothing in Biology Makes Sense Except in the Light of Evolution”
Theodosius Dobzhansky 1973
Evolution is considered the underlying principle that organises life on earth and the “engine”
of biology (Hayden, 2009).
Although the importance of evolution had been recognised and formulated in earlier days, notably
by French naturalist Lamarck (1809) - the Lamarckian modality of evolution being centered at
directed variation that is specifically caused by environmental factors (Koonin & Wolf, 2009a)- this
great figure was ahead of his time and its audience was not ready for his views (Conn, 1907), and it
was not until the second half of the 19th century that the importance and fundamental aspect of
evolution was described when two papers from Wallace (1858) and Darwin (1858) presenting the
“hypothesis of descent by means of natural selection” were published in London. These papers
contained five major statements (Kutschera & Niklas, 2004):
(1) all organisms produce more offspring than their environments can support;
(2) intraspecific variability of most characters exists in abundance;
(3) competition for limited resources leads to a struggle for life (Darwin) or existence (Wallace);
(4) descent with heritable modification occurs; and
(5) as a result, new species evolve into being.
However, it was in the publication of Origin of Species that Darwin (1859) summarised a
solid body of observations, amassed for more than twenty years, to transform a concept into a real
scientific theory (Kutschera & Niklas, 2004) that influenced the scientific world of this time and for
the years to come until now. In this book, Darwin presented his evolutionary theory by which “one
species does change into another through natural selection” and the accumulation of minute
changes. This theory implied that all modern living organisms are related and originate in one or a
very small number of common ancestors, hence the description by Darwin of a Tree of Life linking
all species and representing the evolutionary relationships between species. This Tree of Life
(TOL), deriving from older tradition of natural history (Ragan et al., 2009) although not stated
explicitly as such by Darwin in his first edition of the Origin of Species was nevertheless the only
drawing in this original version, indicating its importance as a metaphor of evolution (Koonin &
Wolf, 2009b). This metaphor soon took a central place in biology and evolutionary thinking and is
still of great importance despite the actual debate regarding its validity to describe the reality of the
7
biological world seen through genomic analysis (but see Koonin & Wolf, 2009b for a detailed
presentation of this topic and of the Forest of Life concept proposed by these and other authors to
replace the TOL).
With the theory of evolution and the concepts of natural selection and TOL, it became
possible for Darwin and his successors to explain the diversity of life on earth and how one species
can become many through speciation. Interestingly, although the famous Darwin’s finches would
become an iconic example of speciation, there was no understanding of the processes behind
speciation until further works by Darwin (1871), Wallace (1865, 1875, 1889), Weisman (1892, in
Kutschera & Niklas, 2004). Later, the rediscovery of Mendel’s laws and the beginnings of
population genetics, followed by the development of the synthetic theory or Modern Synthesis by
Dobzhansky (1937), Mayr (1942), Huxley (1942), Simpson (1944), Rensch (1947) and Stebbins
(1950) (Kutschera & Niklas, 2004; Koonin, 2009) allowed to understand those processes a step
further. This synthesis was later summarised in two points (Mayr & Provine, 1980 p.1):
“1.Gradual evolution can be explained in terms of small genetic changes (“mutations”) and
recombination, and the ordering of this genetic variation by natural selection; 2. the observed
evolutionary phenomena, particularly macro-evolutionary processes and speciation, can be
explained in a manner that is consistent with the known genetic mechanisms”. Embedded in this
approach, is the idea for Mayr and the tenants of the Modern Synthesis, as for Poulton (1904) - an
entomologist whose work and definition of species seem often overlooked (Mallet, 2004)-, that
species are the only truly natural grouping of individual in nature, and as such, one of the
fundamental units in biology along with genes, cells or organisms (Mayr, 1982). Such ideas
strongly contrast with the views of Darwin (1859) for whom accepting evolution forces “to treat
species in the same manner as those naturalists treat genera, who admit that genera are merely
artificial combinations made for convenience”, and of Dawkins (1976) for whom natural selection
can act at the level of an individual gene as much as at the level of an organism as a whole, and that
organisms thus are “just” a means for genes to propagate.
Independently of this debate on the relative importance of species and genes, species remain
an essential operational unit used in most, if not all, fields of biology (deQueiroz, 2005) such as
population biology, evolutionary biology, ecology, conservation biology and hence cannot be
ignored. The next section will introduce in more details the various concepts of species and models
of speciation.
8
I Species Concepts and Modes of Speciation
Species are an essential unit in all subfields of biology (deQueiroz, 2005). Furthermore, in a
time where most biodiversity remains unknown (Blaxter 2004 ; Savolainen et al. 2005) but is facing
its 6th mass extinction, the description of this biodiversity takes a very significant environmental and
economical importance (Cincotta et al. 2000; Myers et al. 2000). Species is the most used unit by
international or governmental organisations when prioritising conservation actions (Sites &
Crandall 1997; Peterson & Navarro-Siguenza 1999; Sites & Marshall 2004; Stockman & Bond,
2007) and cannot be overlooked. However, despite the central importance of species in biology and
biodiversity conservation, for more than 150 years since the publication of the Origin of Species
(Darwin, 1859) there has been no consensus on what is a species, or on how should we identify
species (Barton, 2001; Hey, 2001). In the last few decades many often contradicting definitions and
concepts of species have been put forward by biologists from different sub-fields of biology adding
complexity to the problem (deQueiroz, 2007).
Before the advent of the theory of evolution, the essentialist view of species, preceding
Lamarck, Wallace and Darwin, considered species as separately created and immutable units.
Associated with these views were empirical perceptions of similarity and inter-fecondity of
individuals from the same species. Such conceptions formed the basis of the first attempts to
formalise the concept of species. Thus, for John Ray (1628-1705) a species is “a group of
individuals, which, by means of reproduction, give birth to individuals similar to themselves” (in
Lorion, 2008). Following the rise of the theory of evolution this concept of species was to be
replaced by many others.
I. 1 Diversity of Species Concepts
Unlike other fundamental biological entities such as genes, cells or organisms, species are
difficult to perceive due to their temporal and spatial scales. With the ever increasing specialisation
of various sub-fields of biology, biologists have developed different concepts that fitted to their
field, thus giving to the species concept the specific properties that they were studying.
Two dozens of different species concepts originating in various sub-fields of biology have
been listed by Mayden (1997; Table 1.1) and more have been described since then (e.g. the genetic
9
species concept of Baker & Bradley, 2006). It should be noted that some of these concepts or their
formulations simply correspond to semantic variations on a common theme.
Table 1.1 Species concepts (from Mayden, 1997; see therein for more details)
•Reproductive Competition
•Agamospecies Concept
•Internodal Species Concept
•Morphological Species Concept
Concept*
•Biological Species Concept*
•Non-dimensional Species Concept •Successional Species Concept
•Cladistic Species Concept
•Cohesion Species Concept*
•Phenetic Species Concept
•Taxonomic Species Concept
•Composite Species Concept
•Phylogenetic Species Concept
•Ecological Species Concept*
(Diagnosable Version)*
•Evolutionary Significant Unit*
•Evolutionary Species Concept*
(Monophyly Version)
•Genealogical Concordance
Concept
(Diagnosable and Monophyly
Version)
•Genetic Species Concept*
•Genotypic Cluster Concept
•Polythetic Species Concept
•Hennigian Species Concept*
•Recognition Species Concept*
* Concepts that make reference to biological processes (e.g. reproduction and competition) that occur among
organisms within species (and less so between species) and that contribute to a shared process of evolution
within species
It is usually accepted that these species concepts can be categorised in four classes:
(i)
biological concepts based on reproductive isolation, used by population biologists for
instance;
(ii)
phenetic concepts based on overall similarity, usually in morphology or other observable
traits and mainly used by palaeontologists
(iii)
phylogenetic concepts based on common derived characters;
(iv)
ecological concepts based on common ecological niche or adaptive zone.
However, additional classes and sub-classes may be recognised, as summarised in Table 1.2
(from deQueiroz, 2007). Classes in Table 1.2 are somewhat “artificial” and several concepts are
almost identical. For instance, the Genotypic cluster concept of Mallet (1995) can be seen as an
adaptation of the Phenetic concept to genetic data based on independent markers. In addition, the
Evolutionary concept of Simpson (1951), Wiley (1978) and Mayden (1997) is very similar to the
Lineage Species Concept presented below in I-2.
All these concepts are based on some biological reality and have been formulated in a
particular context with a special “species issue” to solve. Due to a common confusion between the
definition of a taxonomic category (i.e. the definition of a concept) and the criteria that can be used
to identify taxa belonging to that category (i.e. species delimitation) (deQueiroz, 1998; Samadi &
Barberousse 2006), they often end up leading to different conclusions in terms of species
10
boundaries and numbers. The case of brown and polar bears is a good example: polar bear (Ursus
maritimus) and brown bear (Ursus arctos) are very different, both phenetically (size, fur colour…)
and ecologically (they live in different ecosystems and use them in different ways). Nevertheless,
they are able to reproduce and give birth to fertile hybrids. Thus, Ursus maritimus and Ursus arctos
can be seen as belonging to the same species according to a Biological Species Concept!
Furthermore, some populations of Ursus arctos from Alaska do share a direct common ancestor
with Ursus maritimus (Talbot & Shields, 1996), while more meridional populations of Ursus arctos
belong to another branch of the tree. Thus according to that study, the polar bear is monophyletic,
while the brown bear is paraphyletic. Ursus arctos is therefore not a species in the phylogenetic
sense since all descendants of the common ancestor do not share the same derived characters.
Table 1.2 Diversity of contemporary species concepts and properties upon which they are based;
properties usually accepted as necessary are marked with an asterisk (from deQueiroz, 2007)
Concept
Property(ies)
References
Interbreeding (natural reproduction resulting in viable and fertile
offspring)
Wright 1940; Mayr
Dobzhansky 1950
Isolation
*Intrinsic reproductive isolation (absence of interbreeding between
heterospecific organisms based on intrinsic properties, as opposed to
extrinsic [geographic] barriers)
Mayr (1942); Dobzhansky (1970)
Recognition
*Shared specific mate recognition or fertilization system
(mechanisms by which conspecifics organisms, or their gametes,
recognize one another for mating and fertilization)
Paterson (1985); Masters et al.
(1987); Lambert and Spencer
(1995)
Ecological
*Same niche or adaptive zone (all components of the environment with
which conspecific organisms interact)
Van Valen (1976); Andersson
(1990)
Evolutionary
Unique evolutionary role, tendencies, and historical fate
(some
interpretations)
Cohesion
*Diagnosability (qualitative, fixed difference)
Simpson (1951); Wiley (1978);
Mayden (1997)
Grismer (1999, 2001)
Phenotypic cohesion (genetic or demographic exchangeability)
Templeton (1989, 1998a)
Phylogenetic
Hennigian
Heterogeneous (see next four entries)
Ancestor becomes extinct when lineage splits
(see next four entries)
Hennig (1966); Ridley (1989);
Meier and Willmann (2000)
Rosen (1979); Donoghue (1985);
Mishler (1985)
Biological
1942;
Monophyletic
*Monophyly (consisting of an ancestor and all of its descendants;
commonly inferred from possession of shared derived character states)
Genealogical
*Exclusive coalescence of alleles (all alleles of a given gene are
descended from a common ancestral allele not shared with those of
other species)
Baum and Shaw (1995); see also
Avise and Ball (1990)
Diagnosable
*Diagnosability (qualitative, fixed difference)
Nelson and Platnick (1981);
Cracraft (1983); Nixon and
Wheeler (1990)
Michener (1970); Sokal and
Crovello (1970); Sneath and
Sokal (1973)
Phenetic
*Form a phenetic cluster (quantitative difference)
Genotypic
cluster
*Form a genotypic cluster (deficits of genetic intermediates; e.g.,
heterozygotes)
Mallet (1995)
11
I. 2 Towards a unification of species concepts: the Lineage Species Concept
According to deQueiroz (1998), all phenomena emphasized by the different species
definitions are different aspects of the same kind of entity and recognise that species are parts of
separately evolving lineages. Therefore, the so-called “species problem” (Mayden, 1997; Hey,
2001) is not as complex and as insurmountable as it could appear at first. deQueiroz (1998, 2005,
2007) advocates that all the concepts described previously can be considered as delimitation criteria
within a larger concept called Lineage Species Concept, derived from Simpson (1951) and Wiley
(1978). Following deQueiroz (1998), this idea has also been advocated by Samadi & Barberousse
(2006, 2009), who consider an internodal species concept that formalises the definitive divergence
of evolutionary lineages. This internodal species concept represents a broader version of the
Hennigian phylogenetic concept (Table 1.2) where a species disappears when splitting into new
lineages (Fig. 1.1) and differs from the Lineage Species Concept of deQueiroz in that it defines
more precise boundaries to species (Samadi & Barberousse, 2006).
Figure 1.1 From Samadi &
Barberousse, 2006. Species are groups
of organisms connected to one
another
through
the
global
genealogical network, and exist either
between two successive speciation
events or between a speciation and an
extinction.
Such concepts are rooted within the theory of evolution: heritable characters of
genealogically linked organisms change through random mutations. The sorting of these variable
characters of organisms is affected by both a random process (i.e. drift), and a selective process (i.e.
natural selection). The influence of these two processes is affected by intrinsic constraints of the
organisms (described through characters) and dependent on the spatio-temporal context in which
organisms are living. These sorting processes act on groups of organisms or populations and, if they
last long enough divergences can appear between populations, accumulate and ultimately give rise
to completely divergent lineages. All along the divergence process, individuals from different
lineages become more similar to each other than to individual of other lineages. And as the lineages
12
diverge, they become diagnosable through their fixed characters, their reproductive systems change
to a point where they become incompatible, they gradually stop recognising each other as potential
mates and they start using different ecological niches. Hence there is a transition from polyphyly to
monophyly (deQueiroz, 2007). The acquisition of these properties does not occur immediately, and
not necessarily simultaneously. And the order in which all these changes appear can vary.
It is because the various species concepts use different sets of properties as essential and
necessary conditions for species categories that they often become incompatible, hence the
existence of the “species problem”. This phenomenon is illustrated by Fig. 1.2 (from deQueiroz,
2007) where one can see a region in the divergence process in which different species concepts can
contradict each other. This is due to the different abilities of various species concepts to detect
divergence (especially when it is recent) and to identify whether this divergence is definitive or not.
However, previously described species concepts are mostly based on properties that can be used as
evidence to evaluate lineages divergences. Because in the lineage species concept, species are
defined as separately evolving lineages, using these properties can help evaluating the presence of
lineages evolving towards species when evidence suggest that one is looking at the grey zone of
Fig. 1.2.
Such approach has been labelled Integrative Taxonomy (Dayrat, 2005; Padial et al., 2010;
Schlick-Steiner et al., 2010). Integrative Taxonomy involves the use of different sets of criteria
(molecular, morphological, ecological…) and integrating them while assessing the congruence or
lack of congruence between the different sets of characters.
Although there probably is no perfect methodology to delineate species boundaries, this integrative
and evolutionary approach clearly extend the possibilities to identify and describe biodiversity.
13
Figure 1.2 From deQueiroz (2007). Lineage
separation and divergence and species
concepts. This represents a single lineage
splitting to form two lineages. Lineage
divergence through time is illustrated by
gradations in shades of gray. Horizontal lines
represent times at which lineages acquire
different properties (i.e., phenetic differences,
reciprocally monophyletic, reproductively
incompatible, ecologically distinct, etc.)
corresponding to different species criteria in
different species concepts. Within the gray
zone alternative species concepts come into
conflict due to adoption of different species
criteria.
I. 3 Taxonomic characters and Integrative Taxonomy
Integrative taxonomy, as other traditional taxonomic methods, is based on taxonomic
characters. Those characters, which can be qualitative or quantitative, can be classified in five
general classes: biochemical, molecular, morphological, behavioural and ecological. Each character
can possess a unique state within a population that differs from another state within another
population, or several states whose frequency distribution vary between the compared populations.
Choice of characters used to identify species limit is of paramount importance and must be done
carefully. An important point is the role played by the characters used in the speciation process.
Thus every species delimitation case is likely to have a character more appropriate than others to
assess the lineages divergence pattern. For instance, in amphibians or birds call patterns can be
important characters to differentiate species, or in groups in which ecological specialisation plays a
role in differentiation, such ecological character can be more important to consider than, say,
colourations.
14
Generally, traits that contribute to the reproductive isolation of lineages, such as sexually
selected characters or traits that promote pre- or post-mating isolation (e.g. calls in some birds or in
insects) are more likely to be indicative of species-specific differences.
In all five classes of characters cited above, traditionally the most used ones are
morphological and molecular traits. Morphological traits can usually be used with living, dead or
fossilised organisms, and can often, through visual inspection, allow differentiating individuals
from different species (although there are obviously many exceptions), making them particularly
attractive. However, despite their advantages, morphological characters suffer from two major
drawbacks, i.e. there is often a strong subjective component in their use and they usually are
continuous characters. For instance, the shape of marine organisms’ shells is a continuous character
and describing limits within this continuum to differentiate lineages often requires some part of
subjectivity. Alternatively, molecular characters such as DNA sequences have been increasingly
used in the last twenty years. DNA sequences provide many more characters (nucleotides) than
morphology and conclusions drawn from these are usually considered less subjective than
morphologically-based conclusions, at least among the proponents of the various phylogenetic
species concepts. DNA sequences are most often studied with tree-based methods that allow
searching for monophyletic lineages that could represent species. However, caution is needed, as
monophyly can encompass groups of organisms above species level. Furthermore, as a tree based
on a single locus is not necessarily in agreement with the real species tree (Nichols, 2001; Degnan
& Rosenberg, 2006, 2009), conclusions based on only one locus can lead to erroneous species
delimitations. Actual methodological developments now allow building trees based on coalescent
theory using many loci. Such approaches can detect lineage divergence despite individual gene trees
incongruence or lack of monophyly (Carstens & Knowles, 2007).
When using the Lineage Species Concept and an Integrative taxonomy approach, usual
disagreement remains as to the degree of congruence necessary between different sets of characters
to delineate species boundaries within diverging lineages. Some consider congruence between
morphological and molecular traits absolutely necessary (e.g. Dayrat, 2005; Cardoso et al., 2009),
while other authors advocate avoiding a priori combinations of characters (e.g. deQueiroz, 2007;
Padial et al., 2010; Schlick-Steiner et al., 2010). These two different approaches have been called
respectively "integration by congruence" and "integration by cumulation" (Fig. 1.3) by Padial et al.
(2010).
Integration by congruence, by considering that congruence between several taxonomic
characters is likely to indicate full lineage divergence, presents the advantage of taxonomic stability
15
(Padial et al., 2010) as only small lack of congruence between characters will lead to conclude to
incomplete lineage differentiation hence to the lack of complete separation of lineages into species.
However, by doing so, it is possible to underestimate the true number of species. Indeed, character
differentiation will not occur for all characters at different stages of speciation (Fig. 1.2).
Furthermore, such an approach could miss speciation events such as Darwin’s finches or cichlids
fishes in which morphological differences and habitat use characterise diverging lineages that have
not yet fully diverged genetically and can exhibit incomplete gene lineage sorting.
On the other hand, integration by cumulation considers that divergence in any taxonomic
character can allow delineating species. Although acknowledging the importance of congruence of
characters in species delimitations, this approach does not consider it absolutely necessary. This
gives the advantage of being able to use any taxonomic characters deemed most appropriate to
evaluate lineage differentiation. Unlike the congruence approach, this approach is likely to be more
effective to detect recently diverged species or lineages that are in the divergence process. However,
the drawback is that, by allowing the use of a lesser number of taxonomic traits, this approach can
lead to an overestimation of species numbers. For instance, one could consider that using a single
mtDNA marker is enough to draw conclusions, whereas as mentioned above gene trees do not
necessarily reflect the true species tree and evolutionary history of a lineage (Pamilo & Nei, 1988;
Nichols, 2001).
Although the formal description of Integrative Taxonomy is relatively recent, the practice of
combining different kinds of information, morphological, behavioural, molecular etc. is not new
and some authors have been advocating it without putting a name on the methodology (e.g. Forister
et al., 2008). And it has been recognised by various authors that the evolution of molecular,
morphological, ecological and behavioural characters is driven by multiple forces (Rubinoff, 2006)
and that all population level processes need be considered when trying to identify conservation
units or species limits (Forister et al., 2008).
16
Figure 1.3. From Padial et al.,
2010. Schematic representation of
two approaches of integrative
taxonomy. Background yellow,
red, and blue colors represent the
spectrum of character variation,
each dot being an independent
evolutionary linage that requires
identification and delimitation as
separate species. Integration by
cumulation
(left)
identifies
species limits with divergence in
one or more not necessarily
overlapping taxonomic characters
(e. g. mtDNA or morphology),
whereas the integration by
congruence (right) identifies
species
limits
with
the
intersection of evidence from two
or more independent taxonomic
characters (e. g. mtDNA plus
morphology). Both methods of
integration
have
relevant
limitations and there is a trade-off
between the lack of reliability of
the
species
detected
by
integration by cumulation, and
the lower taxonomic resolving
power of the integration by
congruence.
I. 4 Speciation
The origin of species is “(…) the single most important event in evolution” (Mayr 1963, p.
11 in Sobel et al., 2010). Yet, speciation is one of the least understood major features of evolution
and fundamental issues such as what causes reproductive barriers to emerge and what barriers play
a role in speciation are not yet solved (Schluter, 2001; Sobel et al., 2010).
I.4.1 Ecological vs. non-ecological speciation
Traditionally, different modes of speciation have been classified with a geographical
approach depending on the distribution of populations affected by the process, i.e. allopatric,
sympatric and parapatric speciation modes (Schluter, 2001; Coyne & Orr, 2004). Such a
17
classification focuses on the effect of gene flow or absence of gene flow on the development of
reproductive isolation. In the last two decades however, some authors suggested a shift towards an
approach centred on the underlying mechanisms driving the evolution of reproductive isolation (e.g.
Schluter, 1998; Orr & Smith, 1998; Via, 2001) and promoted the distinction between ecological and
non-ecological mechanisms to elucidate the role of natural selection in speciation. In such an
approach, is considered as non-ecological speciation three different processes: (i) speciation by
divergence under uniform selection; (ii) polyploid speciation and (iii) speciation by genetic drift
(Schluter, 2001; Table 1.3). Although such a shift in thinking might appear new, it actually
corresponds to a long-held view, dating back to the early days of the modern synthesis, that ecology
and divergent selection are major factors of speciation (Dobzhansky 1937; Stebbins 1950; Grant
1981; Rundle & Nosil, 2005). It can be argued that Darwin himself considered ecological
adaptation as a key process in speciation (Sobel et al., 2010). Even among biologists believing that
speciation can be non-ecological, the general agreement is that most of the time adaptation does
play a major role (Coyne & Orr, 2004; Templeton, 2008). Probably one of the most convincing
examples that divergent natural selection can be a major cause of reproductive isolation is the
radiation of Hawaiian honeycreepers that, from a single common ancestor, diversified in different
species adapted for feeding on various substrates, such as nectar, fruits, seeds or insects (Price,
2008).
Different definitions of ecological speciation have been stated (Schluter, 2001; Rundle &
Nosil, 2005; Nosil et al, 2009; Schluter, 2009). For instance, Schluter (2009) defines ecological
speciation as “the evolution of reproductive isolation between populations or subsets of a single
population by adaptation to different environments or ecological niches”.
It has been argued recently that, of the three mechanisms proposed as non-ecological (Table 1.3)
and presented below, only genetic drift can be considered purely non-ecological (Sobel et al., 2010).
18
Table 1.3 Modes of speciation (Schluter, 2001)
Mode of
Speciation
Mechanism
of initial
divergence
Initial form
of
reproductive
isolation
Ecological
speciation
Divergent natural
selection
Prezygotic
postzygotic
or
Speciation
by
divergence
under
uniform
selection
Different
advantageous
mutations occur
in
separate
populations
experiencing
similar selection
pressures
Genetic drift
Prezygotic
postzygotic
or
Prezygotic
postzygotic
or
Hybridization
and polyploidy
Postzygotic
Speciation
by genetic
drift
Polyploid
speciation
Proximate
basis of
reduced
hybrid
fitness
Examples of
the roles of
natural
selection
Example roles of
sexual selection
Ecological
selection,
genetic
incompatibility
and
sexual
incompatibility
Genetic
incompatibility
and
sexual
incompatibility
Initial:
Drive
divergence
in
phenotypic traits
Final:
Reinforcement
Amplify divergence of
mate
preferences
initiated by natural
selection
Reinforcement
Initial:
Drive
fixation
of
incompatible
mutations
in
different
populations Final:
Reinforcement
Drive
fixation
of
alternative
incompatible mutations
in different populations
Reinforcement
Genetic
incompatibility
and
sexual
incompatibility
Initial: None; or
opposes divergence
Final:
Reinforcement
caused by drift
Initial: None; or
promotes
further
genetic divergence
Final:
Reinforcement
Amplify differences in
mate
preferences
Reinforcement
Genetic
incompatibility
Reinforcement
Divergence under uniform selection
Although it can be debated whether selective environments can be truly uniform between
populations, different beneficial mutations could theoretically arise under similar selective regime,
thus leading to speciation. In this case, allopatric populations fix different mutations that can lead to
negative impact when combined in hybrids thus leading to intrinsic postzygotic isolation and to
speciation. However, the question remains here as to how often such postzygotic isolation can arise
with this mechanism. Additionally, selection usually generates reproductive isolation only as a
byproduct and does not promote barriers as such, whether it is divergent or uniform (Schluter, 2009;
Sobel et al., 2010).
Under uniform selection, phenotypic convergence is promoted and even distantly related
taxa often converge phenotypically under similar selective environments (Simpson, 1953).
Examples of divergence under uniform selection are rare. One case of colouration convergence
between different populations of beach mice (Peromyscus polionotus) along the Atlantic coast was
described by Steiner et al. (2009): these populations evolved similar colouration by mutations at
19
different loci despite the recognised general importance of the gene Mc1r in colourations in a broad
range of taxa (mammals, birds, lizards) and in beach mice of Florida coasts. Thus, diverse
populations of beach mice evolved convergent light colour through natural selection for camouflage
in pale sand dunes through mutations involving different loci. But in this example the question
remains whether such genetic divergence under uniform selection can be the cause of postzygotic
isolation. It could theoretically be the case, if the mutation for the colour also affect in some ways
the reproductive success of the individuals possessing it or the fitness or their progeny, but evidence
of this needs to be described. Furthermore, if such is the case, then the mechanism of differentiation
could not be truly stated as non-ecological because different fixed mutations concerned characters
directly linked to the environment, i.e. the colouration of sand dunes.
Polyploidy
Polyploïdy, i.e. the duplication of whole genome by the production of unreduced gametes,
affects many organisms, from fishes to yeast through amphibians and plants. Two kinds of
polyploidy exist: autopolyploïds originating from individuals of the same biological species, and
allopolyploids originating by hybridisation of individuals belonging to different species. Sobel et al.
(2010) argue that polyploidy can be seen both as ecological or non-ecological speciation, depending
on whether we consider polyploïds as a new species when they arise, independently of their ability
to persist or not. If we do, then it is a case of non-ecological speciation. However, if we recognise
polyploïds as a new species only when they establish a population isolated from their progenitors,
then speciation is often ecological because polyploidy is likely to produce new genetic and
phenotypic variability that can be subject to divergent selection between polyploïds and their
progenitors thus allowing the former to establish in a new ecological niche.
Drift
Theoretically, speciation by genetic drift is perfectly plausible: complete reproductive
isolation occurs from fixation of different mutations in allopatric populations through a random
process. However, in reality, drift alone can hardly produce significant reproductive isolation
(Turelli et al., 2001; Coyne & Orr, 2004; Sobel et al., 2010). Two main reasons can explain this: (i)
speciation by drift is much slower than speciation by natural selection, and isolation by drift can be
easily overcome by even weak selection (Nei et al., 1983). Furthermore, if lineage isolation is
incomplete, any secondary contact between such lineages is likely to erase isolation through gene
flow. (ii) most of the characters leading to lineage isolation are very likely to be subject to natural
selection, and except in severe bottleneck situations, such adaptive traits can hardly be altered by
20
drift alone. In theory, severe bottlenecks, such as in “founder effect”, where a small number of
individuals colonise a new environment such as an island, can allow speciation by genetic drift
(Gavrilets & Hastings 1996). However tested examples in nature are very few (Sobel et al., 2010)
and founder effect speciation remains controversial and do often include interactions between
selection and drift (Coyne & Orr, 2004). The often cited example where drift could have played a
role is the snail genus Euhadra in which a single, maternally inherited mutation can cause a change
in shell chirality (Ueshima & Asami, 2003). As individuals of opposite chirality cannot mate
together, this mutation can by itself give rise to reproductive isolation and thus to speciation.
The case of sexual selection
Role of sexual selection in lineage differentiation and speciation is widely recognised and
was investigated by West-Eberhard (1983) and Ritchie (2007) among others. Although speciation
by sexual selection alone may happen (Sobel et al., 2010), because drift or natural selection are
involved in all known examples where sexual selection had a role in lineage divergence, proving
that sexual selection is involved in a speciation event does not remove the debate about speciation
process from the ecological/non-ecological speciation context (Schluter, 2001). Indeed, as put
forward by Ritchie (2007) sexual selection mostly facilitates divergence through its interaction with
natural selection in an ecological divergence process. Thus, in the well known examples of sexual
selection in Drosophila (Templeton, 1979) and African cichlids (Seehausen et al., 1999), without
the marked niche divergence experienced by these lineages we can wonder whether sexual selection
alone could have promoted speciation. Similarly, in the sticklebacks case (Boughman, 2001),
nuptial colourations of males and female preferences are light dependent. Thus in that case sexual
selection is promoting reproductive isolation but depends on the ecological context. Similarly, there
exist some situations where sexual selection plays a role in speciation alongside genetic drift
(Lande, 1981) or promotes unique mutations through sexual conflict (e.g. Gavrilets, 2000).
I.4.2 Allopatry, sympatry, parapatry
Although traditional models of speciation focused on the geographical aspect of it through
the models of allopatry and sympatry, it remains unclear how genetically based differences in
geographic distribution can be considered as a form of reproductive isolation (Sobel et al., 2010).
Furthermore, such models have recently been labelled as overly simplistic (Grant & Grant 1997;
Schluter 2001; Rundle & Schluter 2004). However, since most speciation events start with an
21
allopatric phase (Coyne & Orr, 2004), such models can obviously not be considered obsolete, and
rather can be incorporated in the context of ecological speciation described above. Indeed, a
classical ecological speciation scenario (Schluter, 2001; Rundle & Nosil, 2005) can be described as
follow: through an allopatric phase, populations start to accumulate differences through adaptations
to unique characteristics of their environment. Upon secondary contact of diverging lineages,
ecological interactions between the populations constitute novel potential sources of divergent
selection and, if reproductive isolation was not complete, premating isolation can evolve through
reinforcement, i.e. the enhancement of prezygotic isolation by natural selection (Coyne & Orr,
2004) caused by the fact that hybrids are maladaptive. If hybridisation does not involve lesser
fitness, then the two lineages merge again and no speciation occurs. This scenario is summarised in
Fig. 1.4 (from Rundle & Nosil, 2005). If no secondary contact ever happens, or if complete lineage
divergence and reproductive isolation occurred before the secondary contact, then speciation was
allopatric. On the other hand, if lineage divergence and reproductive isolation finished evolving
after the contact, then the speciation process is labelled as sympatric (Schluter, 2001; Rundle &
Nosil, 2005). Parapatric speciation, where gene flow is reduced through isolation by distance but
not eliminated can be seen as a form of sympatric speciation (Rundle and Nosil, 2005), or as an
intermediate form between allopatric and sympatric processes (Coyne & Orr, 2004).
Different isolating barriers play a role in speciation. The difficulty remains to decide what
importance to give to these various barriers (Coyne & Orr, 2004). In this context, it has often been
argued that speciation mechanisms can only be studied in sympatric populations (Coyne & Orr,
2004; Sobel et al., 2010). Thus, it is often said that allopatric taxa cannot be considered true species
until secondary contact brings them in sympatry and “tests” their full divergence (Coyne & Orr,
2004). In this line, Rundle & Nosil (2005) considered that relevant ecological differences between
species are those that allow coexistence of lineages in sympatry. However, sympatry is not
inevitable, and traits involved in habitat isolation could possibly avoid any secondary contact to
occur (Coyne & Orr, 2004; Sobel et al., 2010). This leads some authors to consider that geographic
separation could be considered as some form of assortative mating (Kirkpatrick & Ravigné, 2002)
or that, because allopatric populations often do not exhibit any gene flow, they can be considered
species, whether they have evolved real reproductive barriers or not (Wiens, 2004).
22
Figure 1.4 From Rundle &
Nosil (2005). Speciation
scenario
in
different
geographic
contexts.
Reproductive
isolation
between two populations is
absent at the beginning of the
speciation process (left) and
evolves to completion (right).
Populations are initially
allopatric, but secondary
contact can occur at any time
(dashed
vertical
line),
commencing the second
stage of the speciation
process. Ecological causes of
divergent selection by which
reproductive isolation may
evolve are listed within the
panel for each stage
In this first part, I have introduced the concepts of species, its fundamental importance in
biology and the current state of thinking (although by the time this work is submitted, it is likely to
be partly out of date) in the study of speciation processes and concepts. Since the theory of
evolution states that species are related to each other by descent, (Darwin, 1859), understanding the
relationships between taxa and the evolutionary pathways these taxa have taken most often
necessitates the use of phylogenetics, i.e. the reconstruction of evolutionary history (Delsuc et al.,
2005). In the last decades, the use of molecular data became probably the most important tool to
achieve phylogenetics’ objective, hence the apparition of the field of Molecular Phylogenetics.
II Molecular Phylogenetics
The reconstruction of evolutionary relationships between taxa, phylogenetics, directly
follows the work of Darwin and his theory of evolution. In his “Origin of species” the only
illustration shows the relationships between species in the form of a phylogenetic tree indicating
that all species share a common history through their common ancestry. Thus, any evolutionary
study requires understanding phylogenetic relationships between taxa (Delsuc et al., 2005).
Different kinds of data can be used to investigate these relationships. Morphological characters
23
constituted the main source to phylogenetic reconstruction until the 1970’s. However, although
such data has proved useful and powerful, e.g. through the comparison of fossils and extant taxa, it
also have some limitations. For example, understanding the evolutionary relationships of viruses,
which do not leave fossils, is not possible by use of morphological characters. Indeed, in microorganisms,
morphologically homologous
characters
needed
to
understand
phylogenetic
relationships are almost non-existent, and even in complex organisms such characters are limited
(Delsuc et al., 2005). By dealing directly with the evolving substrate, i.e. the sequence of gene,
molecular phylogenetics allowed, from the 1970’s, to have access to a much greater number of
homologous characters that could be compared. By the 1980’s, molecular phylogenetics reached
maturity (Koonin & Wolf, 2009b) with the introduction of rRNA as phylogenetic marker, opening
the gates of a whole new world of investigations and the discovery of the third domain of life, the
Archaea, whose existence had never been suspected (Delsuc et al., 2005; Koonin & Wolf, 2009b).
II. 1 Phylogenetic reconstruction
The usual flow chart of phylogenetic inference process is described is Fig. 1.5 (from Holder
& Lewis, 2003). Once data is collected, DNA sequences aligned, -i.e. the process of adding gaps to
a matrix of data so that nucleotides in one column are related to each other by descent from a
common ancestral residue (Holder & Lewis, 2003)-, and a model of sequence evolution chosen (see
below), a phylogenetic tree can be constructed.
Although DNA sequence data might be seen as an incontrovertible evidence of evolutionary
relationships, uncertainties are actually frequent in molecular phylogenetic inferences. Such
uncertainties are due on the one hand to the (very) large number of trees that can describe
relationships between a set of taxa, and on the other hand, to differences in the pattern of sequence
evolution between genes, organisms and over evolutionary time. Four trees can describe the
relationships between three DNA sequences, one rooted, and three unrooted (Fig. 1.6). And when
the number of sequences increases to 100, a number easily accessible nowadays, the possible
number of trees exceeds the number of particles in the known universe (Page & Holmes, 1998).
Hence, it is most of the time computationally impossible to examine every tree in order to find the
best one, and various methods have therefore been developed that attempt to find the best tree
without having to examine every single one.
24
Figure 1.5 From Holder & Lewis
(2003).
Usual
phylogenetic
inference flow chart
Figure 1.6. Possible phylogenetic relationships between three sequences
A
B
A
B
C
A
C
B
C
B
A
C
25
Two categories of methods exist: distance methods in which aligned sequences are
transformed into a matrix of pairwise distances (the total number of nucleotide substitutions
between each pair of sequences) that is used to construct a tree; and discrete methods that consider
each aligned nucleotide site independently. Additionally, tree building can follow either a clustering
method or search for an optimality criterion. With clustering methods, a tree of three taxa is built
first, then each additional taxon is added sequentially in the best available position. On the other
hand, optimality criterion methods allows selecting the best tree from all possible trees based on
how well that tree fits a particular model of character evolution (Pade & Holmes, 1998). The most
commonly used methods of tree reconstruction are the distance-based neighbour-joining algorithm
(NJ) and the maximum parsimony (MP), maximum likelihood (ML) and the Bayesian inference
(BI) (Table 1.4) that all use discrete data and an optimality criterion.
II.1.1 Neighbour-joining
The NJ algorithm (Saitou & Nei, 1987; Studier & Keppler, 1988) uses both distance data
and clustering to infer phylogenetic trees. It is a very fast method and performs well when
sequences exhibit low divergence. However, when converting sequence data into a distance matrix
some information is lost because the observed distance between sequences is not equivalent to the
real evolutionary distance between them (Holder & Lewis, 2003). Indeed, multiple substitutions at
the same site can make sequences artificially close to each other. For distantly related sequences it
is therefore necessary to use a correction of the pairwise distance that takes into account multiple
substitutions at the same site. There exist many models of sequence evolution, each with a different
way of correct pairwise distances and the choice of the correction can be debated (Holder & Lewis,
2003). Thus, NJ is useful for an initial exploration of data, or for recently diverged sequences, but is
seldom used as the sole phylogenetic reconstruction method. This method is often used as a starting
point for more computationally intensive methods such as MP or ML.
II.1.2 Maximum Parsimony
Unlike distance-based methods such as NJ, MP and ML attempt to map gene sequence
history onto a tree by assigning a score to trees corresponding to the plausibility of the mutation
required by a particular tree to explain the data. In MP, the score is simply the minimum number of
mutations and the tree with the lowest number is chosen as the best tree. It often happens that more
26
than one tree exhibit the same number of mutations to explain the data, in which case a consensus
tree can be constructed from this set of most parsimonious trees to estimate the most consistently
recovered clades. One serious drawbacks of MP is that this method does not take into account that
the number of changes along branches is unlikely to be equal between branches. If the rate of
sequence evolution differs between taxa, some branches will be longer than others. In MP such long
branches might be considered more closely related than they truly are, simply because they have
acquired the same mutations by chance. Thus MP is susceptible to long-branch attraction
(Felsenstein, 1978) causing two long branches to be erroneously inferred to be the closest relatives
of each other while they are not. Despite such drawbacks, it has been shown that this method can
still perform well, even for divergent trees in which long-branch attraction is likely, given that
enough sequences are sampled (Hillis, 1996; Kim, 1996).
II.1.3 Maximum Likelihood
With this method, the tree that is chosen is the one that, out of all possible trees, is the most
likely to have produced the observed data according to a specified model of evolution. This method
is particularly computationally expensive as it has to investigate two problems: first, for a given
topology which combination of branch length is most likely to have given the data, and second, out
of all available topologies which one has the greatest likelihood. The likelihood for each site is
determined with the probability of that site having different nucleotides following the given model
of sequence evolution. For instances if the model states that transitions, - a mutation between two
pyrimidines (T↔C) or two purines (A↔G)-, are more common than transversions,- a mutation
between a pyrimidine and a purine (A↔C, A↔T,G↔C or G↔T)-, then the probability of a site
having both nucleotides A and G will be higher than the probability of a site having nucleotides A
and T or A and C. Despite the computational drawback, this method remains attractive because it
allows investigating all mutational scenarios compatible with the observed data by using a well
known and reliable function (Holder & Lewis, 2003). However, it must be kept in mind that the
likelihood value of a tree is not the likelihood that this tree is the true tree, but the indication that
this tree has produced the observed data under the chosen model of substitutions. On the ability of
the chosen model to describe the real process of nucleotide evolution depends the conclusion
whether or not the tree is the true one.
As said earlier, the actual number of possible trees given a set of DNA sequences quickly
becomes enormous, rendering it impossible for MP or ML algorithms to explore in a reasonable
27
timeframe every possible tree. These methods sidestep this problem by using heuristic search
methods, also called hillclimbing. Heuristic searches start with a particular tree, rearrange it to
produce different trees and save any trees that are better than their predecessors. It thus allows
investigating large datasets that would be impossible to analyse otherwise. As any method, this one
has drawbacks. The main one is that it is possible with this method to keep trapped in a local
optimum in the tree space that might not include the globally best tree.
Another issue when building a tree lies in the fact that phylogenetic trees are complex
structures whose confidence intervals are difficult to evaluate. Thus, after an analysis has run,
sometimes for days or more, a reasonable question that arises is “how does the data support
relationships seen in that tree?”
Although several methods exist to assess confidence in a phylogenetic tree (Goldman et al., 2000),
the most popular and most used one is ‘bootstrapping’ (Efron, 1979; Felsenstein, 1985). In
boostrapping, pseudoreplicate of the data set is created from the original set of sequences by
randomly selecting nucleotide sites and building new sequences of the same length as the originals.
Each site is replaced after sampling and can be sampled several times, thus, some sites might appear
more than once in the pseudoreplicate, while others might be absent altogether. Many
pseudoreplicates are produced (usually between 100 and 1000) on which the tree-building
algorithm is performed. Bootstrap supports are then calculated for clades, indicating the proportion
of times a clade was recovered from all pseudoreplicates. As a result, a clade with a low bootstrap
support is sensitive to the exact combination of sites that were sequenced and might therefore not
appear if another data set were to be collected. Despite the popularity of bootstrapping, the
interpretation of bootstrap proportions remains unclear and what is the cut-off value indicating a
‘good’ clade is not defined. Some have argued that bootstrap values being conservative, 70% might
indicate a strong support for a group (Zharkikh & Li, 1992; Hillis & Bull, 1993). In any case, what
bootstrap values tell us is whether the same result would be found given additional data collection,
not whether this result is correct. Indeed, even a wrong tree can obtain strong bootstrap values, as in
the case of a tree obtained because of long-branch attraction (Holder & Lewis, 2003).
In the last decade, an alternative method to MP and ML with bootstrapping has emerged that
has become very popular and that I have used for all my phylogenetic tree estimations, Bayesian
Inference.
28
II.1.4 Bayesian inference of phylogeny
Although Bayesian statistics were described in the 18th century, it was not until 1996 that
they were used to estimate phylogenies (Rannala & Yang, 1996; Huelsenbeck et al., 2002). In this
framework, estimating a phylogenetic tree consists in evaluating the posterior probability of a tree
according to Bayes theorem:
Pr[Tree | Data] = Pr[Data | Tree] x Pr[Tree]
Pr[Data]
Where Pr[Tree] is the prior probability of a phylogeny and Pr[Data | Tree] is the likelihood of the
observed sequences given that phylogeny. In this model, all trees are considered equally probable a
priori and a given model of nucleotide evolution is used to calculate the posterior probability of a
tree producing the observed data. The posterior probability of a tree can be seen as the tree is true
given the data set and the specified model of evolution (Huelsenbeck et al 2001). The calculation of
posterior probabilities necessitates the summing of probabilities over all trees, and all possible
combinations of branch lengths and parameter values for each tree. Thus, such calculation is
virtually impossible as such and the estimation of posterior probabilities therefore implies the use of
an algorithm such as the Markov Chain Monte Carlo (MCMC) algorithm. MCMC methods
calculate optimal trees and their accompanying posterior probabilities with randomly chosen
parameter values. A calculated tree is compared with the preceding tree and then either accepted or
rejected as the new tree based on whether it improves the posterior probability or not. In this way,
the MCMC methods can produce a reliable estimation of the posterior distribution and the
accompanied parameters in a very quick way. The Metropolis algorithm (Metropolis et al 1953;
Hastings 1970) is the most widely used MCMC method: a starting tree T1 and its posterior
probability are calculated with randomly chosen parameter values. Then, a closely related tree T2
with slightly changed parameter values is calculated. The algorithm then computes the ratio of the
posterior probabilities of these trees. If the ratio is >1, the new tree T2 is accepted and the next
neighbouring tree T3 is calculated. If the ratio is <1, a random number between 0 and 1 is drawn. If
the drawn number is less than the ratio, the new tree T2 is accepted and the next tree T3 calculated.
Otherwise, T2 is rejected and another neighbouring tree Ti is calculated. In this way, a long chain of
trees is calculated that over time converge towards trees with high probabilities. The danger of
getting stuck in local optima is lowered by the chance that a calculated tree, although worse than its
preceding tree, can be accepted if the ratio is higher than a randomly drawn value. This procedure is
repeated millions of time and the proportion of time a tree is visited constitutes a valid
29
approximation of the posterior probability of that tree (Huelsenbeck et al 2001, 2002; Tierney
1994).
Both ML and BI rely on a likelihood function and can use the same models of evolution. In
ML statistical support is provided by bootstapping, while in BI support can be calculated by
creating a consensus tree on which are reported the posterior probabilities of individual clades. In
this case, these posterior probabilities indicate the posterior probability that a clade is true given the
data set and the model of evolution. BI offers the advantage over ML that the estimation of support
values is much faster, especially for large data sets that are becoming the norm.
One of the problems of BI is that support values derived from posterior probabilities are too
liberal, as opposed to the more conservative bootstrap method. Reasons for this discrepancy are not
yet fully understood but it has been argued that it might be due to inaccuracies in the model of
evolution used (Wròbel 2008). While bootstrap values of 70% are usually seen as indicative of a
good support, BI posterior probabilities values of 95% are usually accepted as good support in a
phylogenetic tree.
When starting a BI analysis, the MCMC usually starts from a random point in the parameter
space and it can take some time before the Markov chain reaches a region with high posterior
probabilities in that parameter space. Thus, this initial period (“burn-in”) is discarded from the
analysis. Once the chain has converged, i.e. it has reached an appropriate region of the parameter
space, it will explore other regions through that space. However it happens that moving from one
region to another may take the chain a long time, or it might become trapped in one region
containing several close local optimums. One of the difficulties of BI is therefore to estimate an
appropriate burn-in period and assessing if it is exploring the character space properly. An
indication can be obtained by checking the evolution of log likelihood values. The latter tend to
increase during the burn-in period and then reaches a plateau from where it fluctuates randomly up
and down. Although this can give a hint at the efficiency of the analysis, the best solution probably
remains to run several MCMC analyses: if results from these different runs are similar, then it is an
indication that the Markov chains appropriately converge and explore the parameter space
(Huelsenbeck et al 2002).
As mentioned several times in the previous paragraphs, methods such as NJ, ML or BI use a
specified model of nucleotide evolution to reconstruct phylogenetic trees that can match the DNA
sequence data.
30
Table 1.4 (derived from Holder & Lewis, 2003) Summary of some advantages and disadvantages
of the four methods used in phylogenetic reconstruction.
Method
Neighbour joining
Advantages
Disadvantages
Fast
Information is lost in compressing
sequences into distances; reliable
estimates of pairwise distances can
be hard to obtain for divergent
sequences
Maximum Parsimony
Fast enough for the analysis of
hundreds of sequences; robust if
branches are short (closely related
sequences or dense sampling)
Can perform poorly if there is
substantial variation in branch
lengths
Maximum Likelihood
The likelihood fully captures what the
data tell us about the phylogeny under
a given model
Can be prohibitively slow
(depending on the thoroughness of
the search and access to
computational resources)
Bayesian Inference
Has a strong connection to the
maximum likelihood method; might
be a faster way to assess support for
trees than maximum likelihood
bootstrapping
The prior distributions for
parameters must be specified; it can
be difficult to determine whether the
Markov chain Monte Carlo
(MCMC) approximation has run for
long enough
II.1.5 Models of nucleotide evolution
Evolutionary processes produce different patterns of nucleotide changes in DNA (Bos &
Posada, 2004). Not taking such patterns into account can lead to estimation of wrong phylogenetic
relationships (Bos & Posada, 2004; Sullivan & Joyce, 2005). Different models of nucleotide
evolution have been proposed over the years that try to explain molecular evolutionary processes,
with different degree of complexity. Most models are related but differ in the number of parameters
they integrate. Parameters involved in model differences include nucleotide frequency within
sequences, the probability of different types of substitution (transitions, transversions) and the
substitution rates between different sites (Pages & Holmes, 1998).
The simplest model of nucleotide substitution was introduced by Jukes & Cantor (1969). By
assuming equal base frequencies within the sequence and equal substitution probabilities this model
(JC69) is mathematically simple but is unlikely to reflect the actual nucleotide evolution and to
allow an accurate estimation of phylogenetic relationships. Following this model, Kimura (1980)
proposed what is known as the Kimura 2 Parameter model (K2P). In this model, different rates for
31
transitions and transversions are used, giving a higher probability to transitions, to take into account
observed differences in substitution type, especially in mitochondrial DNA (Wakeley, 1996). It
must be noted that if transition and transversion rates are equal this model becomes the JC69 model.
Later, Felsenstein (1981) elaborated a model that addresses the possibility that base frequencies in a
set of sequence can differ. In that case, it is reasonable to expect that some types of substitutions
might be more common than others. Felsenstein model (F81) allows the frequencies of the four
nucleotides to be different, but not to the point where base frequencies are not roughly equal across
all the sequences any more. This obviously might not always be true. In order to make models
closer to observed data, several authors then built more complex models incorporating multiple
aspects of sequence evolution. Amongst such models, the Hasegawa Kishino Yano model (HKY;
Hasegawa et al., 1985) allows both different rates for transitions and transversions and different
base frequencies. Thus, it incorporates the K2P and F81 models. However, this model considers all
transversions and all transitions to have the same probabilities. A more complex model taking into
account that transitions rates might be different between purines and between pyrimidines, and that
all transversions might not have the same probability was proposed by Rodriguez et al. (1990). This
model is now referred to as the General Time Reversible model (GTR). These are the most used and
best known models. Others exist that I will not present here.
All the models presented above assume among-site substitution rate homogeneity. Although
this might be true for non-coding portions of DNA, protein coding regions are very likely to be
under functional constraint and any mutation in these regions might change the functional
properties of the coded protein thus individuals bearing this mutation could have a reduced fitness,
or, if the functional change is lethal, be simply removed from the population. Furthermore, since the
genetic code is degenerate, nucleotide substitution will not have the same effect depending on the
position of the modified nucleotide. Indeed, approximately half of third-codon positions are “fourfold degenerate”, i.e. any of the four nucleotides at this position will code for the same amino acid.
Conversely, a nucleotide change in the second codon position will almost invariably lead to an
amino acid substitution. Hence it appears intuitively that the second codon position will be more
constrained than the third codon position. This was confirmed by Nei (1987) in the globin family of
genes, in which substitutions are about three times more frequent at the third codon position than at
the first or second positions.
The most successful method to incorporate rate variation among sites in models is the use of
a gamma distribution (Γ; Bos & Posada, 2005). This statistical representation of rate variation
among sites can be added to any model such as the one I described above. The shape parameter, α,
32
of this distribution is determined by the number of nucleotide sites with various rates of
substitutions, with a shape of the distribution skewed to the right when most nucleotides are
invariable (Fig. 1.7). Thus, when α<1 a few nucleotides are evolving very rapidly and account for
most of the variation in the data. When α>20 most sites have intermediate rates of evolution and
few nucleotides evolve very slowly and very rapidly. The greater the value of α, the smaller the
difference of rate variation between nucleotides and the lesser it is necessary to take among site rate
variation into account (Bos & Posada, 2005).
Figure 1.7 From Bos & Posada
(2005). Gamma distributions
calculated using different shape
parameters (α). The shape
parameter is determined by the
number of nucleotides in a
sequence
evolving
at
a
particular rate. When most
nucleotide sites are invariable
and variation concentrated at a
few rapidly evolving nucleotide
sites, the shape parameter is
small (<1). It becomes larger
while the proportion of variable
nucleotide sites increases. This
indicates that more sites evolve
at a moderate rate and fewer
sites have extremely high or
low rates.
When using model-based phylogenetic methods such as BI or ML, the choice of model
greatly influences the outcome and the performance of the analysis. Although for long sequences
with low polymorphism model choice is not so much an issue, for sequences with high levels of
polymorphism choosing the wrong model can sometime lead to support a wrong tree topology (Bos
& Posada, 2005; Sullivan & Joyce, 2005). Therefore, model choice is of critical importance in
molecular phylogenetic estimation. Choosing an overly simplistic model will most likely affect
negatively phylogenetic estimation, unless no very short or very long branches are present in the
phylogeny that is estimated. A temptation that naturally comes to mind is therefore to always use
the most complex model to evaluate phylogenies, as it has been suggested by some authors, e.g. for
Bayesian phylogenetic inference (Huelsenbeck & Rannala, 2004). However overparameterisation
33
can lead to a loss of degrees of freedom hence to nonidentifiability of parameters and more complex
models with better likelihood scores should not be expected to be better for phylogeny estimation
(Sullivan & Joyce, 2005). Therefore the question remains as how to choose the best model given a
data set of DNA sequences. Several methods exist.
The Likelihood Ratio Test (LRT) is one of the most used model selection method (Sullivan
& Joyce, 2005). The LRT statistics calculation necessitates a starting tree to estimate the likelihoods
of the different models: it first calculates the likelihood score of a null model L0 and an alternative
more complex model L1. The two scores are then compared according to the equation
δ = 2(ln L1 − ln L0)
The test statistic is then evaluated under an assumption of asymptotic convergence to a χ2
distribution, with the degrees of freedom being the difference in number of free parameters in the
two evaluated models (Bos & Posada, 2005; Sullivan & Joyce, 2005).
This method suffers from several drawbacks. The most serious being that the model space must be
traversed by a series of pairwise comparisons, but no relevant theory exists to explain how to do so
(Sullivan & Joyce, 2005). Another weakness of LRT is that this method will tend to favour the
more complex model.
Other methods include the Aikake Information Criterion (AIC; Aikake, 1973). The AIC
selects the best-fit model by computing models’ likelihood while penalysing parameter-rich models
in an attempt to avoid overparameterisation. The chosen model is then the one with the smallest
AIC value, with AIC = -2 ln Li + 2 Ni, whith Li the likelihood for model i and Ni the number of
free parameters in model i. The AIC offers several advantages over the LRT. First, it can compare
all models simultaneously instead of conducting pairwise comparisons; second, it avoids
overparameterisation, at least partially; third, AIC allows model averaging and model selection
uncertainty calculation; four, AIC attempts to find the model that best approximates the true
unknown model of evolution given the information provided by the data.
Alternatively, the Bayesian Information Criterion (BIC) can be used to select the model that
provides the best estimation of the true evolution of nucleotides. BIC was first proposed by
Schwartz (1978) and assesses the fit of a model through maximum likelihood scores and a heavier
penalisation of overparameterisation than AIC. One of the advantages of BIC is that it avoids the
tendancy to select more complex models when sample size increases (Sullivan & Joyce, 2005).
34
II. 2 New perspectives in molecular phylogenetics
Phylogenetics tries to describe the history and relationships of species and populations
(Edwards, 2009). Until recently the common molecular phylogenetic method implied sequencing
one gene in individuals from different species, inferring a tree from the obtained sequences and
considering this tree derived from one gene to represent the species relationships (Degnan &
Rosenberg, 2009). However, it has been recognised for a long time that species trees are not the
same as gene trees, where, following Degnan & Rosenberg (2009), a species tree is “a tree of
ancestor–descendant relationships for a set of populations” and a gene tree “a tree of ancestor–
descendant relationships for a gene, where the same gene is sampled from several individuals”. In
the 1960’s and late 1970’s, Cavalli-Sforza (1964) and Felsenstein (1981) were already using simple
models to take this difference into account, and in the 1980’s several authors investigated this issue,
as presented in Pamilo & Nei (1988). In the early 1990’s, heterogeneity between gene trees within
species had been observed but did not receive much attention in the following years (Edwards,
2009) for several reasons. One of these was the lack of phylogenetic methods able to deal with large
dataset and with the complexity required to incorporate this homogeneity into analyses (Brito &
Edwards, 2009; Edwards, 2009). The development in the last decade of analytical approaches based
on the coalescent theory and Bayesian statistics, and the increasing ease with which it is becoming
possible to collect molecular data from a wide range of loci, rendered possible to tackle this issue.
Fields such as phylogeography (Avise et al., 1987; Avise, 2000) promoted the development of such
approach and models in which the estimation of population parameters above gene level implies to
embed gene histories in species history (Edwards, 2009).
It has been argued that since phylogenies affect populations, species tree parameters and the
ways to evaluate them from genetic data are in the same category than tools and models developed
in the fields of phylogeography to evaluate demographic parameters (e.g. genetic diversity, gene
flow, population divergence …) within species (Maddison, 1997; Avise, 2000; Edwards, 2009).
Thus, all these advances, in particular in coalescent theory, are now slowly closing the gap between
these fields (Brito & Edwards, 2009) and it seems that methodological and conceptual differences
developed between phylogenetics and phylogeography (Hey and Machado 2003; Brito and Edwards
2009) are likely to disappear, thus answering to Felsenstein statement that “systematicists and
evolutionary geneticists do not often talk to each other” (Felsenstein (1988) in Edwards, 2009).
35
THESIS PROBLEMATIC
Contrary to Darwin’s view that accepting evolution leads to relegating species to artificial
combinations of organisms (Darwin, 1859), it is now accepted that species constitutes a
fundamental and essential biological entity (Mayr, 1982). This biological unit is used in all fields of
biology. Being universally recognised, despite the debates surrounding the concept described by the
word, species is fundamental in Conservation Biology and is used by virtually all organisations
involved in the conservation of biological diversity and its processes. Besides, in the actual context
of erosion of biodiversity and 6th mass extinction, description of this biodiversity, i.e. the
identification of species and their relationships, has a renewed and significant environmental and
economical importance.
To describe and understand biodiversity, in order to conserve it, it is necessary to:
1) carry out taxonomic and phylogenetic description of biodiversity, i.e. what and
how many species are there? Where are they? What are the relationships between
them?
2) investigate the patterns of differentiation leading to the formation of species, i.e.
how do lineages diversify? What are the effects of gene flow between taxa on their
differentiation? What are the effects of distance and dispersal abilities? How quickly
do taxa diverge to give rise to species? To cite but a few questions.
Although answering such questions might maybe be relatively straightforward sometimes in
some terrestrial taxa with limited dispersal abilities and easily identifiable taxonomic limits, such is
not the case in many marine organisms, in particular seabirds. Indeed, these organisms exhibit
somewhat paradoxical evolutionary traits. Being highly vagile organisms mostly living in an open
environment, they face very few physical barriers to dispersal, a characteristic supposed to enhance
gene flow between populations therefore limiting genetic differentiation between these populations
(Wright, 1931). However, counteracting selective forces consisting in the discrete location of
breeding localities and extreme philopatric behaviour should, on the reverse, enhance the effect of
genetic drift. As a consequence of these conflicting processes acting on their population genetics,
seabirds show cases of population structure and/or differentiation in the absence of obvious barrier
to gene flow (e.g. Xanthus’s murrelet Synthliboramphus hypoleucus; Friesen et al., 2007).
Explanations to such pattern include the existence of non-visible barriers to gene flow, such as
36
oceanic fronts (e.g. the Almeria-Oran front in the Mediterranean sea that seems to prevent gene
flow between Atlantic and Mediterranean populations of Cory’s shearwater Calonectris diomedea;
Gomez-Diaz et al., 2006), large expanses of low productivity waters such as in the western and
eastern equatorial Pacific ocean or the distribution at sea during the nonbreeding season (Friesen et
al., 2007). These characteristics create very complex phylogeographic patterns, which, combined to
the insularity of most species and their morphological similarities, render taxonomic delineation and
understanding of patterns and processes of differentiation extremely difficult in seabirds, in
particular in petrels (order Procellariiformes).
Because seabirds are declining at a much faster rate than any other bird group (Birdlife
International; Fig. 1.8) it is necessary to undertake thorough investigations to bring answers to
points 1) and 2) in order to conserve them. It is in this context that this PhD work is embedded.
In this study, within seabirds, the biological study organisms used are members of a particularly
threatened and complex group, the petrels.
To address the issues mentioned above, we have seen that genetic and molecular
phylogenetic tools are particularly pertinent. Through the recent theoretical and methodological
developments in these fields, it is now possible, not only to reconstruct phylogenetic relationships
but also to investigate the relationships of lineages through the use of multiple loci analyses and to
understand the complex, sometimes contradicting, histories of the many genes carried by
organisms. It becomes also possible to quantify gene migration between populations and reconstruct
patterns of diversification through time and space to better understand those to come in the future.
37
Figure 1.8 Evolution of degree of threat in six groups of birds (Birdlife International, 2009)
38
Part 2. Study Organism
I Seabirds
The distinction between seabirds and other birds, particularly shorebirds, is not easy to
define (Brooke, 2002; Schreiber & Burger, 2002). Traditionally, are considered seabirds, members
of three Orders, Sphenisciformes (the penguins), Procellariiformes (albatrosses, petrels,
shearwaters, diving-petrels and storm-petrels), Pelecaniformes (tropicbirds, gannets, boobies,
cormorants, anhingas, frigatebirds), and some families in order Charadriiformes (Stercorariidae i.e.
skuas; Alcidae, the auks; and Laridae/Sternidae, gulls and terns) (Brooke, 2002; Friesen et al.,
2007a). Historically, from the fossil record, it has been suggested (Warheit, 2002) that, apart from a
bird apparently belonging to genus Puffinus from the early Oligocene (circa 29-34 millions years
ago), most seabird’s modern genera did not appear until the early Miocene, approximately 16 to 23
millions years ago (mya), although older members of seabird orders are known, e.g. family
Diomedeoididae in Procellariiformes (Mayr, 2009). From the fossil records, Warheit (1992, 2002)
also pointed out that there seem to be a correlation between the taxonomic diversification of
seabirds in the Miocene and the extensive climatic and oceanic changes that occurred during the
middle Miocene transition: around 11-17 mya, sharp oceanic temperature drops (Shevenell et al.,
2004) and oceanic current changes (Miller & Fairbanks, 1983) happened that might have offered
improved ecological conditions due to increased oceanic productivity resulting from the cooling of
ocean surface temperatures (Warheit, 1992) thus promoting seabird lineages diversification.
Although such a diverse group obviously present much differences between its various
members, commonly shared characteristics (Schreiber & Burger, 2002; Friesen et al., 2007a)
include mostly pelagic distribution during the non breeding season, marine foraging during
breeding, colonial breeding, either on isolated islands or on cliffs, late sexual maturity (with a large
interval of age at first reproduction, from 2 to 13 years; Jouventin & Dobson, 2002), low annual
fecundity, biparental care and long lives (e.g. Sagar & Warham, 1993 for a case in albatrosses).
Evolutionary speaking, seabirds have provided some of the most convincing cases of
sympatric speciation, due to temporal segregation of breeding populations (e.g., band-rumped storm
petrel Oceanodroam castro; Monteiro & Furness 1998) or extreme natal philopatry (Friesen et al.
1996, Friesen & Anderson, 1997; Congdon et al. 2000; Steeves et al. 2005). Cases of peripatric and
parapatric speciation have also been shown, e.g. in the white-capped albatross (Thalassarche steadi;
39
Abbott & Double, 2003) and nazca boobies (Sula granti; Friesen et al., 2002). Nevertheless, it is
now generally agreed that almost every speciation events are associated with some kind of
geographic separation (Fitzpatrick et al., 2009; Coyne & Orr, 2004) and allopatric speciation seems
to be the dominant mode of speciation in birds, including seabirds (Friesen et al., 2007a), with only
a mere 5% of speciation events compatible with sympatric scenario (Phillimore et al., 2008). Yet, in
many taxa species diagnostics is not straightforward due to the geographic isolation of their
populations (Coyne & Orr, 2004). This arises because phenotypic similarity may be maintained by
similar ecological conditions even when gene flow has ceased and DNA sequences have diverged
(Price, 2008).
II Procellariiformes
Thorough reviews on the ecology of Procellariiformes can be found in the two masterpieces
of Warham (1990, 1996) and Brooke (2004), and most material presented in this brief introduction
to these birds are assembled from these references.
Order Procellariiformes comprises albatrosses and petrels (Warham, 1990; Brooke, 2004). The term
petrel includes a wide range of species, belonging to three families, from the storm-petrel to the
diving-petrels and an assemblage made of gadfly petrels, shearwaters and allies. Albatrosses are
sometimes included within the general term petrel (Warham, 1990; Brooke, 2004). The main
feature characterising Procellariiformes from other seabirds are their tubular nostrils. Another
difference with other seabirds is their digestive track that lacks a crop, contrary to other birds.
Procellariiformes are probably the best representation of what is a seabird. Typically,
Procellariiformes are pelagic, long-lived species, breeding only once a year or every second year.
They are colonial breeding birds and lay only one egg that cannot be replaced if breeding fails,
although relaying has been documented in diving-petrels and storm-petrels (Warham, 1990). Both
sexes share responsibilities in incubation and chick rearing. All species exhibit high partner fidelity
between years, a feature that might be associated with the high degree of cooperation needed to
successfully rear a chick, the breeding period being longer than in other similar sized seabirds.
These birds are also slow to mature and mostly do not breed until several years of age. This slow
generation time renders the Procellariiformes particularly sensitive to adult mortality. All species
show colouration made of a combination of black, grey, white and brown. In terms of sizes, the
order Procellariiformes has the largest range in birds, from the Least storm-petrel (Halocyptena
microsoma) and its 20.5cm wing for 20.5g to the Southern royal albatross, wing length 69.8/66.6
cm (males/females) and 10.3/7.7 kg (Brooke, 2004).
40
II.1 Threats and Conservation
As shown by Fig. 1.8, seabirds are probably the fastest declining group of birds in the last 20
years, and this poor record is essentially driven by high declines in Procellariiformes populations.
Table 1.5 shows the percentage of species categorised in different conservation statuses for the four
families in the order (calculated from Birdlife International data; Birdlife International, 2010).
Overall, 45.7% of species are threatened in the order, and only 38.2% of species are considered as
Least Concern. Two families do not have any species in this last category, and only Hydrobatidae
have a majority of species categorised LC. Family Diomedeidae is the most seriously threatened
with a staggering 77.3% of its species being threatened! However, no species of albatross is
considered extinct by Birdlife International (2010). Contrary to Diomedeidae, in family
Procellariidae two species are recognised extinct by Birdlife International (2010), a Bulweria and a
Pseudobulweria that both used to breed on St Helena island in the Atlantic Ocean and that were
extirpated circa 1500 (Olson, 1975). Jamaica petrel (Pterodroma carribea), although not officially
classified as Extinct is very likely to have suffered the same fate than St Helena birds and the last
confirmed record dates from 1879. However, recent rediscovery of the formerly presumed extinct
Beck’s petrel (Pseudobulweria becki; Shirihai, 2008) proves that, maybe all hope should not be
abandoned for the Jamaica petrel. Additionally, Rando & Alcover (2008) reported the extinction of
a shearwater from the Canary Islands, Puffinus olsoni, thought to have disappeared later than 1270,
probably soon after the arrival of the first European in the Canary islands. Other shearwaters have
been reported as getting extinct following human colonisation of their breeding islands. These
include P. spelaeus (Holdaway & Worthy 1994) from New Zealand that probably disappeared due
to the introduction of Pacific rat (Rattus exulans) by Maoris, and P. parvus from Bermuda (Olson
2004) probably driven to extinction after human arrival in these islands in the 16th century. Other
potential examples of human induced extinction or extirpations can be found in the Pacific Ocean
islands (such as Easter Island or Ua Huka in the Marquesas; Steadman, 1995), where almost every
island colonisation by human has led to the extirpation or extinction of one Procellariidae
(Steadman, 2006). Nowadays, family Procellariidae is the second most threatened family within
Procellariiformes (Table 5) but the few examples cited above show that this family has suffered a
dramatic decline in the last few thousands of years following the progressive colonisation of their
remote breeding islands by humans.
41
Table 1.5 Percentage of Procellariiformes species in IUCN conservation categories
Diomedeidae
EX
0
CR
13.6
EN
27.3
VU
36.4
NT
22.7
LC
0
DD
0
Procellariidae
2.4
9.8
12.2
20.7
13.4
41.5
0
Pelecanoididae 0
0
25
0
75
0
0
Hydrobatidae
8.7
8.7
4.3
4.3
56.5
17.4
0
Overall
1.5
9.9
14.5
19.8
13.0
38.2
3.1
EX: Extinct; CR: Critically Endangered; EN: Endangered; VU: Vulnerable; NT: Near Threatened;
LC: Least Concern; DD: Data Deficient
Many threats affect seabirds and obviously Procellariiformes are no exception and many of
these taxa have significantly declined in the last century (Warham, 1990; Brooke, 2004).
Causes of decline of Procellariiformes population can be divided in two categories, causes on land
in the breeding/nesting habitat, and causes affecting the birds at sea, i.e. foraging habitat during
breeding season and pelagic habitat during non-breeding season. Using very different habitats
renders seabird sensitive to any change or threat that can affect one of these, thus all age classes can
be affected, from eggs and chicks in the nesting grounds, to immatures and adults, breeding or not,
at sea. In general, species become endangered due to habitat loss, over-harvest, invasive species,
pollution and diseases (Wilcove et al., 1998), to which for seabirds, we can add global climatic
change and fisheries by-catch.
Their breeding habitat being confined to small geographic regions, i.e. usually oceanic
islands and/or cliffs, any loss or damage to these restricted coastal or island environments can have
very negative consequences on Procellariiforme populations. Hence, habitat loss or damage, either
through direct anthropogenic destruction or more natural causes, is indeed an important threat to
their and seabirds’ survival in general (DeeBoersma et al., 2002). For instance, fire in breeding
colonies can have catastrophic effects on breeding success and adult survival, as exemplified by the
fire that affected Zino’s petrel (Pterodroma madeira) breeding colony in Madeira in August 2010,
killing several adults and 65% of this year chicks (Birdlife International, 2010). Human induced
decline or extinction is well documented on land and can be linked to either direct harvesting of
birds by people or indirect effect of environmental changes (Brooke, 2004; Steadman, 2006). Direct
harvesting of Procellariiformes for food or other use used to be a widespread practice described
worldwide (Warham, 1990; Brooke, 2004) and some species were driven close to extinction by
such harvesting (e.g. Short-tailed albatross, Phoebastria albatrus, Kuro-o et al., 2010). Habitat
42
degradation due to anthropogenic causes include the introduction on breeding grounds of herbivores
like goats, sheep, cattle or rabbits that can have very deleterious effect on petrel populations by
degrading the breeding habitat through tramping or by modifying vegetation cover and inducing
erosion. Habitat modification or destruction for urbanised areas or agriculture can also be a cause of
mortality in Procellariiformes, and mortality of juveniles due to artificial lights could contribute to
the demise of species already struggling to survive, such as happening the Mascarene petrel
(Pseudobulweria aterrima) in Réunion island or Newell’s shearwater (Puffinus newelli) in Hawaii
(LeCorre, 2003; Warham, 1996). However, light-induced deaths mainly affect young birds and
survival of long-lived species such as petrels is more affected by adult mortality than juvenile
mortality. The impact of light-induced mortality on species survival can therefore be controversial.
In addition to habitat degradation, another cause of seabird mortality while on land that have
probably had a much greater negative impact on petrel populations consists in predation by
introduced species (Brooke, 2004). Indeed, Procellariiformes being mostly adapted to oceanic
environment are rather clumsy on land making them easy preys for introduced predators such as
cats. Furthermore, because they evolved on isolated oceanic islands without predators,
Procellariiformes taxa did not developed escape or defensive behaviour, either as chicks or adults,
to respond to predation, rendering them very sensitive to this threat. Predators like cats and rats
have had a tremendous negative impact on Procellariiformes populations everywhere they have
been introduced. Fortunately, methods and know-how now exist that allow to clear islands from
these predators while limiting the impact on local fauna and flora (Donlan, 2008). Other predators
that have impacted Procellariiformes populations, as well as other fauna, on islands include pigs,
stoats and mongooses (Brooke, 2004).
While at sea seabirds in general and Procellariiformes in particular can be confronted to
several threats. Impact of fisheries bycatch is still, despite much improvement in the last years, very
important and threatens many populations of albatrosses and petrels (Grémillet & Boulinier, 2009).
Concomitant with bycatch, is the over-exploitation of fish resources by industrial fisheries that can
potentially affect many seabirds populations (Grémillet & Boulinier, 2009). Another threat for
seabirds at sea lies in marine pollution, either through chemicals, heavy metals, plastics or oil
products although its impacts are difficult to assess (Burger & Gochfeld, 2002). Finally, the most
recent threat to affect seabird communities consists in climate change (review in Grémillet &
Boulinier, 2009). Climate change can impact Procellariiformes on land by driving habitat changes
through a vegetation shift in breeding colonies or a shift in parasite loads or attacks (Robertson
1998). Climate change is also likely to induce a mismatch between Procellariiformes ranges and
their preys prompting lower reproductive success as has probably happened in the California
43
current (Veit et al., 1997). Furthermore, sea-level rise potentially accompanying climate change
could also destroy low lying atolls hosting colonies of some tropical Procellariiformes (Brooke,
2004). It was also pointed out by Grémillet & Boulinier (2009) that effects of climate change and
overfishing can hardly be considered separately and they could impact seabirds synergistically. To
such impacts these authors describe three potential responses of seabirds: (i) change their feeding
behaviour and preferences to survive within the same range; however all studies to date indicate a
negative effect of such change on seabird populations (reviewed in Grémillet & Boulinier, 2009).
Furthermore, in Procellariiformes, several studies showed the negative impact of global change on
demographic parameters of Procellariiformes (e.g. Grosbois & Thompson, 2005; Jenouvrier et al.,
2005); (ii) modify their range. It has been speculated that range shift in the non-breeding season in
Black (Oceanodroma melania) and Least storm-petrels was caused by change in oceanic
productivity in the central, equatorial Pacific caused by climate change (Ainley & Divoky, 2001;
Ainley et al., 2005); (iii) some species might be unable to change their feeding behaviour or
feeding/breeding range thus facing a direct risk of extinction linked to global change and
overfishing (Grémillet & Boulinier, 2009).
As a consequence of these threats, seabird communities in particular Procellariiformes have
experienced a regular decline and an increase in the number of threatened species over the last
decades. The challenge to conserve and protect Procellariiforme population worldwide and the
many threats sometimes acting synergistically renders the use of various tools necessary, from
mathematical and modelling tools (e.g. Dumont et al., 2010) to molecular tools.
II. 2 Systematics and Phylogenetics
The order comprises 131 species recognised by Birdlife International (Birdlife International,
2010). These species are grouped in four families, the Diomedeidae (Albatrosses; 22 species in four
genera), Hydobatidae (Storm Petrels; 23 species in eight genera), Procellariidae (the petrels,
shearwaters, prions, fulmars, and similar; 82 species in 14 genera), and the Pelecanoididae (the
diving petrels; four species in one genus). Family Procellariidae, the most speciose in the order, is
made of 14 genera, amongst which genus Pterodroma comprises 32 recognised species (Birdlife
International, 2010), 39% of the species in the family and 24.4% of all species in the order.
Traditionally, the phylogenetic relationships in this order have been difficult to establish and the
systematics of the group have been fluctuating over time. Brooke (2002) suggested that, because
most albatrosses and petrels are highly philopatric, i.e. return to breed in the colony where they
were born (sometimes just a few metres from the nest site where they hatched, or even in the very
44
same burrow!), and because a significant proportion breed on only one or a few islands, few birds
disperse to other colonies, which reduces the selection for plumage divergence or morphological
differentiation. As a result traditionally used characters such as colourations or morphology could
be poorer guide than usual to infer phylogenies. Alternative explanations to colouration similarities
can be linked to selective pressures at sea and might probably be linked to a combination of several
factors related to competition and predation (Bretagnolle, 1993). This has led to many cases of
phylogenetic uncertainties, including cryptic species with unclear phylogeographic histories
(Brooke, 2004) causing constant revision of their taxonomy.
With the rise of molecular phylogenetics in the 1990’s, it was hoped that phylogenetic
relationships and taxonomy in the order could be clarified. Indeed, several pieces of work brought
big changes to traditional phylogenies. First, Kuroda et al. (1990) using protein electrophoresis
produced a phylogeny showing the storm-petrels as the sister group of all other Procellariiformes.
The same year, in their major review of bird classification Sibley & Ahlquist provided the
phylogenetic tree shown in Fig. 1.9 by means of DNA hybridisation. Contrary to widely held views
considering Sphenisciformes as the sister Order to Procellariiformes (Ho et al., 1976; Saiff, 1976;
Mey et al., 2002; Mayr and Clarke, 2003; Ksepka et al., 2006), this tree placed Gavia as closest
relatives to Procellariiformes, and placed the storm-petrels outside a clade containing the three other
families, i.e. Diomedeidae, Procellariidae and Pelacanoididae. Although this work spurred some
criticism it was an interesting step forward that clarified a number of issues (Warham, 1996).
Subsequently, in a major work based on Cytochrome b, Nunn & Stanley (1998) provided one of the
most complete phylogeny of the order. It recovered the albatrosses, petrels and diving-petrels as
monophyletic as previously shown by Sibley & Ahlquist (1990). However, the fourth family,
Hydrobatidae, was found to be paraphyletic, although storm-petrel sub-families were found
monophyletic (Nunn & Stanley, 1998). Following this study, Kennedy & Page (2002) attempted to
build a supertree based on various phylogenetic trees from several studies. They also built a MP tree
based on a supermatrix approach that merged data from several cytb-based trees. Their analyses
recovered broadly the topology of Nunn & Stanley (1998), with family Hydrobatidae paraphyletic.
However, depending on the approach, placements of both storm-petrel clades were not similar, thus
raising questions about this family and its relationship with other Procellariiformes. Furthermore,
placement of some genera conflicted with other studies, e.g. Bulweria and Pseudobulweria,
(Bretagnolle et al., 1998). Finally, the last major attempt to reconstruct Procellariiformes
phylogeny, by use of cytb, by Pennhallurick & wink (2004) recovered the paraphyletic nature of
storm-petrels, which were placed along with Diomedeidae outside a clade containing
45
Pelecanoididae and Procellariidae. However, this last study was flawed with shortcomings (Rheindt
& Austin, 2005) and its results should be taken with caution.
In addition to these various attempts to reconstruct phylogenetic relationships at the order
level, several studies based on molecular tools also investigated relationships at the genus, species
or population level and prompted taxonomic revisions or the discovery of cryptic species or strong
genetic population structuring. Here are some examples.
In albatrosses, successive works from Nunn et al. (1996), Abbott & Double (2003), Cuthbert
et al. (2003) and Burg & Croxall (2004) have prompted the recognition of four genera and 22
species, from two genera and 14 species traditionally accepted before the study of Nunn et al.
(1996). It can be noted that, interestingly, some of the genera (such as Thalassarche and
Phoebastria) were “resurrected” (Nunn et al., 1996). In storm-petrels, several studies have
investigated phylogenetic and population relationships within the Band-rumped storm petrel
complex and revealed cryptic species and population structure (Friesen et al. 2007b, Smith &
Friesen 2007, Bolton et al. 2008). In family Procellariidae, phylogenetic uncertainties exist in
almost all genera, which were only partly explored. For example, in the shearwaters (genus
Puffinus) much debate exists surrounding the phylogenetic relationships within the Audubon/Little
shearwater (P. lherminieri/assimilis) complex. Different taxonomic treatments of this complex
provided from one to eight species and from seven to 19 subspecies depending on the authors
(review in Austin et al., 2004). In this major study, Austin et al. (2004) proposed five species, three
“ocean-based” (bailloni, lherminieri and assimilis) and two formerly misclassified (subalaris,
myrtae), and 14 “lower level” taxa, mostly corresponding to subspecies. In genus Macronectes,
long-standing debates concerning the relationships between the two recognised species, the nonmonophyly of one taxon and the potential importance of hybridisation existed. Recently, based on
two types of molecular markers, Techow et al. (2010) found support additional to morphological
and behavioural evidence, for the definitive recognition of two species recently diverged and still
presenting some degree of gene flow. Finally, Techow et al. (2009) confirmed the specific status of
spectacled petrel (Procellaria conspicillata) that was for a long time considered part of the wide
ranging White-chinned petrel (Procellaria aequinoctialis), by means of molecular evidence.
In genus Pterodroma, Browne et al. (1997) showed that populations of petrels from the Galapagos
and Hawaï deserved specific status based on a study using allozyme electrophoresis, thus separating
the former taxon in two species, one Critically Endangered (Galapagos petrel, Pterodroma
phaeopygia) and one Vulnerable (Hawaiian petrel, Pterodroma sandwichensis) (Birdlife
International, 2010). Subsequently Friesen et al. (2006) went further and analysed the population
46
structure of P. phaeopygia using microsatellite data and found a strong genetic structure between
population breeding on different islands within the archipelago. Based on these results they argued
that each island population should be treated as a management unit (MU) for the long-term
conservation of the species and its genetic diversity.
Figure 1.9. Phylogenetic tree from Sibley & Ahlquist (1990) showing relationships within Order
Procellariiformes (as in Kennedy & Page, 2002)
47
III Gadfly petrels
Family Procellariidae, which contains the most species and genera in Procellariiformes, is
the only one in the order that comprises taxa listed in IUCN Red List that were driven to extinction
by anthropogenic factors (Table 5) and it now presents several Critically Endangered species.
Within the family, gadfly petrels, including members of genera Pterodroma, Lugensa, Bulweria and
Pseudobulweria, is the largest group with 39 species Brooke (2004), 32 in Pterodroma, 1 in
Lugensa, 2 in Bulweria and 4 in Pseudobulweria.
Most gadfly petrel colouration are made of grey or black/brown on the upperparts, with
some white on the head and underparts, some dark taxa showing grey or black underparts (Warham,
1990; Brooke, 2004). Sub-Antarctic and temperate taxa tend to be nocturnal on land and breed in
burrows, while tropical species are more easily seen active above breeding grounds by day, with
activity peaks continuing for a while in the dark. While diurnal species more readily breed on the
surface than their nocturnal counterparts, they usually nest under bushes, rocks or tree roots
(Brooke, 2004, pers. obs.).
Because of the nocturnal and fossorial habits of many taxa in the group, it is believed that
their communication system (and presumably behavioural pre-isolating mechanism: Bretagnolle,
1996) almost never rely on visual cues, hence promoting cryptic and highly convergent plumages in
many taxa. Several studies have investigated the role that vocalisations can play on population
differentiation in these nocturnal species and used this character for systematics studies. For
instance, Bretagnolle (1990) used the calls of males and females together with other behavioural
data to confirm the validity of genus Halobaena (family Procellariidae) and its relationship with
Pachyptila. More recently, McKown (2008) investigated vocalisations within sub-genus cookilarias
(genus Pterodroma) to infer phylogenetic relationships between these closely related taxa.
In terms of taxonomy and phylogenetic relationships, gadfly petrels present uncertainties
and issues at all levels (Brooke, 2004), from genus level, e.g. long-standing debates regarding the
validity of genus Pseudobulweria or the validity of sub-genus cookilaria and the relationships
between its constituting taxa (Warham, 1990; McKown, 2008) to the species/subspecies level. At
this level, several complexes of taxa have long puzzled biologists and taxonomists. For instance, the
Pterodroma arminjoniana/neglecta/heraldica complex living in three ocean basins (Pacific, Indian
and Atlantic) and whose composition in terms of species has long been debated and still not
resolved due notably to recent colonisation of new islands in the Indian Ocean and to apparent
48
hybridisation (Brown, 2010). Another example consists in the Pterodroma mollis complex: this
complex whose taxa are distributed from sub-antarctic to tropical
latitudes in three oceans
(Atlantic, Indian and Austral), has over the years been described as made of one species and six or
four sub-species or three species with or without subspecies (review in Bretagnolle, 1995). The
latter finally considered the complex as made of two species, Pterodroma mollis itself in the
southern Atlantic, Indian and Austral oceans, and Pterodroma feae in Eastern Atlantic, breeding in
Cape Verde, Madeira and Desertas archipelagos. However, the latter taxon is now proving
incredibly complex and its taxonomic status is not yet settled.
In addition, Gadfly petrels, present a striking characteristic of endemicity. Over a quarter of
recognised species are endemic to one archipelago and most of the time of one island in the
archipelago (Brooke, 2004)! Only a handful of species are supposed to have recently colonised new
breeding grounds, such as the Trindade petrel (Pterodroma arminjoniana) supposed to have
recently colonised Round Island in the Indian Ocean from its native Trindade archipelago off the
coast of Brasil (Brown et al., 2010), or the Black-winged petrel (P. nigripennis) that have colonised
a few archipelagos in the New Zealand/ Australia region and Indian Ocean (Warham, 1990; Thiebot
et al., 2010). Furthermore, Steadman (1995) describes a very rich community of Procellariidae in all
archipelagos of the Pacific, of which certain forms have been extirpated or driven to extinction.
Thus the actual observed pattern of distribution and diversity of Procellariidae is biased and it must
be kept in mind that most likely, number of endemic species was much higher prior to the human
colonisation of the Pacific Ocean archipelagos and that the actual repartition of species does not
reflect the past distribution of many of them. That birds presenting such dispersal capabilities have,
during the course of evolution, produced so many forms endemic to one or a few islands raises
questions on the underlying causes, processes and patterns of population differentiation in the group
rendering further phylogenetic and phylogeographic investigations of this group necessary.
49
THESIS OBJECTIVES
Procellariiformes are at a cross point between (i) extensive conservation needs, (ii)
taxonomic uncertainty, and (iii) conflicting processes regarding population differentiation and
lineage divergence. Understanding the third point can greatly help tackling the first two. Genetics
and molecular phylogenetic offer tools that allow dealing with these issues.
The aim of this work is to describe phylogenetic relationships and taxonomy, as well as describe
population histories and timing of divergence in threatened gadfly petrels.
I used techniques related to population genetics, phylogeography and molecular phylogenetics to
reach this objective. New methods developed now allow fields that diverged due to methodological
and conceptual differences (Hey & Machado, 2003; Brito & Edwards, 2008) like phylogenetics and
phylogeography to become more integrated (Edwards, 2009).
Although we have seen through the diverse examples cited that already many studies have been
conducted in the fields of taxonomy and phylogeography in Procellariiformes and gadfly petrels,
the array of investigations needed in this group is so large that it ensures no redundancy between
these studies and the one undertaken in this PhD. Furthermore, the use of new methodologies
allows revisiting with a new perspective some issues not yet resolved in some taxa.
The investigations I undertook in phylogenetic and phylogeographic relationships within
gadfly petrels concerned two taxonomic levels:
1- the genus level: following the rediscovery of the Critically Endangered Beck’s petrel, the
opportunity arose to obtain for the first time samples from all extant taxa of genus Pseudobulweria.
This genus has for a long time been controversial and the phylogenetic relationships between its
taxa had never been investigated. It is one of the least known seabird genus and probably one of the
most endangered one, being composed of three Critically Endangered taxa and one Near Threatened
species. In addition, two extinct taxa have been described as belonging to Pseudobulweria, one in
the Atlantic Ocean and one in the Pacific Ocean. When confronting this pattern of vulnerability and
extinction in this genus, the question arises immediately about the underlying reasons that lead taxa
in this genus to be so much threatened or become extinct. Understanding these underlying processes
requires as the first step the description of the phylogenetic relationships within the genus and
between the genus and the other genera in the family/order. From these further investigations can
then be undertaken.
50
2- at the species/population level:
(i) first I explored the relationships and population histories of
three taxa breeding in Eastern Atlantic in three archipelagos, Madeira, Desertas and Cape Verde.
These taxa have been the topic of much debate and recent publications regarding their taxonomic
statuses, i.e. how many species do they represent, one, two or three? In this study, I aimed at
describing the population history of the three taxa and determining if they are currently exchanging
genes. I also attempted to propose a phylogeographic scenario describing the history of these taxa in
this region.
(ii) second I investigated the relationships between two taxa
living in Eastern Australia and New Caledonia and considered two subspecies of the Gould’s petrel,
Pterodroma leucoptera. This taxon is classified Vulnerable, the Australian subspecies is limited to a
few hundreds breeding pairs on two small islands, while the Caledonian subspecies restricted to
remote mountain ranges has an unknown number of breeding pairs, but it is supposed to probably
be over several thousands. The phylogenetic relationships and potential gene flow between these
two taxa remain unstudied but could have important conservation implications. Contrary to the
petrels of Eastern Atlantic, the relationships and statuses of the two Gould’s petrel populations seem
more settled and based on sound evidences. However, we might see that this not necessarily the
case. These taxa are part of a complex including another species (Collared petrel, Pterodroma
brevipes) and several potential undescribed taxa spread in various archipelagos in the Pacific Ocean
whose phylogenetic relationships are unclear.
These two cases thus represent two facets of taxonomy in gadfly petrels - in one case, the
taxonomy is debated and unsettled despite many investigations in the last three decades, while in
the other case there does not seem to be any taxonomic problem-, and were chosen to illustrate the
need of thorough investigations in the group to uncover underlying patterns and processes.
The thesis manuscript is based on papers submitted or in preparation for submission in scientific
journals and is composed of five chapters in addition to the introduction:
- Chapter 2 is a brief introduction to the techniques and tools used during this PhD;
- Chapter 3 describes the phylogenetic relationships within genus Pseudobulweria and the position
of this genus relative to other Procellariiformes; this manuscript has been submitted to the journal
Conservation Genetics;
51
- Chapter 4 investigates phylogenetic and phylogeographic relationships within three populations of
gadfly petrel of Eastern Atlantic Ocean; this manuscript is in preparation for submission;
- Chapter 5 is divided in two parts. Part 1 describes the population history and phylogeography of
Gould’s petrel subspecies in Australia and New Caledonia. Part 2 is a preliminary investigation of
phylogenetic relationships in the Gould’s/Collared petrel complex by means of the mitochondrial
gene used in birds for the Barcode of Life project, the cytochrome oxidase 1 (CO1) and based on
fragments of CO1 obtained from museum specimens; these two parts are made of manuscripts in
preparation for submission;
- Chapter 6 finally is a summary of the main findings and discussion of the latter in a broader
context and presents some perspectives to the work undertaken during this PhD.
52
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66
CHAPTER 2: GENERAL METHODS
2.I Mitochondrial versus nuclear DNA
For historical reasons (reviewed in Avise, 2000), as well as practical reasons, mtDNA has
long been the molecule of choice for phylogenetic and phylogeographic studies. mtDNA presents
the advantage of being present in high quantity in cells and to be relatively easy to sequence. It is
also acknowledged for its non-recombining properties and its high mutation rate (Avise et al. 1987,
Zink & Barrowclough 2008). In addition, because of its lower effective population size (Ne)
compared to nuclear DNA (approximately 4Ne) lineage sorting will occur faster for mtDNA than
for nucDNA thus allowing the detection of more recent vicariance events (Zink and Barrowclough,
2008; Fig. 2.1). Furthermore, until recently nuclear genes sequences were more difficult to obtain
than mtDNA thus rendering their use limited to a few studies (Lee & Edwards, 2008). mtDNa has
therefore been extensively used in the last 20 years and has allowed improving significantly the
knowledge of phylogenetic and phylogeographic patterns in all groups of living organisms.
However, in the last few years, several authors have questioned the supremacy of
mitochondrial DNA (mtDNA) in vertebrate phylogenetic and phylogeographic studies by using
nuclear genes in addition to mtDNA and uncovering conflicting results between the two types of
markers (Ferris et al., 1983; Shaw, 2002; Leaché & McGuire, 2006; Robertson et al., 2006; Peters
et al., 2007; Spinks & Shaffer, 2007; Good et al., 2008; Lee & Edwards, 2008; Spinks & Shaffer,
2009). It thus appears that inferences based on mtDNA alone should be regarded with caution
(Funk & Omland 2003; Chan & Levin 2005). Despite this line of evidence, respective merits of
nuclear versus mtDNA markers are disputed in regard to disentangling pattern and process in
evolution and speciation (see Zink & Barrowclough 2008, Edwards & Bensh 2008). For some
authors (e.g. Zink & Barrowclough, 2008), mtDNA remains the molecule of choice to determine
geographical or taxonomic limits of recently evolved groups, while for studies that wish to
investigate both patterns and processes of evolutionary and phylogeographic history it is necessary
to use both types of markers. The combination of these allows investigating gene flow, population
growth or phylogenetic relationships above the species level. However, it has been shown that it is
now possible to estimate species phylogenies from gene trees despite incomplete lineage sorting
(Carstens & Knowles, 2007).
67
Furthermore, because phylogenetics tries to describe the history and relationships of species
and populations, each of which made of many independently segregating genes whose gene trees
necessarily show some kind of differences between each others (Edwards, 2009), I follow this
author in his statement that the use of a sole gene mitochondrial gene tree to infer the relationships
of the taxa in which that gene tree is embedded should be questioned, and Petit & Excoffier (2009)
when they suggest not using single uniparentally inherited genome (such as mtDNA) for species
delimitations.
Zink & Barrowclough (2008) summarise potential outcomes of comparison of mtDNA and
nucDNA gene trees in four main scenarios.
(i) both mtDNA and nucDNA retrieve reciprocally monophyletic groups. If the two markers
retrieve the same groups, then there is a clear phylogenetic signal and taxa have probably diverged
long ago, giving enough time to nucDNA lineage to sort. It can also happen that the two markers
conflict in the monophyletic groups found. In that case, if only one nucDNA marker is used, then
the lineage history is unresolved; if however, several nuclear merkers are used and yield the same
grouping conflicting with mtDNA, then the latter, for some reason to identify, is an erroneous
indicator of lineage history.
(ii) mtDNA retrieve reciprocally monophyletic groups, but nucDNA do not support this
monophyly. In the case of closely related taxa recently diverged, this can be explained by the
inability for nucDNA to recover the split due to its longer coalescence time. Alternatively, this can
be explained by a male-mediated gene flow resulting in the failure of the mtDNA marker to reflect
the overall population structure.
(iii) mtDNA do not recover differentiation whereas nucDNA does. Such case can happen if
a selective sweep, by which an advantageous haplotype arises in one population and quickly
expand, replacing other haplotypes at that locus, occurred in the mtDNA genome. Irwin et al.
(2009) report the case of the yellowhammer (Emberiza citronella) and pine bunting (Emberiza
leucocephalos) in which mtDNA is very likely to have introgressed between them as a result of a
selective sweep. The result is a contradiction between the mitochondrial signal, indicating lack of
differentiation between the two taxa, and the nuclear signal, pointing towards a differentiation. In
this example, such hypothesis is supported partly by the important phenotypic differences. An
68
alternative scenario, by which the two taxa very recently diverged and reached nuclear and
phenotypic differentiation, contrary to mtDNA, but this is less likely.
(iv) both mtDNA and nucDNA fail to find reciprocally monophyletic groups. This can
happen when all populations exchange migrants thus sharing haplotypes. Alternatively, dispersal
has only recently ceased between geographically isolated populations and shared ancestral
polymorphisms remain at all markers.
As a result of what precedes, in this PhD work, all investigations have involved at least one
nuclear marker in addition to one or two mitochondrial genes. In cases when sequences were
available for only one mtDNA marker (CO1), caution was suggested relating to the interpretation of
results and conclusion drawns.
Figure 2.1. from Zink and Barrowclough, 2008. Capacity of different markers to detect vicariance
69
2.II Samples: origins and processing
Two types of samples were used in the course of this work: samples that I will qualify as
“fresh”, despite being sometimes collected on dead birds, and samples collected on museum
specimens.
2.II.1 Fresh samples
Samples of this type were collected by various fieldworkers around the world and consisted
of three types: blood, feathers, tissue.
Blood was collected from the veins on the leg or wing using microcapillaries and stored in 70%
ethanol then frozen at -20°C until processing, except during mail transfer from the collector lab to
the CEBC.
Feathers were plucked from live birds, either on the back, at the basis of the neck or on the
chest. Three to four feathers per bird were collected. Feathers were subsequently stored in 70%
ethanol and frozen when possible until processing.
Finally, tissue samples were collected on dead birds collected opportunistically in or around
the breeding colonies, or at sea. Again, these samples were stored in ethanol and frozen when
possible.
2.II.2 Museum samples
These were collected on museum specimens with extra care to avoid damaging the
specimen, and with the authorisation of the curator of the museum. A small piece of skin (about
1mm long) was cut from the web between the toes on one foot of the specimen.
When processing these samples, extra care was taken to avoid any potential contamination, i.e.
these were not processed along with fresh samples and DNA extraction and PCR preparation were
carried out separately, on different days and in separate rooms, following standard procedure used
in the Service de Systematique Moléculaire in the National Museum of Natural History in Paris.
70
2.II.3 DNA extraction
Total genomic DNA was extracted using the DNeasy Tissue Extraction Kit (Qiagen,
Valencia, CA, USA) following manufacturer’s instructions except that we increased the time of
proteinase digestion to 8h, overnight.
2.II.4 Mitochondrial and nuclear DNA amplification and sequencing
Cytochrome
Oxydase
1
gene
(CO1)
was
amplified
using
primers
F1B
5’-
AACCGATGACTATTYT-CAACC-3’ and R1B 5’-TACTACRTGYGARATGATTCC-3’, derived
from primers F1/R1 (Simon et al., 1994). The PCR consisted of 37 cycles following a hot start at
94°C and a 4 minutes initial denaturation step at 94°C. Cycles, i.e. 94°C for 30s, 51°C for 40s and
72°C for 50s, were completed by a final extension at 72°C for 5 minutes.
Cytochrome b gene was amplified using primers L14987 5’-TATTTCTGCTTGATGAAACT-3’
and H16025 5’-CTAGGGCTCCAATGATGGGGA-3’ (Jesus et al., 2009) and 40 PCR cycles
consisting of 30s at 94°C, 50s at 58°C, 50s at 72°C. These cycles followed a 4 minutes initial
denaturation step at 94°C and were completed by a final extension at 72°C for 5 minutes.
Primers
FIB-BI7U
5’-GGAGAAAACAGGACAATGACAATTCAC-3’
and
FIB-BI7L
5’-
TCCCCAGTAGTATCTGC-CATTAGGGTT-3’ (Prychitko & Moore, 1997) were used for Beta
Fibrinogene intron 7 (βFibint7) amplification. We ran thirty-nine PCR cycles consisting in 1 minute
at 94°C, 40s at 58°C and 50s at 72°C preceded by an initial denaturation step of 4 minutes at 94°C.
These cycles were followed by a 5 minutes final extension step at 72°C.
PCR procedure for cold shock domain containing E1 (CSDE1) and PAX interacting protein 1
(PAXIP1) introns followed protocols and used primers described in Kimball et al. (2009).
In addition, for samples collected on dead or old museum birds, DNA was degraded and
fragment sizes for amplification were mostly small (<200bp) and proved difficult to amplify and
sequence. I therefore used additional primers designed specifically for the taxa studied.
Amplification problems were mostly encountered with samples from genus Pseudobelweria.
Primers used are summarised in Table 2.1.
For all markers and all analyses, sequencing was conducted under BigDyeTM terminator
cycling conditions at the “Genoscope - Centre National de Séquençage”, France.
71
DNA sequences were aligned using CodonCode Aligner 3.0.3 (CodonCode Corporation, 2009) and
ClustalW (Thompson et al., 1994) as implemented in Mega version 4 (Tamura et al., 2007) and
checked by eye.
Table 2.1 Primers used in amplification of CO1, Cytb and βFibint7 genes in Pseudobulweria
petrels. All primers were designed for this study
PRIMERS CO1
F1B 5’AACCGATGACTATTYTCAACC-3’
R1B 5’TACTACRTGYGARATGATTCC-3’
R141 5’AGCATGGGCGGTGACGATT-3’
F78 5’ACTTATTCGTGCAGAACTTGGTC-3’
R208 5’AGGGGGACTAGTCAGTTTCC-3’
F150 5’CGCCCATGCTTTCGTAATAATTT-3’
R254 5’AGCTTATGTTGTTTATACGTGGGA3’
F207 5’TGGAAACTGACTAGTCCCCCT-3’
R323 5’ACCTGCTCCTGCTTCTACGG-3’
F288 5’ACCTCCGTCCTTCCTCCTAT-3’
R416 5’CCTGCCAGGTGGAGGGAGA-3’
F377 5’ATCTAGCCCATGCCGGAGC-3’
R502 5’AAGGGGGTTTGGTACTGTGA-3’
F453 5’GGCAATCAACTTCATTACAACAGC3’
R581 5’AGCATGGTGATGCCTGCGG-3’
F537 5’ACTCATCACTGCCGTCCTAC-3’
R678 5’TGGGTGGCCGAAGAATCAG-3’
F642 5’TGGCGGAGGAGACCCAGTC-3’
PRIMERS CYTB
F13 5'-CTTCGAAAGTCCCACCCCT-3'
R142 5'-TGGCTAGTAGGAGGCCGGT-3'
F103 5'-GGATCCCTCCTAGGCATCTGT-3'
R202 5'-ACATTTCGGCAGGTGTGAGC-3'
F167 5'-ACACAGCTGACACAACCTTAGC3'
R297 5'-CCGTAGTAGAATCCCCGTCCG-3'
F249 5'-ACATGCAAACGGAGCCTCA-3'
R369 5'-ACGAAGGCAGTTGCCATAAGA-3'
F314 5'ACGGCTCCTACCTATACAAAGAG-3'
R457 5'-TGGCCAATGTAGGGGATGGC-3'
F418 5'-TCATTCTGAGGTGCGACAGTCA3'
R529 5'AGTGTAGGGCGAAGAATCGGGT-3'
F473 5'GCCAAACCCTTGTAGAATGAGCC-3'
R595 5'-AGCCAGATTCGTGGAGGAAGGT3'
F553 5'CTCCTACCTTTTGCAATCACAGGA-3'
R674 5'AGCCTAGGATATCTTTTAAGGTGA-3'
F630 5'-TGGTGTCGTATCAAACTGCGA-3'
R758 5'CGCTGGAGTAAAGTTTTCTGGGT-3'
F711 5'-TCTCCCACTAACAGCCCTAGCT3'
R836 5'-GGAATTGAGCGTAGGATGGCGT3'
F793 5'ACACCTCCCCATATTAAACCAGA-3'
R915 5'-TGGCTTTATGGAGGAATGGA-3'
F885 5'-AGCTGCCTCAGTATTGATCCTA3'
R1012 5'-ACTGGCTGGCTGCCTACTCA-3'
PRIMERS ΒFIBINT7
F21 5'-TGACAATTCACAATGGCATGTTCT3'
R158 5'-ACCACGACATGCTGTGAAAACT-3'
F72 5'-GATGGTACGTACTTGCATTAGACA3'
R210 5'-TGCATGGACGTTCAGCTGGT-3'
F160 5'-TTTTCACAGCATGTCGTGGT-3'
R277 5'-ACTTGGCTGTGGAGCAGCA-3'
F244 5'-GCCAAGGGCAGGTAAAACT-3'
R380 5'-TGCCACCATCAGTCTCTGACA-3'
F347 5'ACAAATCAGCAAATCTGGATGCAA-3'
R462 5'-CCTGTCTCTTTCCTCAGGACCCA-3'
F411 5'-CCACTGACTTGCTTAAGTAGGAA3'
R522 5'-ACAATTGAGCTCCTGTCTTCTG-3'
F476 5'-AGAGACAGGTAGCATGTCCTATT3'
R638 5'-TGAGAACTGTACATCTTCCCCAA3'
F574 5'-ACTATGTGCTATGTCTTTCTCT-3'
R722 5'-GTCTACCGATTGTAGTCTAACTT-3'
F641 5'GGGAAGATGTACAGTTCTCATTGT-3'
R796 5'-GCACTTGGAAGGTGAAGCAGC-3'
F756 5'-TCCGAAAGAGATGCAGCTAAA-3'
R852 5'-AAATCCTCCCTGAACTTTCTGT-3'
F807 5'-TTCCAAGTGCACTGTGTAGCA-3'
R938 5'-GAGTGGCAGATGAACTGTAAGCA3'
F900 5'-TCAGTACAGGGGCAGGTGTA-3'
R1048 5'-GGGTTGGCTGAGTGGCAGC-3'
72
2.III Estimation of gene flow, population divergence time and
effective population sizes
Two extreme scenarios have traditionally been the basis of evolutionary models
investigating genetic comparisons in different population, an equilibrium migration scenario, and
isolation scenario. In the former, populations are supposed to have exchanged migrants at a constant
rate for an infinite period of time. In the latter, populations are considered as having evolved
independently without gene flow since the split from the common ancestral population.
The problem with measures of genetic distance such as FST is that they cannot really
differentiate between the two scenarios of equilibrium migration or isolation. In the case of FST, a
low value can mean either a high gene flow over time between the populations studied, or a recent
cessation of gene flow without enough time for drift and/or selection to accumulate differences
between the populations. Isolation with Migration (IM) models bring a great improvement by
allowing both cases to be considered and distinguished. IM can therefore be of great use for
conservation biology by allowing differentiating populations that are still connected by migration
and gene flow, from those that are isolated and are potential incipient species (or at least some kind
of independent management units). This model includes population-size parameters for an ancestral
and two descendant populations, as well as a splitting time term, and two parameters for gene
exchange (Fig. 2.2).
IM models assume selective neutrality, constant population size and no recombination. Early
programs implementing these models, such as IMa (Hey & Nielsen, 2007), allowed investigating
relationships between only two populations and had several limitations: there should not be other
populations more closely related to the sampled populations that these were from each other, nor
should there be unsampled populations exchanging genes with either of the study populations or the
ancestral population. However, the recently released IMa2 (Hey, 2010) circumvents these
limitations by allowing investigating relationships between two and ten populations (Fig. 2.3). The
main difficulty when running programs such as IMa or IMa2 is to estimate if the output from
markov chain Monte Carlo simulations describes patterns that can be close to reality. Thus many
precautions need be taken, as described in the documentation, including running simulations at least
three or four times. The first time serves to assess the relevance of chosen input parameters and to
refine them, while other runs confirm or not that independent runs give equivalent results.
73
In the work presented in this manuscript I used the program IMa2 to estimate migration
rates, dates of divergence and population sizes (at time of divergence due to the assumption of
constant population size of the IM model) between populations of gadfly petrels living in the
Eastern Atlantic Ocean and of Gould’s petrel of Australia and New Caledonia.
Figure 2.2 From Pinho & Hey, 2010. (A) Isolation model with three population size parameters and
a splitting time parameter. Effective population sizes (N), migration rates per gene copy per
generation (M), and time since population splitting (T) are all scaled by the mutation rate (u) of the
genes being studied. Effective population sizes are assumed to be constant. The ancestral population
is assumed to have been present indefinitely back into the past. (B) The isolation-with-migration
(IM) model includes two parameters for gene flow between the sampled populations. Gene flow
rates are assumed to be constant over the time period since population splitting.
A
B
Descendant populations (from
which data are collected)
Present
M2.u
4N1u
4N2u
4N1u
M1.u
-1
-1
4N2u
Splitting time
4NAu
4NAu
Ancestral
population
Past
74
Figure 2.3 From Hey, 2010. Isolation with Migration model with 3 populations. Sampled
populations 1 and 2 are more recently diverged from each other than either are with respect to
population 3
75
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79
CHAPTER 3: THE COMPLETE PHYLOGENY OF
PSEUDOBULWERIA THE MOST ENDANGERED SEABIRD
GENUS: SYSTEMATICS, SPECIES STATUS AND
CONSERVATION IMPLICATIONS
Gangloff B, Shirihai H., Watling D., Cruaud C., Couloux A., Tillier A., Pasquet E. and Bretagnolle
V., in revision, Conservation Genetics
Abstract
Pseudobulweria is one of the least known and most endangered of all seabird genera. It comprises
five or six taxa, of which two are extinct, three are critically endangered and one is near threatened.
Phylogenetic relationships between these taxa and position of the genus in Order Procellariiformes
have never been studied, and the taxonomic status of several taxa remains unsettled. Conservation
management of Pseudobulweria taxa will be enhanced if these uncertainties are resolved. We used
a multilocus gene tree approach with two mitochondrial DNA markers (CytochromeOxidase
subunit 1 and Cytochrome b gene) and one nuclear intron (βFibrinogen intron 7) to investigate
phylogenetic relationships within the genus using sequences from all taxa. We combined gene trees
to estimate a phylogeny of the genus using a multispecies coalescent methodology. We confirmed
the link between Pseudobulweria and a clade comprising Puffinus and Bulweria genera. The Fiji
petrel’s status within the genus is confirmed, as is the specific status of newly rediscovered Beck’s
petrel. Maintenance of the two sub-species of Tahiti petrel is not supported. Discovering the
breeding grounds of all taxa is the key for their conservation, which is vital to both the marine and
fragile insular tropical ecosystems where Pseudobulweria are apical predators.
Keywords
Conservation; Procellariiformes; Pseudobulweria; seabird; CO1; Cytochrome b; βFibrinogen
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Introduction
Specific status is the most recognised unit used by conservation organisations and
international and/or national government agencies to determine conservation policy and actions
(Drummond et al. 2009; Farrier et al. 2007; Posingham et al. 2002). As resources (both human and
financial) are limited, all conservation organisations need access to the most accurate systematic
and taxonomic analyses of all taxa together with their evolutionary history. Seabirds, and more
particularly petrels (Order Procellariiformes), have traditionally been difficult to study because
much of their life is spent at sea coupled with the remoteness of their breeding grounds, usually on
isolated oceanic islands (Brooke 2004; Friesen et al. 2007). In consequence, even today, detailed
knowledge of their taxonomy, phylogeography and conservation status is scant and controversial
(Brooke 2004). Though there has been considerable improvement in the past twenty years through
the development of molecular biology, many groups in this Order remain poorly known, the
systematics of many taxa and even genera is still debated, and their phylogenetic relationships
unsettled (Brooke 2004; Mayr 2009). This is particularly disturbing at a time where most
Procellariiformes taxa are in decline and to some degree threatened with extinction (IUCN 2010).
Genus Pseudobulweria (family Procellaridae) is a good example. Of six taxa that compose the
genus, two are extinct, three are critically endangered and one is near-threatened (IUCN 2010),
making Pseudobulweria the most threatened seabird genus in the world. In addition, it remains one
of the least known and most controversial petrel genera (Bretagnolle et al. 1998; Brooke 2004;
Shirihai 2008). Indeed, the mere existence and validity of the genus remains controversial, its
relationships with other petrels are contradictory, the number of species is debated, and species
relationships have never been investigated.
First proposed by Mathews (1936) for the Fiji Petrel (P. macgillivrayi), the genus was later
merged within genus Pterodroma (Jouanin & Mougin 1979; Del Hoyo et al. 1992; Warham 1990)
before being reinstated (Imber 1985; Sibley & Monroe 1990). Based on molecular data from only
two taxa of the genus, Bretagnolle et al. (1998) confirmed its validity. Yet, its position
Procellariiformes continues to be debated according to four recent phylogenetic analyses.
Bretagnolle et al. (1998) found that Pseudobulweria was closely related to genus Puffinus (and next
to Bulweria and Procellaria) rather than Pterodroma, but did not analyse Pachyptila. Nunn &
Staley (1998) also found that Bulweria was closest to Procellaria and next to Puffinus, though they
did not include any Pseudobulweria in their analysis. Subsequently, Kennedy & Page (2002) using
a supertree approach to reconstruct the Procellariiformes phylogeny with partial trees from various
81
studies (including the two former ones) concluded that Pseudobulweria was closely related to
Pachyptila and Halobaena, and not to Procellaria, Puffinus and Bulweria, in contradiction with
Bretagnolle et al. (1998). Then, Penhallurick & Wink (2004), used Cytb as Nunn & Stanley (1998),
to analyse the taxonomy in this Order (note critical views in Rheindt & Austin 2005), but did not
include sequences from Bretagnolle et al. (1998) in their phylogenetic analyses. These authors
found that Procellaria/Bulweria were a sister clade of the Pachyptila/Halobaena clade. These
results are therefore clearly contradictory and call for further analyses.
Furthermore, the number of species within the genus is still uncertain. Five Pseudobulweria
taxa (four species, of which one has two subspecies) are currently known and a further one still
remains to be named (Worthy & Tennyson 2004). The phylogenetic relationships between these are
unknown (Brooke 2004; Shirihai et al. 2009) and even the precise number of species within the
genus is not settled. The extinct taxon P. rupinarum is known only from St Helena Island in the
Atlantic Ocean, where it was numerous, and was probably extirpated after 1502 (Olson 1975). From
bone remains used to describe the taxon, it appeared to be slightly larger than Mascarene petrel P.
aterrima. Another extinct taxon was breeding in the southern Tuamotu archipelago, Pacific Ocean,
being apparently very abundant, and was the size of Beck’s P. becki and Fiji petrels (Worthy &
Tennyson 2004). All surviving taxa now live in the Indian and Pacific Oceans (Fig. 3.1) and
breeding colonies are unknown for all but Tahiti petrel (P. rostrata). Until recently, three taxa were
known by no more than two (Beck’s, and Fiji petrels) and seven (Mascarene petrel) specimens
(Attié et al. 1997; Bretagnolle et al. 1998; Shirihai 2008; Shirihai et al. 2009). Hence, given the
paucity of data and poor number of specimens held in museums, systematic studies on this genus
were so far mostly based on scant morphological data and Tahiti petrel is the only taxon that has
been studied in some details (Villard et al. 2006). Furthermore, phylogenetic relationships between
taxa have been investigated solely between Tahiti and Mascarene petrels, the validity of the two P.
rostrata subspecies still being questioned (Bretagnolle et al. 1998; Villard et al. 2006) and the
phylogenetics of Beck’s and Fiji petrels having never been investigated. In addition, the taxonomic
status of becki is still uncertain.
The recent rediscovery of Beck’s petrel (Shirihai 2008) and the first observation at sea of Fiji petrel
(Shirihai et al. 2009) threw new light on the conservation status of this genus. In this paper we use
three different genes obtained from all extant taxa in order to investigate species limits and species
validity, species relationships, genus monophyly and genus position within the other petrels. In
particular we use newly developed species tree inference tools based on Bayesian statistics and
multispecies coalescent theory (Heled & Drummond 2010; Liu et al. 2009a, 2009b; O’Meara 2010;
Yang & Rannala 2010). As even the most probable gene trees topologies are not necessarily
congruent with species trees (Degnan & Rosenberg 2009; Nichols 2001), as seen in pines (Syring et
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al. 2007), grasshoppers (Carstens & Knowles 2007), finches (Jennings & Edwards 2005) or
hominids (Ebersberger et al. 2007), implementation of such multilocus approaches allows inference
of phylogenies when there are conflicting branching patterns between different genes (Degnan &
Rosenberg 2009; Heled & Drummond 2010).
Figure 3.1 Repartition of Pseudobulweria taxa, including extinct St Helena petrel P. rupinarum
Material and methods
Samples
Table 3.1 summarises the origin of Pseudobulweria samples used with all three genes
sequenced. Tissue samples were collected on dead birds, apart from birds from New Caledonia that
were alive and released after being bled. For the two type specimens of P. becki and for two
specimens of P. aterrima, a 1mm piece of skin from the palm was collected without damaging the
specimen. Total genomic DNA was extracted using the DNeasy Tissue Extraction Kit (Qiagen,
Valencia, CA, USA) following manufacturer’s instructions except that we increased the time of
proteinase digestion to 2h. Since samples were mostly collected on dead or old museum birds, DNA
83
was degraded and fragment sizes for amplification were mostly small (<200bp) and proved difficult
to amplify and sequence. Thus we failed to obtain useable gene sequences for some samples, and
for others we could not obtain whole gene sequences. We sequenced two mitochondrial genes and
one nuclear intron. Primers used for sequencing are shown in Table 3.2.
Table 3.1 Pseudobulweria taxa, identifying codes, origins, and tissue sources analyzed. Museum
samples came from Paris National Museum of Natural History (MNHN), New York American
Museum of Natural History (AMNH), Geneva Natural History Museum (MHNG), Fiji Museum
(FM). Sample BB73 was collected by HS on a dead juvenile bird later deposited in the British
Museum of Natural History (BMNH).
Taxon
Code
Locality
Tissu source
Internal Id
P. aterrima
MNHN 1995-165
Reunion Island
muscle
B01
P. aterrima
MNHN 1970-102
Reunion Island
Skin (from palm)
BB69
P. becki
AMNH 235376
Bismarck sea
Skin (from palm)
BB72
P. becki
BMNH 2008-1-1
New Ireland
Leaver
BB73
P. macgillivrayi
NA
Gau Island, Fiji
Neck tissue
BB74
P. macgillivrayi
NA
Gau Island, Fiji
Neck tissue
BB75
P. rostrata rostrata
GenBank U704821
Gambiers
Blood
B15
MHNG P08-30
Marquesas
Muscle
BI46
MHNG P08-31
Marquesas
Muscle
BI47
MHNG P08-32
Marquesas
Feather
BI48
NA
Tahiti
Blood
BB86
FM 170141
Vatuira, Fiji
Muscle
BD02
P. rostrata trouessarti
GenBank U704931
New Caledonia
Blood
B02
NA
New Caledonia
Feather
BB84
NA
New Caledonia
Feather
BB85
NA
New Caledonia
Feather
1
GenBank accession number refers to Cytb sequence from Bretagnolle et al. (1998)
BD04
Cytochrome Oxydase 1 gene (CO1) was amplified using PCR consisting of 37 cycles
following a hot start at 94°C and a 4 minutes initial denaturation step at 94°C. Cycles, i.e. 94°C for
30s, 55°C for 40s and 72°C for 50s, were completed by a final extension at 72°C for 5 minutes.
Cytochrome b gene was amplified with 40 PCR cycles consisting of 30s at 94°C, 50s at
58°C, 50s at 72°C. These cycles followed a 4 minutes initial denaturation step at 94°C and were
completed by a final extension at 72°C for 5 minutes.
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Primers FIB-BI7U 5’-GGAGAAAACAGGACAATGACAATTCAC-3’ and FIB-BI7L 5’TCCCCAGTAGTATCTGC-CATTAGGGTT-3’ (Prychitko & Moore, 1997) were used along with
other primers specifically designed (Table 3.2) for Beta Fibrinogene intron 7 (βFibint7)
amplification. We ran thirty-nine PCR cycles consisting in 1 minute at 94°C, 40s at 58°C and 50s at
72°C preceded by an initial denaturation step of 4 minutes at 94°C. These cycles were followed by
a 5 minutes final extension step at 72°C.
For all genes, sequencing was conducted under BigDyeTM terminator cycling conditions at
the “Genoscope - Centre National de Séquençage”, France. DNA sequences were aligned using
CodonCode Aligner 3.0.3 (CodonCode Corporation 2009) and ClustalW (Thompson et al. 1994) as
implemented in Mega version 4 (Tamura et al, 2007) and checked by eye.
Table 3.2 Primers used in amplification of CO1, Cytb and βFibint7 genes in Pseudobulweria
petrels. All primers were designed for this study, except for L14987/H16025 from Jesus et al.
(2009)
Primers CO1
F1B 5’-AACCGATGACTATTYTCAACC3’
R1B 5’TACTACRTGYGARATGATTCC-3’
R141 5’-AGCATGGGCGGTGACGATT3’
F78 5’ACTTATTCGTGCAGAACTTGGTC-3’
R208 5’-AGGGGGACTAGTCAGTTTCC3’
F150 5’CGCCCATGCTTTCGTAATAATTT-3’
R254 5’AGCTTATGTTGTTTATACGTGGGA-3’
F207 5’TGGAAACTGACTAGTCCCCCT-3’
R323 5’-ACCTGCTCCTGCTTCTACGG3’
F288 5’-ACCTCCGTCCTTCCTCCTAT-3’
R416 5’-CCTGCCAGGTGGAGGGAGA3’
F377 5’-ATCTAGCCCATGCCGGAGC-3’
R502 5’-AAGGGGGTTTGGTACTGTGA3’
F453 5’GGCAATCAACTTCATTACAACAGC-3’
R581 5’-AGCATGGTGATGCCTGCGG3’
F537 5’-ACTCATCACTGCCGTCCTAC3’
R678 5’-TGGGTGGCCGAAGAATCAG3’
F642 5’-TGGCGGAGGAGACCCAGTC3’
Primers Cytb
L14987 5’-TATTTCTGCTTGATGAAACT-3’
H16025 5’-CTAGGGCTCCAATGATGGGGA-3’
F13 5'-CTTCGAAAGTCCCACCCCT-3'
R142 5'-TGGCTAGTAGGAGGCCGGT-3'
F103 5'-GGATCCCTCCTAGGCATCTGT-3'
R202 5'-ACATTTCGGCAGGTGTGAGC-3'
F167 5'-ACACAGCTGACACAACCTTAGC-3'
R297 5'-CCGTAGTAGAATCCCCGTCCG-3'
F249 5'-ACATGCAAACGGAGCCTCA-3'
R369 5'-ACGAAGGCAGTTGCCATAAGA-3'
F314 5'-ACGGCTCCTACCTATACAAAGAG-3'
R457 5'-TGGCCAATGTAGGGGATGGC-3'
F418 5'-TCATTCTGAGGTGCGACAGTCA-3'
R529 5'-AGTGTAGGGCGAAGAATCGGGT-3'
F473 5'-GCCAAACCCTTGTAGAATGAGCC-3'
R595 5'-AGCCAGATTCGTGGAGGAAGGT-3'
F553 5'-CTCCTACCTTTTGCAATCACAGGA-3'
R674 5'-AGCCTAGGATATCTTTTAAGGTGA-3'
F630 5'-TGGTGTCGTATCAAACTGCGA-3'
R758 5'-CGCTGGAGTAAAGTTTTCTGGGT-3'
F711 5'-TCTCCCACTAACAGCCCTAGCT-3'
R836 5'-GGAATTGAGCGTAGGATGGCGT-3'
F793 5'-ACACCTCCCCATATTAAACCAGA-3'
R915 5'-TGGCTTTATGGAGGAATGGA-3'
F885 5'-AGCTGCCTCAGTATTGATCCTA-3'
R1012 5'-ACTGGCTGGCTGCCTACTCA-3'
Primers βFibint7
F21 5'-TGACAATTCACAATGGCATGTTCT-3'
R158 5'-ACCACGACATGCTGTGAAAACT-3'
F72 5'-GATGGTACGTACTTGCATTAGACA-3'
R210 5'-TGCATGGACGTTCAGCTGGT-3'
F160 5'-TTTTCACAGCATGTCGTGGT-3'
R277 5'-ACTTGGCTGTGGAGCAGCA-3'
F244 5'-GCCAAGGGCAGGTAAAACT-3'
R380 5'-TGCCACCATCAGTCTCTGACA-3'
F347 5'-ACAAATCAGCAAATCTGGATGCAA-3'
R462 5'-CCTGTCTCTTTCCTCAGGACCCA-3'
F411 5'-CCACTGACTTGCTTAAGTAGGAA-3'
R522 5'-ACAATTGAGCTCCTGTCTTCTG-3'
F476 5'-AGAGACAGGTAGCATGTCCTATT-3'
R638 5'-TGAGAACTGTACATCTTCCCCAA-3'
F574 5'-ACTATGTGCTATGTCTTTCTCT-3'
R722 5'-GTCTACCGATTGTAGTCTAACTT-3'
F641 5'-GGGAAGATGTACAGTTCTCATTGT-3'
R796 5'-GCACTTGGAAGGTGAAGCAGC-3'
F756 5'-TCCGAAAGAGATGCAGCTAAA-3'
R852 5'-AAATCCTCCCTGAACTTTCTGT-3'
F807 5'-TTCCAAGTGCACTGTGTAGCA-3'
R938 5'-GAGTGGCAGATGAACTGTAAGCA-3'
F900 5'-TCAGTACAGGGGCAGGTGTA-3'
R1048 5'-GGGTTGGCTGAGTGGCAGC-3'
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Phylogenetic analyses
Genetic distances, Haplotype network and Gene trees inference
For the three genes, corrected genetic distances (K2P) between taxa were calculated with
Mega v.4 (Tamura et al. 2007) using the pairwise deletion option to allow for comparison of
complete gene sequences with sequences in which fragments were missing due to poor DNA
quality.
Since we were able to obtain additional sequences from P. rostrata trouessarti from New Caledonia
with CO1 (for a total Tahiti petrel sample size of 68), we investigated the relationship between the
specimens belonging to the two recognised subspecies of Tahiti petrel, P. rostrata rostrata and P.
rostrata trouessarti (hereafter termed rostrata and trouessarti respectively). We built a median-
joining haplotype network using the software Networks v4.5.1 (Bandlet et al. 1999; Fluxus
Technology Ltd, 2009) after identifying haplotypes with DnaSP v5 (Librado & Rozas 2009), using
all sequences of Tahiti petrel and two sequences of Beck’s petrel.
For each of the three loci, phylogenetic relationships were estimated using Bayesian Markov Chain
Monte Carlo (MCMC) phylogenetic analyses with MrBayes v3.1.2 (Huelsenbeck & Ronquist 2001;
Ronquist & Huelsenbeck 2003). jModelTest v0.1.1 (Guindon & Gascuel 2003; Posada 2008) was
used to evaluate the fit to the data of 24 models of nucleotide substitution implemented in MrBayes.
We used Bayesian Information Criterion (BIC) to evaluate which model best fitted our data
(Sullivan & Joyce 2005). For all Bayesian analyses, default priors of MrBayes 3.1.2 were used for
MCMC parameters. We used three heated chains and one cold chain for all analyses and runs were
started with random trees. Two independent MCMC runs were conducted with 4.106 generations
each. Trees and parameters were sampled every 100 generations. Standard deviations of split
frequencies were used to assess stationarity (cut off value of 1% was used), with the average
standard deviation of split frequencies expected to approach zero when the two runs converge onto
stationarity distribution (Ronquist et al. 2005). Additionally the potential scale reduction factor
should approach one when runs converge. For each run the first 25% were discarded as burn-in.
Since order Sphenisciformes is supposed to be the closest relative to Procellariiformes (Brooke
2004) and because we could obtain sequences from all three genes on GenBank for this taxon, we
used Humboldt penguin Spheniscus humboldti as an outgroup to root the gene trees. Since the
availability of sequences from different taxa was not homogeneous between genes in GenBank
(Cytb sequences’ availability far exceeding CO1 or βFibint7 sequences’ availability), we could not
gather the same data sets for the three genes. Thus, the data set used for βFibint7 was smaller, with
five less genera than for the mitochondrial markers. Used sequences’ accession numbers are shown
86
in Appendix 1, Table 3. For clarity, we built CO1 phylogenetic tree with a limited number of
sequences corresponding to individuals sequenced with the two other markers, and not with all 68
sequences at our disposal. Analyses were also conducted with the complete CO1 data set to check
for potentially spurious results (data not shown).
Species tree inference
In “species tree”, we here use “species” following the terminology of Heled & Drummond
(2010), i.e. it is not necessarily referring to the taxonomic rank, but designates any group of
individuals that have diverged sufficiently to no longer have breeding history with individuals
outside that group, and thus it can include taxonomic rank or any “diverging population structure”.
We found different placements of genera, and variations in taxa positions within the
Pseudobulweria clade in gene trees although incongruent pattern were mostly unsupported
(posterior probabilities < 95%; see results). Potential gene trees incongruences with species trees
have been known for decades (Pamilo & Nei 1988). A common practice to avoid the problem
consists in concatenating data assuming that all data have evolved under a single evolutionary tree.
However, this method was recently shown to result potentially in support of incorrect species trees
(Kubatko & Degnan 2007). Here we used the *BEAST methodology (Heled & Drummond 2010) as
applied in the software BEAST v1.5.4 (Drummond & Rambaut 2007) to reconstruct
Pseudobulweria species tree.
In order to precisely identify the phylogenetic relationships of Procellariiformes genera, we
ran an analysis with all sequences used previously to build phylogenetic gene trees with MrBayes,
first with both mitochondrial genes, then with these two markers and βFibint7 on a reduced dataset
to confirm results from the mitochondrial analysis. We specified genera (Pseudobulweria, Puffinus,
Fulmarus, Pterodroma etc.) as “units” to build the species tree. The analysis was run with unlinked
substitution and clock models and unlinked trees. For each gene, we used a relaxed clock model
with an uncorrelated lognormal distribution of the substitution rate with a fix mean value of 0.794 ±
0.115% per million year for CO1 (Pereira & Baker 2006) and 1.89 ± 0.35% (obtained for
Procellariiformes) for Cytb (Weir & Schlutter 2008). Since βFibint7 lacks a well calibrated
substitution rate, we did not enter this parameter. We used HKY model, with a discrete
approximation of the gamma-distributed rate of heterogeneity (four rate categories, Yang 1994) for
CO1 and βFibint7, and a GTR+G model for Cytb following jModeltest analysis. We specified a
Yule process species tree prior under a continuous population size model. The analysis was run for
200.106 generations. The run was sampled every 1000 generation, with the initial 50,000 steps
being discarded as burn in. We used the software TRACER v1.5 (Drummond & Rambaut 2007) to
87
visualize the results of the run as well as for checking effective sample size of each parameter.
Second, we investigated the inter-taxa species tree within Pseudobulweria with the three genes
using only Pseudobulweria sequences. The same specifications than the previous analysis were
used.
All *Beast analyses were run on the BioHPC compute cluster at the Cornell University
Computational Biological Service Unit (http://cbsuapps.tc.cornell.edu)
Results
Sequencing of Pseudobulweria samples provided 1140bp sequences for Cytb, 736bp CO1
sequences and sequences of 993bp for βFibint7. Two samples failed to provide sequences in all
three genes, i.e. one of the type of becki and one museum specimen of aterrima (Table 3.1). Some
samples failed to give useable sequences for one or two genes despite numerous PCR attempts and
therefore do not appear in corresponding gene trees. Sequences are deposited in GenBank under
accession numbers XXXX
The genus Pseudobulweria: monophyly, and relationships with other petrels
With all three genes, although tree topologies differ somewhat in the placement of genera
(Fig. 3.2; and Fig. 3.6, 3.7 Appendix 2), Pseudobulweria appears to be strictly monophyletic.
Although we could not sequence rupinarum (only bones are available), plates provided in Olson
(1975) clearly suggest that rupinarum also belongs to Pseudobulweria (based on characters on skull
and especially bill angle in comparison to skull).
With CO1, the gene used for the BarCode of Life in birds, Pseudobulweria appears as a
sister clade of a clade containing Puffinus, Calonectris, Bulweria, Fulmarus and Macronectes (Fig.
3.2). Node support between these two clades, however, is not strong (0.88). In Cytb,
Pseudobulweria is a sister group of Bulweria, although with posterior probability inferior to 0.8
(Fig. 3.6 Appendix 2). With this gene, different subclades within family Procellariidae are weakly
supported, with the exception of the Fulmarus/Macronectes group. In βFibint7, Pseudobulweria is
the sister group of Puffinus with strong support (posterior probability of 100%; Fig. 3.7 Appendix
2). However we lack data from genera such as Bulweria with this nuclear intron.
88
With both data sets (i.e. mitochondrial and nuclear+mitochondrial), clades recovered with
species tree analyses on genera data recovers mostly weakly supported clades. These analyses
indicate the presence of two main clades: one clade made of family Hydrobatidae and one clade
made of families Diomedeidae and Procellariidae (Fig 3.3; Fig. 3.8 Appendix 2). In the latter clade,
Pseudobulweria is the sister group to a sub-clade composed of Bulweria, Calonectris and Puffinus
(Fig. 3.3) or Puffinus alone (Fig. 3.8, Appendix 2) though the separation is very weakly supported
(Posterior probability 0.66 and 0.81 in mitochondrial and mitochondrial+nuclear analyses
respectively; Fig. 3.3, Fig. 3.8 Appendix 2). In both analyses time to the most recent common
ancestor of Puffinus and Pseudobulweria was estimated at about 13 millions years ago (95%HPD:
respectively 11-23 and 5.5-21.4 million years ago in mitochondrial and mitochondrial+nuclear
species tree analyses).
Genetic distances and taxa relationships within genus Pseudobulweria
Inter-taxa divergences (K2P corrected distances) in the genus with CO1 ranged from 1.21%
between trouessarti and becki to 7.4% between this latter taxon and aterrima. Fiji petrel showed the
lowest divergence with Beck’s (6.31%) and the greatest with Mascarene (7.15%) petrels
respectively. Using βFibint7, the greatest divergence was between becki and Fiji petrel (2.46%), the
latter being closest to Mascarene petrel (7.10-4%). Mean Fiji petrel divergence was the greatest with
rostrata (7.99%) and the smallest with trouessarti (4.99%) when using Cytb. For the two forms of
rostrata, sequence divergence was 0.12% with CO1, 0% with βFibint7 and 1.73% with Cytb.
Within genus Pseudobulweria, with CO1 aterrima and macgillivrayi seem the first to have
evolved in the sampled taxa, and becki is slightly differentiated from rostrata (Fig. 3.2). However,
contrary to the monophyly of the genus that is well supported, nodes within the genus are poorly
supported. The situation is different with Cytb. With this gene, macgillivrayi is embedded in a
group also containing becki, rostrata and trouessarti, while aterrima is placed outside this group
with strong node support. With the nuclear intron βFibint7, two subclades exist within
Pseudobulweria, separating rostrata (all but the Fiji specimen) from the other taxa, although node
support for these subclades is poor. Thus the relationship between the two Tahiti petrel taxa (i.e.
rostrata and trouessarti) is unclear when using βFibint7, and unresolved with the two mitochondrial
markers.
89
Figure 3.2 Bayesian Inference Phylogenetic Tree within Procellariiformes obtained with CO1 gene
with MrBayes. Node with Posterior Probabilities superior or equal to 95% are indicated by bold
lines and labels indicate posterior probabilities
90
Figure 3.3 Species tree reconstructed for genera within Order Procellariiformes with *BEAST,
using two mitochondrial loci (CO1, Cytb). Node labels indicate posterior probabilities.
Oceanites
0.82
Oceanodroma
0.99
Hydrobates
1
Phoebastria
Pachyptila
0.37
1
Halobaena
0.98
Pterodroma
Macronectes
0.28
1
Fulmarus
0.42
Pseudobulweria
0.66
0.4
Bulweria
Puffinus
0.98
Calonectris
91
The species tree analysis finds support for the hypothesis that becki and rostrata are each
other closest relatives and, within that group, that becki differs from the two Tahiti petrel taxa (Fig.
3.4). This tends to confirm the full separation of the becki lineage from the rostrata. According to
this analysis, the dark (aterrima, macgillivrayi) and white vented (becki, rostrata) Pseudobulweria
taxa diverged around six-seven millions years ago (95%HPD range: 3.2-10.75).
Figure 3.4 Pseudobulweria species tree reconstructed with *BEAST, based on two mitochondrial
loci (CO1, Cytb) and one nuclear marker (βFibint7). Node labels indicate posterior probabilities.
P_mcgillivrayi
1
P_aterrima
0.64
P_becki
0.99
P_trouessarti
0.99
P_rostrata
1.0
92
Finally, haplotype network reconstruction with CO1 using Tahiti and Beck’s petrel
sequences (Fig. 3.5) recovers a clear separation of becki from the two Tahiti petrel subspecies (11
mutations). Within Tahiti petrel, rostrata birds from Gambier, Tahiti and Marquesas are
differentiated from New Caledonian birds. Rostrata specimen from Fiji, although having a different
haplotype from the main trouessarti haplotype, is related to this later taxon rather than to other
rostrata birds from Polynesia (Marquesas, Gambiers, Society).
Figure 3.5 Haplotype network built with 68 sequences of Tahiti petrel and two sequences of Beck’s
petrel with the mitochondrial gene CO1. Bars across lines connecting different haplotypes indicate
the number of mutations between haplotypes. Small dots between some haplotypes indicate putative
missing haplotypes. Circles sizes are proportional to number of individuals
93
Discussion
Position of Pseudobulweria within Procellariiformes
With Cytb, Bretagnolle et al. (1998) found that Pseudobulweria was part of a clade
containing both Puffinus and Bulweria, with a more direct relationship to Puffinus. Here, with the
same gene Pseudobulweria was more directly related to Bulweria than to Puffinus though with low
support. With CO1, the relationship of Pseudobulweria was found in a clade including both
Puffinus and Bulweria. Our species tree analyses, based on two and three genes simultaneously,
strongly support a link between a Puffinus/Bulweria clade and Pseudobulweria, Halobaena and
Pachyptila forming a clade distantly related within Procellariidae. Our results therefore clearly
contradict
Kennedy
&
Page
(2002)
findings
that
placed
Pseudobulweria
close
to
Halobaena/Pachyptila and those of Penhallurick & Wink (2004), who placed Bulweria also close to
Halobaena/Pachyptila. Thus, despite the lack of nuclear intron data from Bulweria and Procellaria,
we believe that our study, by integrating information from several markers, strongly supports the
conclusion that Pseudobulweria affinities are to be found with Puffinus/Bulweria rather than with
Pachyptila/Halobaena or Procellaria.
Subspecies of Tahiti petrel
Evidence from individual genes used in this study is somewhat confusing regarding the
degree of divergence of these two lineages. CO1 node supports were low and corrected divergence
was 0.12%, well under the average intraspecific divergence found among 260 North American bird
taxa by Hebert et al. (2004). In addition, species tree reconstruction separated these two taxa from
becki but does not allow concluding on their status and degree of divergence. An important
limitation however remains: species tree analysis (such as in *Beast and other softwares) requires
that the taxa investigated do not exchange genes. However, in the case of rostrata and trouessarti,
gene exchanges cannot be ruled out, certainly not on the basis of our results since we have too few
sequenced individuals from taxon rostrata. Thus, the genetic difference between these taxa appears
very small, a result also found when using birds from American Samoa (M. Rauzon, S. Olson & R.
Fleischer pers. comm.). In addition, inconclusive morphological differences was found (Villard et
al. 2006), and the birds from Fiji belonging presumably to subspecies rostrata are actually closer to
birds from New Caledonia (spp. trouessarti) in the haplotypic network, thus raising additional
issues with regard to subspecies geographic delineation. Therefore, given the lack of specimens and
94
genetic data from Vanuatu and New Hebrides, we advocate the suppression of the two subspecies of
Tahiti petrels because they are currently ill-defined geographically, until stronger evidence is
provided either by morphological or phylogeographic investigation.
Beck’s petrel taxonomic status
Originally described by Murphy (1928) as warranting full specific status (though at that time
he placed becki within genus Pterodroma) because of its smaller size (about 10-15%, with no
overlap) compared to Tahiti petrel, Beck’s petrel taxonomic rank has subsequently been debated
and challenged, and the taxon was considered either a subspecies of Tahiti petrel (Imber 1985;
Jouanin & Mougin 1979; Warham 1990) or a full species (Collar & Andrew 1988; Sibley &
Monroe, 1990). In addition to biometrics, at-sea behavioural differences led Shirihai (2008) to
advocate for full specific status, though acknowledging that no single criteria (except size if judged
correctly) could allow separating both forms at sea. We found that becki was consistently separated
from rostrata in all three loci used. The separation of the two taxa in the species tree analysis was
supported by a posterior probability of 0.99, giving credit to the full specific status of Beck’s petrel.
Despite being well supported, the genetic distance is however small. Using CO1, a divergence of
1.21% was detected. Hebert et al. (2004) found that among 260 North American taxa, maximum
average intraspecific divergence was 1.24% with an average value of 0.27%. Our value is therefore
just below this average maximum divergence. Interestingly, Hebert et al. (2004) also found 13
species that showed interspecific distances lower than 1.25%. Thus, genetic divergence found with
CO1 (as well as the two other genes) seems to indicate the presence of two distinct species, albeit
only recently separated. Thus, we provisionally suggest that the two taxa should be considered as
fully distinct species. We expect other important species isolating characters such as calls
(Bretagnolle 1995) to confirm this separation when becki breeding colonies are discovered and
birds recorded. Such pre-mating isolating trait is likely to be important in those taxa since it is
rather likely that Tahiti petrels also breed in close vicinity to becki breeding colonies and even
possibly together (HS pers. obs.).
Fiji petrel
The phylogenetic relationship of macgillivrayi with other members of Pseudobulweria had
never been studied before. However, based on skull characters (Olson 1975), and at-sea behaviour
95
and flight (Shirihai et al. 2009), there was little doubt that Fiji petrel was a member of the genus.
Our genetic data fully confirm the pattern, both gene trees and species tree analyses placing this
taxon within Pseudobulweria. The exact placement within the Genus varied slightly with the
different genes. The species tree topology, suggests that becki and rostrata branched apart from
aterrima and macgillivrayi around 6-7 Myrs ago, in parallel with the colourations of the four
species (rostrata and becki are white vented, while the other two are entirely dark).
Timing of divergence
Values found in these analyses need to be taken with caution given the overall poor node
supports and also because we used only one estimation rate per marker and no fossil calibration.
The separation between Mascarene/Fiji and Tahiti/Beck’s petrels estimated around 6-7 millions
years ago corresponds to the end of Miocene, a time of marked ecological change (Janis 1993). The
Messinian stage of the end of Miocene was characterised by important sea-level regression that
were subsequently followed by sea transgression in the early Pliocene (Haq et al. 1987). Such
pattern could have increased the available habitat on several islands in the Indo-Pacific region, thus
promoting the colonisation of new breeding locations by Pseudobulweria common ancestors. New
populations would then be isolated due to sea-level increase, promoting the divergence of these taxa
and their colouration change by fixation of alternative alleles of the melanocortin-1 receptor gene
(MC1R). This gene is known to affect colourations in several bird lineages through a single nonsynonymous change, as well as a wide range of other organisms, from lizards to mammals (Mundy
2005). Alternatively, changes in oceanic conditions could have driven some birds to modify their
foraging habits thus promoting the differentiation of these lineages through different foraging
patterns and at-sea behaviour. Indeed, colourations in Procellariiformes were suggested to be linked
to feeding strategies and selective pressures such as competition and predation (Bretagnolle 1993).
Similarly, the split between Puffinus/Bulweria and Pseudobulweria around 13 millions years ago
correspond to the mid-Miocene climate transition, a period that experienced sharp oceanic
temperature drops (Shevenell et al. 2004) and oceanic current changes (Miller & Fairbanks 1983). It
was suggested that this period might have offered improved ecological conditions due to increased
oceanic productivity resulting from the cooling of ocean surface temperatures thus possibly
promoting taxonomic diversification of seabirds (Warheit 1992, 2002). This transition however
remains one of the least understood of such events in the last 34 millions years (Lewis et al. 2008)
and its importance in promoting seabird lineages diversification remains to be investigated.
96
Why are these petrels so rare?
Pseudobulweria taxa apparently exhibit poor resilience to human presence and its
accompanying invasive predators (e.g. Olson 1975). However, the recent rediscovery of Beck’s
petrel and the survival of Mascarene and Fiji petrels on islands that suffered important human
alteration and where many introduced predators now roam freely, show that these taxa can still
survive for a while in adverse conditions. We suggest that surviving taxa have probably been saved
by their formerly very large populations rather than the difficult access of their breeding sites. In
comparison to Pterodroma that often breed in cliffs or top of active volcanoes, Pseudobulweria
usually breed on more gentle slopes, at medium to low altitudes and even in some cases on the seashore. In addition, breeding sites of rostrata and aterrima at least are close to inhabited areas (e.g.
in Marquesas, or Reunion Island; VB, pers. obs.). Despite this, and probably because these petrels
breed on inhabited islands, their future survival is strongly impeded, and urgent action is required to
save at least three taxa from a likely extinction in the near future.
Conservation implications
Given the current biodiversity crisis and financial limitations, investments may be justified
in the conservation of distinct taxa, ecosystems or evolutionary units that are likely to produce
future biodiversity (Bowen 1999). The conservation of Pseudobulweria is unlikely to promote
future biodiversity. Despite this, we believe that in addition to being important systematics
conservation target (Bowen 1999), these birds also constitute good conservation objectives in an
“ecologist perspective” (Bowen 1999). Their conservation, both on land and at sea, would include
highly diverse and fragile insular and marine ecosystems. For instance, investing in Beck’s petrel
conservation would mean investing in the protection of whole forest ecosystems in one of the
world’s most biodiversity rich region (Papua-New Guinea area), which might be at risk of excessive
logging and/or forest clearance for oil-palm plantations, particularly in New Britain (Buchanan et
al. 2008; Shirihai 2008). Similarly, the conservation of Fiji petrel on Gau Island, Fiji, by promoting
the control of populations of introduced predators such as cats Felis catus, rats Rattus spp or pigs
Sus scrofa (Priddel et al. 2008) will benefit both native plant and animal communities in remnant
forest patches. In conclusion, conservation of these taxa is important both for their intrinsic
evolutionary and taxonomic values, as well as for the wider ecosystems of which they are a part. In
addition to current action in face of light-induced mortality (Le Corre et al. 2002, 2003; Priddel et
al. 2008; P. Raust pers. comm.), conservation of all Critically Endangered species of the genus, i.e.
97
Mascarene, Beck’s and Fiji petrel, urgently requires the discovery of their breeding colonies for
predator control, and possibly translocation operations, as it is unlikely that predator removal will
be feasible.
Acknowledgments
This work was supported by the "Consortium National de Recherche en Génomique", and
the "Service de Systématique Moléculaire" of the Muséum National d'Histoire Naturelle (CNRS
UMS 2700). It is part of the agreement n°2005/67 between the Genoscope and the Muséum
National d'Histoire Naturelle on the project "Macrophylogeny of life" directed by Guillaume
Lecointre. We are deeply indebted to Joel Cracraft, Curator, Paul Sweet, Collection Manager, and
Margaret Hart at the American Museum of Natural History (AMNH) for giving us access to the
collections and providing samples from Pseudobulweria becki type specimens. BG also
acknowledges receipt of a Collection Study Grant from the AMNH. We thank Alice Cibois for
providing us samples from P. rostrata rostrata from Marquesas. Many thanks also to T. Steeves
and P. Pelser for useful comments that improved a previous draft of the manuscript.
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Appendix 1
Table 3.3 Outgroup taxa and sequences GenBank accession numbers, BarCode Id number (marked
with an asterisk) or internal code
Family
Genus
Pachyptila
Taxon
Pachyptila_turtur
AF076070
Halobaena
Halobaena_caerulea
CO1
Cytb
BROMB700-07*
AF076057
Bulweria
Bulweria_bulwerii
CO1
Cytb
BROMB697-07*
AJ004156
U74341
Puffinus
Puffinus_tenuirostris
CO1
Cytb
βFibint7
DQ434025
DQ434027
U74352
AY695220
CO1
Cytb
βFibint7
DQ434015
AF076085
DQ881991
CO1
DQ432808
DQ433417
AY139626
U74356
DQ433651
DQ432933
AJ004178
U74348
EF552765
DQ881958
Puffinus_lherminieri
PROCELLARIIDAE
CO1
Cytb
Sequence Code
BROMB324-06*
Calonectris
Calonectris_diomedea
Cytb
Fulmarus
Fulmarus glacialis
CO1
Cytb
βFibint7
Macronectes
Macronectes_giganteu
s
CO1
Cytb
FJ027768
U48941
AF076060
Pterodroma
Pterodroma_hasitata
CO1
Cytb
DQ434001
EU167017
Pterodroma_cahow
CO1
Cytb
KKBNA075-04*
U74331
Pterodroma_cookii
CO1
Cytb
ROMC325-07*
U74345
Pterodroma_ultima
CO1
Cytb
βFibint7
BE84
BE85
BE84
104
Pterodroma_neglecta
CO1
DIOMEDIDAE
Cytb
Phoebastria
Cytb
βFibint7
HYDROBATIDAE
Oceanites
SPHENI
SCIDAE
Phoebastria_nigripes
βFibint7
CO1
Oceanites_oceanicus
CO1
Cytb
βFibint7
Hydrobates
Hydrobates_pelagicus
CO1
Cytb
βFibint7
Oceanodrom Oceanodroma_leucor
a
hoa
Spheniscus
Spheniscus humboldti
CO1
Cytb
βFibint7
CO1
Cytb
βFibint7
BE54
BE61
U74341
GQ328985
BG54
DQ433934
DQ433935
U48950
EU166988
EU739406
EF552760
DQ433048
DQ433049
AF076062
EU739449
AY567885
AF076059
AJ004182
DQ881965
AY666284
DQ434684
DQ434685
AF076064
AY695221
AY567888
DQ137220
DQ881996
105
Appendix 2
Figure 3.6 Bayesian Inference Phylogenetic relationships within Procellariiformes obtained with
Cytb gene in MrBayes. Node with Posterior Probabilities superior or equal to 95% are indicated by
bold lines and labels indicate posterior probabilities
106
Figure 3.7 Bayesian Inference Phylogenetic relationships within Procellariiformes obtained with
BetaFibrinogen intron 7 in MrBayes. Node with Posterior Probabilities superior or equal to 95% are
indicated by bold lines and labels indicate posterior probabilities
107
Figure 3.8 Species tree reconstructed for genera within Order Procellariiformes with *BEAST,
using two mitochondrial loci (CO1, Cytb) and one nuclear intron (βFibint7). Node labels indicate
posterior probabilities
Oceanodroma
0.98
Hydrobates
0.36
Oceanites
1
Phoebastria
Fulmarus
0.49
0.39
Pterodroma
0.99
Puffinus
0.81
Pseudobulweria
108
Feae petrel
© Hadoram Shirihai
109
CHAPTER 4: TRACKING THE PHYLOGEOGRAPHIC HISTORY
IN NORTH EAST ATLANTIC GADFLY PETRELS REVEALS
MITOCHONDRIAL AND NUCLEAR DNA INCONGRUENCE AND
COMPLEX SCENARIOS
Gangloff et al., in prep
Abstract
Northeast Atlantic Macaronesia archipelagos have been hosts of complex patterns of colonisation
and vicariance in many groups of organisms. Complex phylogeographic patterns are also found in
seabirds including the gadfly petrels (genus Pterodroma). Long time considered sub-species of the
widely distributed Soft-plumaged petrel, the taxonomic status of the three gadfly petrel taxa
breeding in Macaronesia is not yet settled, some authors advocating the presence of three, two or
one species. These birds have already been the subject of genetic studies but only with one mtDNA
marker. However, several recent studies in a range of taxa have underlined potential discrepancies
between mitochondrial and nuclear DNA when investigating phylogenetic and phylogeographic
histories. In this study, using a total of five loci (2 mitochondrial and 3 nuclear introns) we
investigated the population and phylogeographic histories in these threatened seabirds. Despite their
morphological and behavioural (calls) similarities, these taxa showed complete lineage sorting with
mtDNA contrary to nucDNA, and no gene flow was detected between them. In addition, genetic
diversity was unexpectedly high for taxa with such low current population sizes (supposed to be
less than 1,000 birds). It appears that these birds diverged in the late Pleistocene in the last 110,000
years, i.e. 10 times more recently than previous estimates based solely on one mtDNA gene.
Finally, contrary to previous studies, our results suggest that Madeira petrel is ancestral rather than
Feae petrel from Cape Verde. This study advocates the use of nuclear loci in addition to mtDNA
when inferring complex demographic and phylogeographic histories of organisms
Keywords :
Pterodroma, Madeira, Cape Verde, Phylogeny, Petrel, Intron, CO1, Cytochrome b, evolutionary
history, divergence, seabird, nucDNA, mtDNA
110
Introduction
The supremacy of mitochondrial DNA (mtDNA) in vertebrate phylogenetic and
phylogeographic studies has been recently challenged by the increasing use of, and the sometimes
conflicting results that emerged from, nuclear genes (Edwards et al. 2005, Zink & Barrowclough
2008, Edwards & Bensh 2008). mtDNA is acknowledged for its non-recombining properties and its
high mutation rate (Avise et al. 1987, Zink & Barrowclough 2008). In addition, because of its lower
effective population size (Ne) compared to nuclear DNA (approximately 4Ne) lineage sorting will
occur faster for mtDNA than for nucDNA thus allowing the detection of more recent vicariance
events (Zink and Barrowclough, 2008). Moreover, until fairly recently, nuclear genes were not
available (Lee & Edwards, 2008). However, with the increasing use of nuclear markers along with
mtDNA, a growing body of evidence has indicated that inferences based on mtDNA should be
regarded with caution (Funk & Omland 2003; Chan & Levin 2005). Many studies indeed revealed
incongruent results between mtDNA and nuclear genes (e.g., Shaw 2002, Spinks & Shaffer 2009;
see also Zink & Barrowclough 2008 and Lee & Edwards, 2008 for a review and a recent example in
birds). Incongruence has been explained by lower mutation rate of the nuclear genes, incomplete
lineage sorting, and possibly more complicated evolutionary scenarios including hybridization or
natural selection which may actually act on mtDNA (Avise et al. 1987; Edwards et al. 2005, Bazin
et al. 2006, Spinks & Shaffer 2009). Although the respective merits of nuclear versus mtDNA
markers are disputed in regard to disentangling pattern and process in evolution and speciation (see
Zink & Barrowclough 2008, Edwards & Bensh 2008), consensus has eventually emerged that in
front of complex evolutionary or biogeographical histories, mtDNA alone will be insufficient (Chan
& Levin 2005).
Oceanic islands have long been considered laboratories of evolution (Losos & Ricklefs,
2009) where fundamental evolutionary processes such as speciation can be addressed (review in
Emerson, 2002). For more than 50 years, debates have been heated over the main processes of
speciation, and it is now generally agreed that almost every speciation events are associated with
some kind of geographic separation (Fitzpatrick et al., 2009; Coyne & Orr, 2004). This has been
especially well studied in birds, this class of vertebrates being for a long time one of the best studied
groups (Price 2008). In this context, it may therefore seem paradoxical that oceanic birds, that
mainly breed on islands, have been so poorly studied (but see Friesen, 2007; Friesen et al. 2007):
the most recent and major review of speciation in birds provides only seven examples from the
seabirds (two from boobies, four from gulls, and one from the petrels), only concerning three
genera (Price 2008). Pelagic seabirds breeding on oceanic islands can be seen as challenging
models as they are highly vagile and physical barriers to their dispersal are virtually non existent,
111
and therefore this should enhance high gene flow between populations. However, counteracting
selective forces consist in the discrete location of breeding localities, and extreme phylopatric
behaviour shown by these seabirds that should, on the reverse, enhance genetic drift. Therefore,
conflicting processes are acting on the populations genetics of seabirds, with the petrels (order
Procellariformes, containing 113 recognised species; Birdlife International, 2010) being the extreme
example in this group. These are the most pelagic seabirds and almost all species breed on oceanic
islands (review in Brooke 2004). They also show an extreme degree of philopatric behaviour
(Austin 1996; Brooke 2004). In addition, because many species show nocturnal habits on the
breeding grounds, their communication system (and presumably behavioural pre-isolating
mechanism: Bretagnolle 1996) almost never rely on visual cues, and many taxa show cryptic and
highly convergent plumages, making their taxonomy poorly understood (Brooke, 2004). This has
led to many cases of phylogenetic uncertainties, including cryptic species with unclear
phylogeographic histories causing constant revision of their taxonomy. Indeed recent use of
phylogenetic information has led to several taxonomic revisions (Abbott & Double 2003, Burg &
Croxall 2004, Austin et al. 2004, Bolton et al. 2008), or the discovery of cryptic species and strong
genetic population structuring (Friesen et al. 2006, 2007, Smith & Friesen 2007, Bolton et al.
2008).
Perhaps one of the best example of Procellariiformes phylogenetic complexity exists in the
north-east Atlantic islands, where gadfly petrels (genus Pterodroma) breeding in Macaronesia
(including the archipelagos of Azores, Madeira, Canarias and Cape Verde) have led to long-lasting
and never-ending taxonomic debates (review in Zino et al. 2008, Shirihai et al. 2010). Three
Pterodroma taxa breed in these islands: one on Madeira island itself (madeira), one on Bugio island
(deserta) which is just 30km south-east of Madeira, and one in Cape Verde archipelago (feae), on
four distinct islands (Fig. 1). First considered subspecies of the widely distributed soft-plumaged
petrel, P. mollis, the three forms were subsequently considered as one distinct species with three
subspecies (Bretagnolle 1995), two species including one with two subspecies (Bourne 1983, Zino
et al., 2008), or three distinct species (Robb, 2008, Jesus et al. 2009). Phylogenetic analyses based
on mtDNA have represented a major step in these splits, since these populations hardly differ
morphologically, in colouration and vocalisations (Zino & Zino, 1986; Bretagnolle, 1995; Zino et
al., 2008, Shirihai et al. 2010). Using a total of 26 individuals, Zino et al. (2008) showed that birds
from Madeira and Bugio differ on Cytochrome b (Cytb) sequences by 2.2% nucleotide divergence.
They further suggested that the two species separated some 2.5 million years ago (i.e., late
Pliocene), though Sangster et al. (2002) suggested a much recent split (840,000 years ago, Early
Pleistocene), based on unpubl. data on Cytb also. Similarly, Jesus et al. (2009) compared 35
individuals from Bugio and Cape Verde, and found that the average K2P sequence divergence
112
(Kimura, 1980) was 1.58% (2.4% and 2.3%, respectively, for Madeira and Bugio, and Madeira and
Cape Verde divergences). The split between Bugio and Cape Verde was suggested at 1.75 million
years ago. In both studies, no haplotypes were shared between the three populations. Despite the
evidence presented by Jesus et al. (2009), Birdlife International currently recognises only two
species, madeira and feae, the latter with two subspecies, feae and deserta. Though never tested
accurately, the acknowledged historical scenario proposes that birds came from Cape Verde and
colonised Madeira twice, hence explaining the differences between Madeira and Bugio birds
despite the close proximity of these islands (see e.g., Bourne 1983).
Because taxonomic uncertainties are often due to complex underlying phylogeographic
patterns that are uneasy to apprehend with the use of a single mitochondrial locus and, since the
separation of Macaronesian petrels in three species is currently uncorroborated by any other criteria
such as morphology (Shirihai et al., 2010) or vocalisations (Bretagnolle 1995; but see Robb 2008),
we investigated further the population, phylogeographic and biogeographic histories of these
petrels. Methods are now available that provide an analytical framework to investigate the origin of
island taxa and their evolution (Emerson, 2002; Liggins et al., 2008), by integrating simultaneously
several genes histories (e.g. Heled & Drummond, 2010; Hey, 2010). We therefore used one
additional mitochondrial gene and three nuclear introns in a much extended sample of birds from
the three taxa to investigate Macaronesian petrel relationships. More precisely, i) we test whether
nuclear DNA confirms the reciprocal monophyly of the three taxa; ii) we used phylogeography and
population genetics tools to infer the population structure, timing of divergence, and demographic
history in these threatened seabirds.
113
Figure 4.1. Macaronesian archipelagos and breeding localities of Macaronesian Pterodroma taxa:
Madeira island (P. madeira), Bugio island (P. deserta), Fogo and other Cape Verde islands (P.
feae)
114
Material and Methods
Because of the taxonomic uncertaintiessurrounding these taxa we consider them without reference
to their potential species status in the following.
Sample collection and laboratory methods
Blood samples were obtained from Bugio Island (Desertas islands, off Madeira), several
colonies on Madeira Island and two islands of the Cape Verde, Fogo and Santo Antao Islands (Fig.
4.1). Blood was collected from the veins on the leg or wing using microcapillaries and stored in
70% ethanol then frozen at -20°C until processing. In all analyses, no differentiation was found
between different colonies within madeira and feae (data not shown), and therefore we pooled the
data from all colonies within each of the three taxa. Total genomic DNA was extracted using the
DNeasy Tissue Extraction Kit (Qiagen, Valencia, CA, USA) following manufacturer’s instructions
except that we increased the time of proteinase digestion to 2h. Cytochrome Oxydase 1 gene (CO1)
was amplified using primers F1B 5’-AACCGATGACTATTYT-CAACC-3’ and R1B 5’TACTACRTGYGARATGATTCC-3’, derived from primers F1/R1 (Simon et al., 1994). The PCR
consisted of 37 cycles following a hot start at 94°C and a 4 minutes initial denaturation step at
94°C. Cycles, i.e. 94°C for 30s, 51°C for 40s and 72°C for 50s, were completed by a final extension
at 72°C for 5 minutes.
Primers
FIB-BI7U
and
FIB-BI7L
5’-
For the five markers, sequencing was conducted under BigDyeTM terminator cycling conditions at
the “Genoscope - Centre National de Séquençage”, France. DNA sequences were aligned using
CodonCode Aligner 3.0.3 (CodonCode Corporation, 2009) and ClustalW (Thompson et al., 1994)
as implemented in Mega version 4 (Tamura et al., 2007) and checked by eye.
115
Genetic diversity, genetic structure and historical demography
To estimate haplotypes of nuclear loci, the software PHASE 2.1.1 (Stephens et al. 2001) was
used as implemented in DnaSp v5 (Librado & Rozas, 2009). The latter was then used to estimate
haplotypes of all five loci for use with Arlequin. Nucleotide diversity (π) and haplotype diversity
(h) were calculated with Arlequin v3.5 (Excoffier & Lischer, 2010). With this software, we also
conducted an exact test of population differentiation (Raymond & Rousset 1995) and investigated
population structure with an AMOVA. Furthermore, to assess whether selection has affected the
loci examined and also to test for past population expansion, Fu’s Fs (Fu, 1997) and Tajima’s D
(Tajima, 1989) tests of neutrality were performed using 1,000 simulations in Arlequin. Although
Fu’s indices have been found to be more powerful than Tajima’s (Ramos-Onsins & Rozas, 2002), it
was also shown by these authors to have an irregular behaviour. Thus, we considered FS statistic
significant when the P value was below 0.02 (Fu, 1997). Significant negative values of both
statistics point towards population growth and/or positive selection, whereas positive Tajima's D
values indicate bottlenecks and/or balancing selection. We also investigated historical demography
of the three taxa using mismatch distribution under a model of sudden range expansion with
Harpending’s raggedness index (Harpending, 1994) and the sum of squared deviation SSD
(Schneider et al., 2000) between the observed and expected distributions. A significant SSD value
indicates a departure from the model of sudden population expansion (Schneider & Excoffier,
1999) and low values of r usually reflect a population expansion.
Evolutionary relationships estimation
jModelTest v0.1.1 (Posada, 2008; Guindon & Gascuel, 2003) was used to evaluate the
model of nucleotide substitution that best fitted the data. We used Bayesian Information Criterion
(BIC), which penalises more heavily than AIC overparamerisation therefore avoiding unnecessary
complex models when sample size increases (Sullivan & Joyce, 2005). For each of the five loci,
phylogenetic relationships were estimated using Bayesian Markov Chain Monte Carlo (MCMC)
phylogenetic analyses with MrBayes v3.1.2 (Huelsenbeck & Ronquist, 2001; Ronquist &
Huelsenbeck, 2003). For all Bayesian analyses, default priors of MrBayes 3.1.2 were used for
MCMC parameters. We used three heated chains and one cold chain for all analyses and runs were
started with random trees. Two independent MCMC runs were conducted with 4.106 generations for
each run for all five markers. Trees and parameters were sampled every 100 generation. Standard
deviation of split frequency were used to assess stationarity, with the average standard deviation of
116
split frequencies expected to approach zero when the two runs converge onto stationarity
distribution (Ronquist et al., 2005). Additionally the potential scale reduction factor should
approach one when runs converge. For each run the first 25% were discarded as burn-in. For CO1
data, we added sequences from Pterodroma hasitata retrieved on GenBank (accession number
DQ434000 and DQ434001) to root the tree, as this species appears to be one of the closest relative
of these three taxa (Nunn & Stanley, 1998; Zino et al., 2008). For Cytb data, sequences of
Pterodroma mollis (n=1), P. cahow (n=1) and P. hasitata (n=2) retrieved on GenBank (accession
numbers U74334, U74331, EU167017, U74332 respectively) were added. βFibint7 tree was rooted
using one sequence of Puffinus pacificus (Gangloff et al., MS submitted). Finally, trees obtained
with CSDE1 and PAXIP1 were rooted with sequences of Pseudobulweria becki (Gangloff et al.,
MS submitted). In addition, for all five markers, phylogenetic relationships between haplotypes
were inferred using the median-joining networks method as implemented in NETWORK v4.5.1
(Bandlet et al., 1999; Fluxus Technology Ltd, 2009).
Estimation of gene flow, population divergence time and effective population
sizes
A potential consequence of gene flow between populations could be unresolved
phylogenetic gene trees. Hence, using IMa2 (Hey, 2010), we estimated this parameter between
macaronesian petrel populations. IMa2 allows testing for gene flow between any number of
populations between two and ten, but it requires an a priori topology of the populations under
study. Since we cannot be certain of the correct species tree topology in our case, in a first step, we
ran IMa2 on pairs of petrel populations rather than on all three populations at once. In a second step
we ran an analysis using the topology obtained with mitochondrial genes (this study, see Results;
Jesus et al., 2009). Several runs were sampled with a burn-in period of 4.105 steps and sampling
period of 2.106 steps, using 10 to 100 chains and a geometric heating scheme. A range of mutation
rates can be given as prior to the analysis for scaling parameter estimates in demographic units. We
used a mean value of 0.794 ± 0.115% per million year for CO1 (Pereira & Baker, 2006) and 1.89 ±
0.35% for Cytb (value found for Procellariiformes; Weir & Schluter, 2008). For introns, we let
IMa2 calculate mutation rates scalars for the other loci (i.e. no mutation rate was specified for the
three nuclear introns). In addition, to evaluate the degree of gene flow, divergence time and
population sizes in demographic unit, it is possible to enter the generation time of a taxon.
Pterodroma petrels are, as all other Procellariiformes long-lived seabirds. No precise estimation of
generation time currently exists for Macaronesian petrels. However, in the Atlantic petrel
(Pterodroma incerta) this parameter is estimated to be 20 years (Cuthbert, 2004) and in another
117
genus belonging to Family Procellariidae, Scott et al. (2008) found two estimates of generation time
in the sooty shearwater (Puffinus griseus) of 14.7 and 21.6 years respectively based on different
adult survival assumptions. Furthermore, adult survival rate in a similar species was estimated at
0.93 (Simmons et al. 1984, Brooke et al. 2010), giving a generation time of c. 14 years. We thus
used an approximate generation time of 15 years for Macaronesian petrels. Runs were monitored by
observing effective sample size (ESS) values and inspecting parameter plots for trends following
the manual recommendations. Analyses were run three times to ensure convergence and since all
results were similar, only one is presented here.
As computations with this software can take a very long time, and because we had unbalanced
sample sizes between loci, we ran all analyses using a subset of samples from CO1, Cytb and
βFibint7 corresponding to individuals from which we obtained CSDE1 and PAXIP1 sequences,
rather than integrating all the sequences at our disposal for the three former markers.
Results
Genetic diversity
Overall, madeira and feae had a higher genetic diversity than deserta, the latter consistently
having lower h and π values than its macaronesian counterparts in all loci (Table 4.1 for all five
loci). Despite the fact that the sample size of individuals in deserta was 1.5-1.6 times more than
those for feae and madeira, the latter two showed twice as many haplotypes (9 and 11) as deserta (4
and 6; Table 4.1) with mitochondrial markers. A similar situation arose with PAXIP1 (4 vs. 8 and
10). In the last two other nuclear loci, deserta also had the lowest number of haplotypes, along with
one of the other two taxa (Table 4.1).
Population history
Haplotype and nucleotide diversity can be used to interpret demographic history of
populations (Grant & Bowen, 1998). The pattern observed in madeira and feae mtDNA, i.e. high h
(>0.5) and low π (<0.5%) is suggestive of a bottleneck followed by a rapid population expansion.
Conversely, deserta shows low h and low π, indicating rather a recent population bottleneck or a
founder event by a few mitochondrial lineages (Grant & Bowen, 1998). In addition, in madeira, all
neutrality tests have negative values, which are significant in three markers (Table 1), thus
suggesting past population growth or positive selection. Similarly, non-significant SSD values and
low values of r also indicate a population growth. The situation is a bit more contrasted in feae. For
118
this taxon, half of neutrality tests values are significant supporting the hypothesis of population
growth; other values are however negative. Mismatch analysis indices, as in madeira, all indicate a
population growth. In deserta, although all negative, a majority of Fu’s Fs and Tajima’s D values
are not significant. Furthermore, raggedness index’s high values suggest a stable population rather
than an increasing one, while non-significant SSD values do not contradict the hypothesis of
population growth. Thus in this taxon, the signal is not clear and this could reflect a period of low
population followed only very recently by a population increase.
Population structure
In both mitochondrial genes, exact test of population differentiation indicated a significant
differentiation at the global level and for each pairwise comparisons (all P<0.001). In addition,
population structure was detected with pairwise Fst values among each pairs of taxa (all Fst>0.9; all
P<0.001) indicating a pattern of geographic differentiation. Indeed, most of the genetic variation
was observed between populations rather than within population, the latter accounting for only
8.7% and 3.7% of the total variation in CO1 and Cytb respectively (Table 4.2). These results are
fully consistent with previous analyses carried out on Cytb only (Zino et al., 2008, Jesus et al.,
2009), though obtained on a much larger sample size (c.4-5 times) and with two, rather than a
single gene.
Results obtained with nuclear introns contrasted strongly, however, with those obtained with
mtDNA. Out of the three introns, only one, βFibint7, showed some geographic structure. However,
although significant differentiation was detected using Raymond & Rousset test at the global level
(i.e. all populations together; P<0.001), only feae actually differed from the other two forms (both
P<0.001). Similarly, pairwise Fst values between feae and the two other two taxa were significant
(Fst=0.24 and 0.14 with deserta and madeira respectively; both P<0.001). But in contrast to Cytb
and CO1 results, more than 80% of genetic variation was found within populations rather than
between taxa in βFibint7 (results of AMOVA, see Table 4.2). Furthermore, neither PAXIP1 nor
CSDE1 showed differentiation (exact test of population differentiation: P=0.2 and P=0.49,
respectively), and only one pairwise Fst value was found significant in PAXIP1, between deserta
and madeira (Fst=0.2; P<0.05), while none were significant in CSDE1 (all Fst=0.0). Similarly to
βFibint7, more than 90% of genetic variation is detected within populations by the MANOVA in
PAXIP1 and CSDE1 (Table 4.2).
119
βFibint7
PAXIP1
CSDE1
Cytb
CO1
Table 4.1 Diversity and neutrality estimates for three Macaronesian Pterodroma taxa. Number of sequences (N), number of polymorphic sites (Np),
number of haplotypes (Nh), haplotype diversity (h), nucleotide diversity (π, expressed as percentages, i.e. 0.001=0.1%), average number of nucleotide
differences (k). Significant values (P<0.05; P<0.02 for Fu’s Fs) are in bold
r
SSD
Taxon
N
Np
Nh
h
π%
k
Fu’s Fs Tajima’s
D
P. deserta
89
5
4
0.474
0.07
0.534
-0.305
-0.714
0.198
0.022
P. feae
58
10
9
0.687
0.15
1.095
-3.426
-0.886
0.078
0.003
P. madeira
57
11
9
0.778
0.23
1.683
-1.616
-0.475
0.04
0.007
P. deserta
94
5
6
0.219
0.03
0.229
-5.552
-1.473
0.367
0.002
P. feae
59
11
11
0.556
0.11
0.963
-6.606
-1.679
0.052
0.0001
P. madeira
59
12
11
0.535
0.09
0.788
-8.01
-1.929
0.086
0.001
P. deserta
12
0
1
0
0
0
NA
0
NA
NA
P. feae
10
0
1
0
0
0
NA
0
NA
NA
P. madeira
12
3
4
0.45
0.1
0.5
-2.124
-1.63
0.009
0.153
P. deserta
10
4
4
0.53
0.3
1.4
-0.175
-0.038
0.382
0.055
P. feae
14
15
10
0.95
0.7
3.56
-3.833
-0.999
0.076
0.016
P. madeira
14
12
8
0.87
0.5
2.58
-2.427
-1.257
0.05
0.008
P. deserta
154
1
3
0.026
0.003
0.026
-4.696
-0.904
0.9
0.0
P. feae
56
15
9
0.67
0.17
1.654
-2.558
-1.486
0.074
0.003
P. madeira
58
2
3
0.07
0.007
0.069
-3.239
-1.448
0.75
0.00003
120
Table 4.2 Genetic variation within and between the three taxa of Macaronesian Pterodroma petrels
(AMOVA)
PAXIP1
CSDE1
βFibint7
Cytb
CO1
d.f
2
Sum of
Squares
704.787
Variance
components
5.3
Percentage of
variation
91.28
201
101.835
0.51
8.72
2
1079.63
7.85
96.39
209
61.407
0.29
3.61
2
3.62
0.022
17.4
263
28.446
0.108
82.96
Among
populations
2
0.162
0.00
0
Within populations
31
2.75
0.09
100
Among
populations
2
5.324
0.11
7.5
Within populations
35
46.229
1.32
92.5
Among
populations
Within populations
Among
populations
Within populations
Among
populations
Within populations
Phylogenetic relationships
The model of nucleotide substitution that best fitted the data (using jModeltest) differed
between the different markers: Hasegawa, Kishino and Yano model (Hasegawa et al., 1985) with a
gamma-distributed rate variation across sites (HKY+G) for Cytb, CO1, and βFibint7, HKY model
for CSDE1, and F81 model for PAXIP1.
In both mitochondrial markers the phylogenetic trees (Fig. 4.2) recovered the three taxa in
two well supported clades: one containing deserta and feae, the other containing madeira. In CO1
this latter taxon presents a well supported sub-clade that is not present in Cytb. The three taxa are
closest to Bermuda petrel (P. cahow) then to Black-capped (P. hasitata) and soft-plumaged (P.
mollis) petrels (see also Jesus et al. 2009). The haplotype network analyse on Cytb and CO1 clearly
indicates complete lineage sorting between the three taxa (Fig. 4.3).
Conversely, as expected from the lack of population structuring and/or genetic diversity for
nuclear introns, the BI phylogenetic analyses failed to recover monophyletic taxa with any of the
121
three introns studied (Fig. 4.2). All three taxa are mixed together in a large polytomy, although a
well supported sub-clade made of feae and madeira specimens is present in PAXIP1. In the
haplotype networks (Fig. 4.3) lineages are not sorted and all share a dominant haplotype. In
PAXIP1 madeira and deserta do not share any haplotype other than the dominant one found in all
three taxa, while on the other hand, feae shares haplotypes with the two other taxa.
122
Figure 4.2. Phylogenetic tree of Macaronesian petrels with five markers. A CO1; B Cytb; C
βFibint7; D PAXIP1; E CSDE1; for clarity, in trees A, B and C not all sequences are shown, thus
number of leaves is inferior to number of sequences indicated in Table 4.1; trees D and E show all
sequences. Nodes with Posterior Probabilities <95% are collapsed. Posterior Probabilities >95% are
shown (with exception of one node in CO1 tree with 94% support)
100
P hasitata
100
P mollis mollis
P hasitata
P cahow
99
98
99
100
100
100
100
96
94
100
D
E
99
100
100
99
95
98
100
98
97
100
100
100
100
C
A
B
feae
deserta
madeira
123
Figure 4.3. Haplotype networks obtained with CO1, Cytb, PAXIP1, βFibint7 and CSDE1 loci with
Macaronesian Pterodroma petrels. Size of circles are proportional to number of individuals
possessing this haplotype. Green: haplotypes from Madeira, madeira; Red: haplotypes from Bugio,
deserta; Blue: haplotypes from Cape Verde, feae.
124
Gene flow, population divergence time and effective population sizes
No gene flow was detected between taxa, i.e. distributions of migration rate peaked at zero,
hence no significant population migration rate was detected by the likelihood ratio test (Nielsen &
Wakeley 2001) in any pairs of taxa. Divergence times between pairs of population and population
sizes estimates are summarised in Table 4.3. All population divergences are estimated as having
occurred within the Pleistocene: despite large 95% Highest Posterior Density Intervals (95%HPD),
all estimates are still below one million year ago. The oldest estimated divergence time concerns the
split feae/madeira (around 290,000 years ago).
Using the topology obtained with mitochondrial genes (Fig. 4.2) on the three taxa
rather than pairs provided very similar results: no significant migration rate is detected between any
taxa, the split feae/deserta is estimated around 37,000 years ago (95%HPD 13,000-99,000), and the
differentiation between the ancestral feae/deserta and madeira occurred about 110,000 years ago
(95%HPD 46,000-222,000; Fig. 4.4). Populations of the three extant Macaronesian petrel taxa have
estimated sizes comprised between two and three thousands individuals (Table 4.4), which is lower
than estimates found when analysing pairs of taxa (Table 4.3).
Table 4.3 Result from Isolation with Migration analysis with IMa2. 95%HPD intervals are given in
brackets. Population estimates are for effective population Ne. “Population 1” refers to the first
taxon of the pair (in the first column), “population 2” to the second taxon
Divergence time
(Million years)
0.29
[90 656 – 648 542]
0.092
Madeira/Deserta
[18900 – 324538]
0.058
Feae/Deserta
[14406 – 204562]
Madeira/Feae
Population 1
Population 2
Ancestral population
6363
[2354 – 14 964]
4457
[1528 – 11 000]
5202
[1681 – 13152]
8297
[3261 – 18 887]
3793
[1174 – 9652]
3228
[880 – 8777]
13 000
[4358 – 89 914]
23 000
[10 700 – 74 600]
11 000
[2401 – 30786]
Table 4.4 Population size estimates obtained with IMa2 for the three populations of Macaronesian
petrels when using the mitochondrial gene trees topology
Population size estimation
(95% highest posterior density interval)
Deserta
Feae
Madeira
Ancestral Feae/Deserta
1949 (573 – 5160)
3096 (1032 - 7224)
2179 (1032 - 5848)
7912 (573 – 206 300)
6307 (1261 – 16627)
125
Figure 4.4 Population divergence estimation under IM model with IMa2 for the three populations
of Macaronesian petrels. Histories for all three population pairs are represented as boxes (for
sampled and ancestral populations), horizontal lines (for splitting times). Time is represented on the
vertical axis in each figure, with the sampled species and subspecies names given at the top of each
figure at the most recent time point. 95% highest posterior density intervals are shown with arrows
in gray for population sizes (i.e., box widths; horizontal arrows) and splitting times (dotted lines;
vertical arrows)
126
Discussion
Gadfly petrels from Macaronesia have already been the subject of genetic study (Zino et al.
2008, Jesus et al. 2009), although with reduced sample sizes, and with only one mtDNA marker
compared to five in this study (including nuclear markers). Main results are as follow: nucleotide
diversity and haplotype diversity are unexpectedly high given the current population sizes of all
three taxa (between 80 and 250-500 breeding pairs; see Shirrihai et al., 2010). Nucleotide diversity
is much lower in deserta than in the other two taxa, despite the fact that current population size of
deserta is supposed to be at least 3-5 times larger than, e.g., madeira. In addition, we found that
nuclear genes provided a strikingly different picture compared to mtDNA, the latter two genes
under study providing a rather similar pattern (though not identical). In contrast to previous
statements, our results suggest that madeira is the ancestral taxon, not feae. Finally, our times of
divergence between any pairs of taxa are about 10 times smaller than previous estimates, one being
estimated less than 40,000 years ago. Our study therefore contradicts almost all previous statements
based only on one mtDNA marker and reduced sample sizes. Below we discuss these findings and
their implications.
Genetic diversity
High haplotypic diversity in various mitochondrial genes has been described in a wide range
of Procellariiformes species, despite most are currently declining, from albatrosses (Abbott &
Double, 2003; Burg & Croxall, 2001,2004) to giant petrels (Macronectes spp.; Techow et al., 2010)
and White-chinned petrels (Procellaria aequinoctialis; Techow et al., 2009). In two recent studies
of currently bottlenecked populations of Procellariiformes, Lawrence et al. (2008a, b) in Taiko
petrel (Pterodroma magentae), and Kuro’o et al. (2010) in Short-tailed albatross (Phoebastria
albatrus) found high genetic diversity, while the reverse was expected given the extreme bottleneck
faced by these populations (Spielman, 2004; but see Miller & Waits, 2003). The explanation
provided in these two species was that current high genetic diversity (despite very low numbers)
was linked to high diversity prior to the bottleneck. In addition, these seabirds are long-lived, and
the bottlenecks were of relatively short durations hence did not have time to affect genetic diversity.
The three Macaronesian petrels also currently show unexpectedly high genetic diversity given their
low population sizes. Since they are also long-lived, and since our data support a population growth
in the recent past, we suggest that the level of genetic diversity reflects previous levels, i.e. before
the bottleneck they are now going through. However genetic diversity found in this study are
127
moderate, and even low in the case of deserta, which requires some explanation. We suspect that
this taxon experienced a serious bottleneck and/or a founder event by a very low number of
individuals, as shown by the pattern of haplotypic and nucleotide diversity (see Grant & Bowen,
1998). Since then, either the bottleneck period was long, or not enough time elapsed since the
bottleneck event, to recover genetic diversity observed in other Procellariiformes taxa. It should be
noted that the islands of Madeira and Cape Verde were only recently colonised (17th century) by
man, so the bottleneck experienced by deserta must have been much before human colonisation. In
this context, the former presence of subfossils of Pterodroma attributed to feae on several islands of
northern Europe (Serjeantson, 2005; see Robb 2008 for a review) may indicate a former breeding
range much larger than the actual one, which could explain its genetic diversity. However, given the
morphological similarities between madeira, feae and deserta, it is almost impossible to ascertain
which of these taxa these fossils or historical records refer to until genetic investigations are carried
out with ancient DNA to complete past genetic diversity and phylogeographic history of these
populations (Lawrence et al., 2008a, b; Steeves et al, 2010).
Lack of population structure and reciprocal monophyly in nuclear intron, as opposed to
strong structuration, lineage sorting and monophyly in mitochondrial markers such as we found,
although striking, is not a unique case in birds (Zink & baroowclough, 2008). Lee & Edwards
(2009) showed that sister species red-backed (Malurus melanocephalus) and white-winged
(Malurus leucopterus) fairy wrens in Australia are still sharing their nuclear genes, but not their
mtDNA. These authors argued that such pattern can be expected to be common for bird species that
recently diverged or had large ancestral populations. This can be due to the differences in
coalescence time for nuclear and mitochondrial loci (Moore, 1995; Palumbi et al., 2001) and their
different effective population sizes (Zink & Barrowclough, 2008) leading to incomplete lineage
sorting. Our data suggest that ancestral populations were not especially high, but divergence times
are extremely recent, thus giving credit to this hypothesis. Alternatively, the discrepancy between
mitochondrial and nuclear markers in macaronesian petrels could also be explained by extreme
female philopatry associated with a nuclear gene flow through males (Zink & Barrowclough, 2008).
However male-biased dispersal has never been documented in petrels and, in birds in general,
female dispersal is dominant (Newton, 2003), so this explanation is unlikely. In addition, estimating
potential gene flow between the three petrel populations indicated the absence of migration between
populations. In consequence, it seems reasonable to explain the lack of reciprocal monophyly and
128
population structure in nuclear introns by an incomplete sorting of nuclear lineages due to very
recent divergence of macaronesian petrels populations
Population divergence
North-Eastern Atlantic petrels apparently diverged in the last 150,000 years. These estimates
are about 10-15 times younger than previous published estimates (Zino et al., 2008, Jesus et al.
2009). Therefore the use of nuclear loci, in addition to the more traditional cytb, resulted in un (or
less) biased estimates of divergence time, and argue for a necessary integration of nuclear loci in
phylogeographic studies (see also Lee & Edwards, 2008, Edwards & Bensch, 2009).
This fairly recent divergence may also explain, to some extant, the incredible similarity
between the three taxa: contrary to fairy wrens that exhibit marked phenotypic differences despite
their recent divergence (Lee & Edwards, 2008), in the three Pterodroma studied here, biometric
differences occur on average but with some overlap, and they are not diagnosable at sea (Shirihai et
al., 2010; see also Bretagnolle, 1995; Zino et al., 2008 and Jesus et al., 2009 for details on
morphology and colourations in these taxa). Such similarity between divergent lineages can be
maintained by selection when populations are facing similar ecological conditions (Price, 2008).
Furthermore, because these are nocturnal, burrowing species, visual cues such as morphology or
colouration probably do not play a role in population differentiation processes in these petrels.
However, even in calls these populations do not show significant differences despite the importance
of this trait as pre-mating isolation mechanism in burrowing petrels (Bretagnolle, 1995, 1996).
Alternative hypotheses to explain population differentiation and absence of gene flow in these
populations could possibly involve olfactory capabilities of Procellariiformes, breeding allochrony
and non-physical barriers to dispersion. While it has been shown that petrels show odour
preferences towards their kin/breeding partner (Bonnadonna & Nevitt, 2004; Mardon &
Bonnadonna, 2009), the potential role of olfaction in population differentiation remains to be
described. Breeding allochrony on the other hand has been shown to be an important isolating
mechanism in seabirds (Monteiro & Furness 1998; Smith & Friesen 2007). The three taxa studied
here are allochronic breeders: feae lay in December-January (Bannerman & Bannerman, 1968;
Cramp & Simmons, 1977; Jesus et al., 2009), deserta lay in July-August (Bourne, 1957; Zino &
Zino, 1986; Zino et al., 2008; Jesus et al., 2009) and madeira lay in May-June (Bourne, 1957; Zino
& Zino, 1986; Zino et al., 2008), possibly in relation to adaptation to local oceanographic
conditions. Friesen (2007), Steeves et al. (2005) and Gomez-Diaz et al. (2006) identified nonphysical barriers to gene flow as playing a role in population differentiation in seabirds. It should be
noted that the last 150,000 years (the time period during which all splits occurred) have been
129
marked by a succession of glacials/interglacials events that were accompanied by important sea
level and temperature fluctuations that affected oceanic conditions such as thermohaline circulation
(Lambeck et al., 2002; Bintanja et al., 2005). Glaciation events and sea level fluctuations in the
Pleistocene were already shown to have a strong impact on population structure and also led to
speciation in several high latitude seabirds, such as penguins (Ritchie et al., 2004), skuas (Ritz et
al., 2008) and Giant petrels (genus Macronectes; Techow et al., 2010). In the order
Procellariiforme, Pleistocene variations in climatic conditions have been repeatedly outlined in
population structuring and differentiation (e.g., Austin et al., 2004; Cagnon et al., 2004; GomezDiaz et al., 2006). In our study, population split estimates (i.e. 110,000 and 37,000) fall during two
periods of climatic cooling and oceanic changes : about 120,000 years ago the earth experienced a
warmer than present interglacial, followed by the last major glaciation whose inception stage started
around 115,000 years ago, a period that saw temperatures drop dramatically (Bintanja et al., 2005).
Later, other cycles of small glacial/interglacial occurred that weer accompanied by several periods
of high ice-cap instability linked with sea water temperature drop and changes in oceanic conditions
(Heinrich events). One of these occurred between 35,000 and 40,000 years ago (Lambeck et al.,
2002). However, this seemingly coincidence in divergent times and climatic major changes must be
relativised by the wide confidence intervals in our estimates. Nevertheless, given the cycles of
glacial/interglacial periods and number of Heinrich events in the last 150,000 years (Lambeck et al.,
2002), it is likely that taxa divergence occurred concomitantly with one of these climatic instability
event.
Because fossil data are missing and because seabirds, contrary to terrestrial taxa, can be
extirpated and recolonise an island many times depending on environmental conditions,
establishing a scenario of colonisation of the Eastern Atlantic archipelagos is difficult. However,
our data impose a complete re-evaluation of the generally accepted phylogeographic scenario
explaining the presence and distribution of the macaronesian petrel taxa. Previous to 115,000 years
ago, a petrel population, ancestral to the three currently occurring taxa, was present in the NorthEastern Atlantic (whether it was on all, only parts, or none of the Macaronesian archipelagos can
not be ascertained due to lack of fossil evidence). Following the climatic change with sea level and
temperature drops starting around 115,000 years ago, this ancestral population split into two
populations, one of these being the current population of Madeira (though again, it could have been
breeding elsewhere at that time), the other the ancestral population on Cape Verde. These two
populations probably faced different oceanic and climatic conditions, and thus migration between
the two populations ceased, allowing their differentiation by adaptation to local environmental
conditions and/or drift. Hence, following the cold climate episode, these two populations would
have sufficiently diverged to stay separated despite potential secondary contact. Population
130
expansion detected in the genetic history of these birds can then be explained by an expansion
during one of the warm climatic events that followed the split. During this range expansion, we
propose that Bugio would have been colonised by a very few birds from Cape Verde (hence lower
diversity in the former). During the cold climatic event characterised by cold North Atlantic waters
and ice-rafted debris in North and South Atlantic around 38,000 years ago (Lambeck et al., 2002),
these two populations became isolated and diverged. However, the estimates of Ne by IMa2 (c.
2,000) contradict this view, although the confidence interval is large. An alternative possibility
would be that the ancestral feae/deserta population was highly structured geographically before the
split, and the observed poor diversity in deserta could be the result of this structure: while feae
conserved the diversity of several sub-populations, deserta conserved the diversity of a single
population. A last hypothesis could be that deserta experienced a bottleneck after the split with
feae, thus reducing the genetic diversity in this population. Our proposed scenario must remain, at
this time, highly speculative: it is nonetheless more plausible than the supposed scenario (twin
colonisation of Madeira archipelago from Cape Verde), which conflicts with our genetic evidence.
In conclusion, we have seen that the use of nuclear intron sequence data in addition to
mitochondrial data revealed a population history considerably more complex than what was
originally though by using mitochondrial DNA only. The use of nuclear loci and Isolation with
Migration model allowed us to re-examine the divergence pattern of these three populations of
seabird in a new light and reinforced the call for the role of multilocus statistical phylogeography
investigations to describe complex patterns of population differentiation.
Aknowledgements
We would like to thank Annie Tillier from the Service de Systématique Moléculaire
(MNHN, Paris) for her help during the laboratory work. This work was supported by the
"Consortium National de Recherche en Génomique", and the "Service de Systématique
Moléculaire" of the Muséum National d'Histoire Naturelle (CNRS UMS 2700). It is part of the
agreement n°2005/67 between the Genoscope and the Muséum National d'Histoire Naturelle on the
project "Macrophylogeny of life" directed by Guillaume Lecointre".
131
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Gould’s petrel
©”Chris” southportpelagics.wildiaries.com
140
CHAPTER 5: PHYLOGEOGRAPHY OF GOULD’S PETREL
(Pterodroma leucoptera) and
PRELIMINARY TAXONOMIC INVESTIGATIONS IN GOULD’S
(Pterodroma leucoptera) AND COLLARED PETREL
(Pterodroma brevipes)
.
141
Part 1: Phylogeography of a threatened seabird taxon, the
Gould’s petrel (Pterodroma leucoptera)
Gangloff B et al. in prep
Abstract
Gould’s petrel (Pterodroma leucoptera) is a small gadfly petrel breeding in Eastern Australia and
New Caledonia, whose two populations are considered subspecies. It is classified as Vulnerable and
populations are supposed in decline in New Caledonia due to introduced predators. In Australia, the
population survives only through intensive conservation management on the two breeding islands.
Subspecies were defined on phenotypical criteria (morphology, colouration, breeding range and
dates) but their phylogenetic relationships are unknown. Information on genetic relatedness and
structure is important for the conservation of both populations. In this study we used five markers (2
mitochondrial and 3 nuclear) to investigate whether these populations are genetically isolated from
each other. Lineage sorting, was not found in any marker. Furthremore, estimation of gene flow
indicated that these populations exchanged migrants after splitting, although this flux was not
significant. Despite lack of lineage sorting, a weak but significant population structure was found in
nuclear markers but not in mitochondrial loci.
This study shows tht the two Gould’s petrel populations are not genetically independent from ech
other. This needs to be taken into account when devising conservation strategies for the two taxa.
Keywords
mtDNA, nucDNA, Pterodroma, petrel, New caledonia, Australia, CO1, Cytb, intron,
phylogeography
142
Introduction
The currently occurring 6th mass extinction, that affects all types of organisms, combined
with the period of global change that the biosphere is entering into give more and more importance
to the field of Conservation Biology in the starting 21st century (Hedrick, 2001). This field aims at
protecting biological diversity and the processes that sustain it (Moritz, 2002) and is a
multidisciplinary field of which conservation genetics and molecular phylogenetics are important
components. In the last 20 years, the rapid progresses of molecular biology techniques have given a
crucial importance to these last two fields for the long term persistence of biodiversity (Ehrlich &
Wilson, 1991; Hedrick, 2001), leading Daugherty et al. (1990) to argue that “taxonomies are not
irrelevant abstractions, but the essential foundations of conservation practice”. As a consequence,
genetic tools are more and more used for conservation biology objectives. However, in the case of
wild fragmented populations of threatened species, use of these tools is still in an early stage and
biodiversity conservation at the species or subspecies level still requires identifying proper
conservation and taxonomic units and the relationships between them (Frankham, 2010). When
management decisions are critical for the survival of a taxon, in both the short and long terms,
information on the genetic differences and demographic history, phylogenetic relationships and
potential gene flow between these units is necessary to adopt appropriate management options
(Miller et al., 2009). Indeed, in endangered taxa, evaluation of such parameters can help
maintaining current levels of diversity (Roques & Negro, 2005) and avoid genetic diversity loss,
through inappropriate conservation strategies promoting the merging of diverging genetic lineages,
thus helping these taxa survive future environmental fluctuations (Bouzat, 2010). Such information
are particularly important for populations that have suffered demographic decline and when
interpopulation migrations might be reduced by large geographical separation and/or differences in
foraging ecology or natal philopatry and breeding site fidelity (Irwin & Gibbs 2002; Martinez-Cruz
et al. 2004; Pearce 2007) such as commonly observed in seabirds, particularly in family
Procellariidae.
Gadfly petrels of the genus Pterodroma (family Procellariidae) are pelagic seabirds showing
extreme dispersal abilities and extreme philopatric behaviour (Brooke, 2004). From these two
characteristics, these birds may be expected to exhibit either highly structured populations due to
their breeding site fidelity, or unstructured population due to migration promoted by their dispersal
abilities. It has appeared from several studies (e.g. Friesen et al., 2006, Rayner et al., 2010) that the
presence of population structure might be dominant, even in the absence of physical barriers to gene
flow (Friesen et al., 2007). Furthermore, 21 of the 32 recognised Pterodroma species now have an
unfavourable conservation status, i.e. Vulnerable, Endangered or Critically Endangered (Birdlife
143
International, 2010) hence rendering conservation actions particularly important for the survival of
these taxa and maintenance of their evolutionary history and potential. Sub-genus cookilaria
(Bonaparte, 1855; Imber, 1985) comprises six to eight species (Bourne 1983, Imber 1985) breeding
in the south Pacific. Only two of these breed on more than one archipelago including Gould’s
petrels (Pterodroma leucoptera). This petrel comprises two discrete populations (Fig. 5.1.1) now
treated as subspecies (Imber & Jenkins, 1981; de Naurois, 1978): P. leucoptera leucoptera
(hereafter called leucoptera) breeding on two small islands in Western Australia, Cabbage Tree and,
more recently, Boondelbah (Priddel & Carlile, 1997), the other P. l. caledonica (hereafter called
caledonica) breeding in the main island of New Caledonia, in at least two mountain massifs
(Bretagnolle, 2001). However, the situation is rendered more complex by the presence in the
Australs archipelago of a Pterodroma colony (first discovered by R. & J. Seitre in 1989, identified
by V. Bretagnolle) whose taxonomic affiliation is still to be clarified but that could belong to
caledonica (V. Bretagnolle pers. obs.) and the capture in the Society archipelago of three birds
identified as belonging to either P. leucoptera or to the closely related Collared petrel P. brevipes.
Taxonomic identity of these birds, which could come from breeding colonies established in the
Society, need to be assessed by genetic means. Gould’s petrel is classified as Vulnerable due to its
small breeding range, small number of breeding locations and likely predation by introduced
mammals (Birdlife International, 2010). The leucoptera population in Australia was estimated at
around 200-500 breeding pairs in the 1970’s (Fullagar, 1976) and about 250 breeding pairs in the
1990’s (Priddel & Carlile 2009). Although the population possibly fluctuated in the first half of the
century (Hindwood & Serventy, 1941) it remained low throughout the years. Following intensive
conservation actions started in the 1990’s, breeding success was improved and the population
slowly started to increase, reaching currently more than 750 breeding pairs (Priddel & Carlile,
2009). Despite this conservation success, the status of the population remains critical and further
management is still needed to ensure its survival. In New Caledonia a bird was collected by
McMillan in 1931, but it is only when a breeding colony was discovered by deNaurois in 1977 that
the morphological and colouration differences with Australian birds were noticed. Imber & Jenkins
(1981) finally raised this population to subspecies status, based on birds collected in Northern New
Zealand and on the work of DeNaurois. Seven colonies of caledonica are currently known, each
with an approximated 100-200 pairs, but sightings at sea suggest greater number of colonies and
much greater number of birds (Villard et al., in prep.). Although morphological and colouration
differences between the two forms are recognised (listed in Imber & Jenkins 1981), overlap in these
two criteria does exist (Bretagnolle, unpubl. data) and the phylogenetic relationships between the
two taxa remain unstudied. Given the unfavourable status of the two populations and the threats
144
they face in their respective habitats, investigation of possible gene flow and exchange of birds
would bring important information for the management and conservation of the two taxa.
It is now widely recognised that single-locus investigations, although having proven much utility
and promoted great advances in the description of phylogenetic relationships between taxa, can
sometimes lead to evolutionary diagnosis errors (Forister et al., 2008) and need to be improved by
use of several loci if one wants to investigate complex evolutionary patterns that are shaping taxa’s
history such as gene flow, population divergence or effective population sizes (Edwards, 2009). In
this context, by means of five molecular markers, two mitochondrial genes and three nuclear
introns, we investigated the molecular ecology of Gould’s petrels from New Caledonia and
Australia in order to (i) infer their phylogenetic relationships and degree of divergence, (ii)
investigate populations’ divergence and demography history by means of Isolation with Migration
models and molecular diversity indices, (ii) check potential gene flow between the population that
might prevent the two lineages from differentiating.
Figure 5.1.1 Breeding locations of Gould’s and Collared petrels populations
caledonica
leucoptera
145
Feathers were collected on live adult birds in New Caledonia in a single breeding colony, in
breeding season 2005-2006. They were stored in 70% ethanol then frozen at -20°C until processing.
Blood samples were collected on Australian birds from 2005 to 2008. Blood was collected from the
veins on the leg or wing using microcapillaries and, as for feathers, was stored in 70% ethanol then
frozen at -20°C until processing. Samples of skin from birds of Society Archipelago, small pieces of
skin were cut from the palm. Total genomic DNA was extracted using the DNeasy Tissue
Extraction Kit (Qiagen, Valencia, CA, USA) following manufacturer’s instructions except that we
increased the time of proteinase digestion to 8h overnight to ensure complete digestion.
Cytochrome
Oxydase
1
gene
(CO1)
was
amplified
using
primers
F1B
5’-
Primers
FIB-BI7U
and
FIB-BI7L
5’-
For the five markers, sequencing was conducted under BigDyeTM terminator cycling
conditions at the “Genoscope - Centre National de Séquençage”, France. DNA sequences were
aligned using CodonCode Aligner 3.0.3 (CodonCode Corporation, 2009) and ClustalW (Thompson
et al., 1994) as implemented in Mega version 4 (Tamura et al., 2007) and checked by eye
Sequences are deposited on GeneBanks under accession numbers XXXXXX.
146
Genetic distances in CO1 can be used when investigating species delineation in the Barcode
of Life project (Hebert et al., 2003a, 2003b, 2004). We therefore computed corrected mean K2P
distances between taxa, and within taxon K2P distances in Mega v.4 using the “Pairwise Deletion”
option with CO1 data.
Because phylogenetic trees obtained with all markers did not differentiate the two Gould’s
petrel taxa (data not shown), we used haplotype network reconstruction to infer relationships
between populations of caledonica and leucoptera. These methods, contrary to phylogenetic tree
reconstruction, do not impose bifurcating relationships between sequences and are well suited for
intraspecific investigations (Posada & Crandall, 2001; Forister et al., 2008). For all five markers,
phylogenetic relationships between haplotypes were inferred using the median-joining networks
method implemented in NETWORK v4.5.1 (Bandlet et al., 1999; Fluxus Technology Ltd, 2009).
Estimate of genetic diversity, genetic structure and historical demography
To estimate haplotypes of nuclear loci, the software PHASE 2.1.1 (Stephens et al. 2001) was
used as implemented in DnaSp v5 (Librado & Rozas, 2009). The latter was then used to estimate
haplotypes of all five loci for use with Arlequin. Nucleotide diversity (π) and haplotype diversity
(Hd) were calculated with Arlequin v3.5 (Excoffier & Lischer, 2010). This software was also used
to conduct an exact test of population differentiation (Raymond & Rousset 1995) and test for
population structure with an AMOVA. Furthermore, to assess whether selection has affected the
loci examined and also to test for past population expansion, Fu’s Fs (Fu, 1997), Tajima’s D
(Tajima, 1989) and Ramos-Onsins & Rozas R2 (Ramos-Onsins & Rozas, 2002) tests of neutrality
were performed using 1,000 simulations in Arlequin and DnaSp. We considered Fs statistic
significant when the P value was below 0.02 to take into account the potential irregular behaviour
of this indice (Fu, 1997; Ramos-Onsins & Rozas, 2002). Significant negative values of Fs and D
statistics point towards population growth and/or positive selection, whereas positive Tajima's D
values indicate bottlenecks and/or balancing selection. R2 test was found to be more powerful than
the two previous tests in most cases by its authors (Ramos-Onsins & Rozas, 2002) and significant
value suggests population growth. We also investigated historical demography of P. leucoptera
using mismatch distribution under a model of sudden range expansion with Harpending’s
raggedness index (Harpending, 1994) and the sum of squared deviation SSD (Schneider et al.,
2000) between the observed and expected distributions. A significant SSD value indicates a
147
departure from the model of sudden population expansion (Schneider & Excoffier, 1999) and low
values of r usually reflect a population expansion.
Estimation of gene flow, population divergence time and effective population sizes
Given the distance between breeding localities and the philopatric behaviour of most petrel
taxa, one would expect an absence of migration between Australian and New Caledonian breeding
colonies. Potential gene flow between populations may homogenise the gene pool of caledonica
and leucoptera and prevent the divergence of these two lineages, a process that could have
important conservation consequences. Using IMa2 (Hey, 2010), we estimated this parameter
between the two populations. This software allows one to test for gene flow between any number of
populations between two and ten and requires the user to provide a topology of the populations it
wishes to study. Several runs were sampled with a burn-in period of 4.105 steps and sampling
period of 2.106 steps, using 10 to 100 chains and a geometric heating scheme. In order to scale
parameter estimates in demographic units, we provided the software with a range of mutation rates
for the two mitochondrial loci, using a mean value of 0.794 ± 0.115% per million year for CO1
(Pereira & Baker, 2006) and 1.89 ± 0.35% for Cytb (value found for Procellariiformes; Weir &
Schlutter, 2008). We let IMa2 calculate mutation rates scalars for the other loci (i.e. no mutation
rate was specified for the three nuclear introns, as these are currently unknown). In addition, to
evaluate the degree of gene flow, divergence time and population sizes in demographic unit, it is
also possible to enter the generation time of a taxon. Pterodroma petrels are, as all other
Procellariiformes, long-lived seabirds. No precise estimation of generation time currently exists for
Gould’s petrels. However, in the Atlantic petrel (Pterodroma incerta) this parameter is estimated to
be 20 years (Cuthbert, 2004) and in another genus belonging to Family Procellariidae, Scott et al.
(2008) found two estimates of generation time in the sooty shearwater (Puffinus griseus) of 14.7
and 21.6 years respectively based on different adult survival assumptions. We consequently decided
to use an approximate generation time of 15 years for Pterodroma leucoptera petrels. Runs were
monitored by observing effective sample size (ESS) values and inspecting parameter plots for
trends following the manual recommendations.
148
Results
Phylogenetic relationships
Mean intra-taxon K2P distances with gene CO1 were 0.29% and 0.21% in caledonica and
leucoptera respectively. The two taxa were virtually undifferentiated when calculating the mean
corrected K2P distance between the two groups (0.02%). Calculating the overall mean K2P distance
between all sequences of caledonica and leucoptera gave a value of 0.23%.
When analysing Gould’s petrels with a haplotype network approach, all three nuclear introns
produce a network characterised by the presence of two dominant haplotypes and a star-like
repartition of mostly private haplotypes differing from the main ones by one or two mutations (Fig.
5.1.2). Furthermore, in each network, the main haplotype is shared by birds from both populations
of Gould’s petrel, and with βFibint7 and CSDE1, it is the case also for the second main haplotype.
The βFibint7 network differs from the others by the presence of one Caledonian haplotype
separated from the main one by a putative unsampled haplotype and five mutational steps. The
observed structure of the nuclear intron networks is typical of populations that expanded recently
from small or limited numbers of founders (Avise, 2000), a pattern congruent with what can be
deduced from genetic diversity indices. The CO1 haplotype network presents a structure similar to
that of nuclear introns in that it is dominated by one haplotype shared by individuals of both
populations and a star-like “radiation” of haplotypes. More of these are shared between the two
populations than what is found with the nuclear loci. The cytochrome b network is somewhat
similar to that obtained with CO1. However, with Cytb only one dominant haplotype is recovered
and its prevalence is not as strong as what is found with CO1 or the nuclear introns. Most private
haplotypes are differentiated from their closest neighbour by two or more mutations.
Overall, all networks are characterised by one or two dominant haplotypes shared between the 2
taxa. Although private haplotypes are present for both Gould’s petrel taxa in all five loci, no lineage
sorting appears in any of the loci. All networks are characteristic of populations that experienced a
rapid expansion.
149
Figure 5.1.2 Haplotype networks obtained with five loci in P. leucoptera leucoptera and P.
leucoptera caledonica. Circle size proportional to number of individual sharing that haplotype. Bars
across line joining haplotypes represent mutational differences.
150
Genetic diversity and population structure and history
Mitochondrial loci
With both loci caledonica and leucoptera populations show high haplotypic diversity (Hd>0.5;
Table 5.1.1) and low nucleotide diversity (i.e. π <0.5%; Grant & Bowen, 1998). This type of pattern
can be linked to a scenario of population expansion following a period of low effective population
size, population growth enhancing the retention of new mutations (Avise et al., 1984; Grant &
Bowen, 1998). In leucoptera, all indices indicate a population increase, although Fu’s Fs is not
significant with CO1 (Table 5.1.1). Conversely, in caledonica all but SSD tests support a stable
population and departure from the population growth scenario. However, in this taxon, non
significant negative values of Tajima’s D and Fu’s Fs in CO1 and of Tajima’s D in Cytb suggest a
potential selective sweep or population increase. In terms of population structure, with both markers
pairwise FST are small and not significant (Table 5.1.2) and the exact test of population
differentiation of Raymond and Rousset (1995) is not significant in Cytb, and only weakly
significant in CO1. In addition, the genetic variation is mainly due to within population variation
(>95%) rather than to interpopulation variation (<5%) as shown by AMOVA (Table 5.1.3), with
virtually no interpopulation variation with Cytb data.
Nuclear intron
With the three nuclear markers, caledonica population exhibits high haplotypic diversity and
low nucleotide diversity (Table 5.1.1) thus supporting a hypothesis of population expansion
following a population low. Neutrality indices corroborate the scenario of population growth, all
markers showing either two or three of the Fu’s Fs, Tajima’s D or Ramos-Onsins and Rozas R2
significant. This is also supported by the mismatch analysis with the three markers. (Table 5.1.1).
Thus we observe a contradiction between mitochondrial and nuclear loci for this taxon.
In leucoptera significant Fs and R2 with βFibint7 and PAXIP1 both indicate that this taxon
experienced a population expansion. Although not significant, negative values of D with the three
markers indicate such an increase or a potential selective sweep. High haplotypic and low
nucleotidic diversities observed with these two markers also suggest that following a low
population size this taxon experienced an expansion. However, the signal given by the various
indices with CSDE1 is somewhat confusing relative to the two other introns: with this gene,
negative values of Fs and D would suggest a potential population increase, as does SSD value,
while non significant R2 and high raggedness index value do not support the population expansion
hypothesis. However, under a scenario of population expansion, R2 value is supposed to be low
151
(Ramos-Onsins & Rozas, 2002). Here with CSDE1, R2 value in leucoptera is 0.054, i.e. similar to
the significant values found with the other markers in this taxon (Table 5.1.1), thus suggesting that a
potential population growth.
As found with mitochondrial loci, most of genetic variation (>90%) is found within populations
rather than among populations (AMOVA; Table 5.1.4) with the three nuclear introns. Similarly, FST
values are low (Table 5.1.3). However, contrary to mitochondrial loci, pairwise FST values are
significant indicating population structure between the two taxa. Furthermore, exact test of
differentiation are highly significant (P<0.01; Table 5.1.3) contrary to mitochondrial data.
Thus we observe contradicting signals between mtDNA and nucDNA: in caledonica the
former indicates a stable population, while the latter supports the hypothesis of population increase;
in addition, mtDNA contradicts nucDNA in that it detects only a weakly significant population
structure in one marker, while all three nucDNA loci indicate significant population structure
between the two taxa.
152
Table 5.1.1 Diversity and neutrality estimates and mismatch anlysis indices under a model of population expansion for two Pterodroma leucoptera
taxa. Number of sequences (N), number of polymorphic sites (Np), number of haplotypes (Nh), haplotype diversity (Hd), nucleotide diversity (Pi,
expressed as percentages, i.e. 0.001=0.1%), average number of nucleotide differences (k). Significant values (P<0.05; P<0.02 for Fu’s Fs) are in bold
Np
Nh
Hd
π%
k
Fu’s Fs
R2
Tajima’s D
r
SSD
30 11
11
0.821
0.346
2.531
-3.065
0.097
-0.733
0.102
0.033
P. leucoptera
43 34
13
0.714
0.379
2.777
-3.276
0.057
-2.101
0.078
0.026
P. caledonica
5
4
0.9
0.457
4.2
0.212
0.164
-0.197
0.41
0.178
P. leucoptera
16 20
11
0.925
0.388
3.442
-4.543
0.074
-1.726
0.054
0.02
P. caledonica
26 18
17
0.917
0.251
2.354
-13.779
0.069
-1.777
0.054
0.003
P. leucoptera
68 8
9
0.605
0.085
0.792
-4.747
0.055
-1.358
0.132
0.012
P. caledonica
20 10
8
0.774
0.333
1.6
-3.024
0.0794
-1.512
0.05
0.058
P. leucoptera
92 6
6
0.399
0.097
0.465
-2.892
0.054
-1.379
0.152
0.002
P. caledonica
12 8
5
0.667
0.308
1.47
-0.952
0.15
-1.778
0.05
0.008
P. leucoptera
72 12
11
0.61
0.245
1.17
-4.866
0.05
-1.47
0.066
0.146
PAXIP1
Cytb
CO1
P. caledonica
βFibint7
N
CSDE1
Taxon
9
153
Table 5.1.2 Pairwise FST values and exact test of population differentiation P values between
Gould’s petrel populations from New Caledonia and Australia
Pairwise FST
CO1
0.026
Exact test of differentiation
(P value)
0.045
Cytb
0.01
0.488
βFibint7
0.034
0.0001
CSDE1
0.092
0.0006
PAXIP1
0.068
0.006
Table 5.1.3 Genetic variation within and between the two Gould’s petrel taxa (AMOVA)
PAXIP1
CSDE1
βFibint7
Cytb
CO1
d.f
Among
populations
1
Sum of
Squares
2.591
Within populations
71
95.026
1.338
97.42
Among
populations
1
2.061
0.019
0.93
Within populations
19
36.462
1.919
99.04
Among
populations
1
1.42
0.022
3.43
Within populations
92
55.952
0.608
96.57
Among
populations
1
1.434
0.034
9.22
110
36.352
0.33
90.78
Among
populations
1
1.52
0.044
6.85
Within populations
82
49.611
0.605
93.15
Within populations
Variance
components
0.035
Percentage of
variation
2.58
154
Gene flow, population divergence time and effective population sizes
Using a mean exponential migration rate prior value of 0.1 yields an estimation of
population split between caledonica and leucoptera approximately 40,000 years ago (Table 5.1.4;
Fig. 5.1.3). Population size at time of splitting is estimated very high, ≈85,000 for caledonica and
≈29000 for leucoptera. However, confidence intervals (95% Highest Posterior Density) are
extremely large for all these parameters. Migration was found to happen in both directions between
populations (Fig. 5.1.3), though being more important from leucoptera to caledonica than the
reverse. However these migration rates are not significant (Nielsen and Wakeley test, P>0.05;
Nielsen & Wakeley, 2001).
Table 5.1.4 Result from Isolation with Migration analysis with IMa2. 95%HPD intervals are given
in brackets. Population estimates are for effective population Ne. “Population 1” refers to the first
taxon of the pair (first column), “population 2” to the second taxon
Divergence time
(Million years)
Population size
caledonica
Population size
leucoptera
0.041
[0.020844 – 0.23197]
84954
[31164 – 639078]
28720
[13159 – 81550]
19000
[4376 – 19744]
Figure 5.1.3 History of caledonica and leucoptera under an Isolation with Migration model, with an
exponential migration prior with mean m=0.1. Horizontal lines represent splitting times (dashed
lines for 95% HPD) and curved arrows migration per generation. Time is represented on the vertical
axis, with the sampled subspecies names given at the top of the figure at the most recent time point.
Plain line boxes give population size, and lighter blue boxes and horizontal arrows represent 95%
HPD for population estimates
155
Discussion
Phylogenetic relationships between the two Gould’s petrel taxa and taxonomic
implications
With mitochondrial gene CO1, we found that the mean distance between all specimens of
caledonica and leucoptera together was 0.23%, exactly the same value than the mean intraspecific
distance found by Kerr et al. (2007) in 643 species of North American birds, and slightly lower than
the mean intraspecific distance found in Scandinavian birds by Johnsen et al. (2010). In addition,
within group mean K2P distance was less than 0.3% in both taxa. Hebert et al. (2004) found a
maximum intraspecific difference of 1.24% in 260 species of North American birds. Thus, these
CO1 data suggest that caledonica and leucoptera are not differentiated with this gene and, if
considered in the framework of the BarCode of Life initiative (Hebert et al., 2003a, 2003b), that
they do not deserve specific status.
This conclusion is corroborated by the haplotype network analysis: these taxa do not exhibit
any lineage sorting in any of the five loci investigated. Although a sample of five loci is small
comparatively to the total number of genes of these seabirds, these results nevertheless indicate that
these population lineages have not (yet) diverged. This is supported by the values of gene flow
identified by the Isolation with Migration analysis: although non significant, contemporary or recent
migration was detected between the two populations in both directions.
This study therefore brings clear evidence that the two Gould’s petrel taxa constitute a single
Evolutionary Significant Unit (Moritz, 2002) and surely do not deserve full species status. Given
the apparent lack of genetic differentiation between the two populations, it could also be debated
whether the subspecies status is warranted. Furthermore, it appears that morphological and
colouration differences although present do show some overlap between the two taxa hence only
weakly support their subspecies status. However, it has been argued (Winker & Haig, 2010) that
good subspecies, even if described on other characters than molecular ones can have conservation
importance and be important evolutionary speaking. Here, given differences in other characters
such as breeding ranges or ecological differences, notably in terms of breeding habitat and breeding
phenology, we follow Winker & Haig and argue in favour of the retention of the actual subspecies
status attributed to the two populations.
Population history
In terms of population demographic history, leucoptera clearly exhibits signs of a rapid
population expansion, possibly consecutive to a founder event or population bottleneck. This
scenario is supported by a majority of indices in all five loci, and the structure of the haplotype
156
networks is an expected pattern for species that experienced population growth following a founder
event (Avise, 2000). On the other hand, the overall signal given by the five loci in caledonica is
somewhat more confused. This taxon could be experiencing a prolonged period of relatively
important population size, as seems to be indicated by the mitochondrial data, that would follow a
period of population expansion in a more distant past that can be observed in some nuclear loci.
The pattern of population size found with the IM analysis somewhat contradicts this
scenario: a Ne of ≈29000 does not support the idea of a founder event or bottleneck for leucoptera.
One potential explanation would consist in a population crash shortly afterward the split followed
by a rapid population expansion that would lead to the recovery of past diversity: because the IM
model assumes constant population sizes it is possible that it will not detect a bottleneck occurring
after the population divergence. Another explanation could be that, from a very large caledonica
population, a small number split and gave rise to leucoptera but due to important migration and
gene flow between the two populations and small data set used, in particular in terms of number of
loci, the IM model fails to recover the proper population and migration parameter estimates. Indeed,
all parameter estimates in the IM analysis show extremely large confidence intervals (Table 5.1.4),
suggesting caution is needed in the interpretation of these estimates.
The absence of population structure among mtDNA sequences suggests contemporary gene
flow and/or recent evolutionary connections among populations of Gould’s petrel (Slatkin &
Maddison, 1989). The IM analysis supports this hypothesis: although detecting migration (i.e.,
movements between the populations), this was not significant. The pattern observed of migration
more important from leucoptera to caledonica can make sense if one supposes that leucoptera at
sea join the large populations of caledonica, which are known to forage in the Tasman Sea (Imber
& Jenkins, 1981; Thomas & Bretagnolle 1986), and very likely follow the East Australian Current
(EAC) and the Tasman Front and their associated eddies. Because the EAC bifurcates from the
coast approximately at 32°S, i.e. very close from Boondelbah and Cabbage Tree Islands (Bostock et
al., 2006) this could help leucoptera to mix with caledonica birds and promote mixing of
populations. However, data is needed on the movements of these birds in the Tasman Sea to
confirm this hypothesis. These data are currently beingcollected with GLS loggers.
Furthermore, it was shown by Birky et al., (1983) and Slatkin (1987) that an exchange of
just one or two females per generation is susceptible to counteract genetic drift in the mitochondrial
genome. This could explain the opposite patterns of population structure found between
mitochondrial and nuclear loci: it can be suggested that in a small population such as leucoptera,
157
limited amount of female mediated gene flow, through immigration of caledonica females, could
affect more the mitochondrial lineage than the nuclear lineage due to the maternal inheritance of
mitochondrial DNA and its smaller effective population size compared to nuclear DNA (Ne vs 4
Ne). This would concur with the well known pattern of dominant female mediated gene flow
through female dispersal observed in most bird species (Newton, 2003).
A hypothetic phylogeographic scenario
We may propose that, following the colonisation of New Caledonia by caledonica or its
ancestral form, leucoptera is the result of a colonisation in the late Pleistocene of Western
Australian islands by New Caledonian birds that disperse widely in the Tasman Sea (Imber and
Jenkins, 1981). Given the possible contemporary or recent gene flow between populations and that
this divergence is very recent, lineages have not had time yet to diverge sufficiently, as shown by
their low genetic differentiation and the presence of shared haplotypes.
In a somewhat similar pattern of population expansion signature in genetic diversity and
haplotype structure, in their study of Puffinus tenuirostris shearwaters in southern Australia, Austin
et al. (1994) observed a low diversity between individuals and that most haplotypes were shared
between all the 11 colonies investigated. Furthermore, the mean mtDNA sequence divergence
between individuals was 0.25%, i.e. just slightly above the values found in our study with CO1
data. These authors concluded that this species have probably been greatly reduced in numbers in
the past, probably through one or more bottlenecks during the last glaciation events in the late
Pleistocene, followed by subsequent expansions. It is well recognised that cycles of
glacials/interglacials during the Pleistocene are likely to have had a marked effect in seabird avian
species, through population bottlenecks and founder events during subsequent population expansion
from breeding refugia, variations in habitat availability or likely changes in foraging distribution
linked to oceanic conditions variations (Moum et al., 1991; Ovenden et al., 1991; Birt-Friesen et al.,
1992; Avise & Walker, 1998; Cagnon et al., 2004; Gomez-Diaz et al., 2006; Techow et al., 2010).
Here we find that the population split between caledonica and leucoptera would have happened
approximately 40,000 years ago during the last glaciation. The period 35-30,000 thousands years
ago corresponds to an interglacial period characterised by warmer periods and higher sea levels
(Kershaw et al., 2003) as well as warmer sea surface temperatures in the southern oceans (Barrows
et al., 2007). In addition, Kawahata (2002) showed that the Tasman Front moved several times in
latitude during the late Pleistocene. We propose that the split between the populations of Gould’s
petrel might have been promoted by these changes in oceanic conditions in the Tasman Sea and
changes of habitat availability linked to variable sea levels and variations of vegetation in relation to
climatic conditions. The present observed differences in breeding habitats, caledonica nests in
158
mountains, along river beds in the forest while leucoptera nests pretty much at sea level, in crevices
or rudimentary burrows on cabbage tree island, indicate that this species might be very adaptable in
terms of breeding environment, rendering even more complicated to hypothesise on the underlying
reasons of the colonisation of West Australian islands.
In such a scenario of a large caledonica population, from which emerges the leucoptera
population in a recent past, the pattern of nucleotide and haplotype diversity observed for
caledonica can seem surprising. However, it must be noted that one of the limitations of this study
lies in the biased sampling of caledonica colonies: indeed, not all colonies have been sampled due
to their remoteness and difficult access and that not all colonies are known in the different mountain
massifs (Villard et al., in prep.). It is likely that with an appropriate sampling of all breeding
colonies in New Caledonia, the observed genetic diversity would be higher and haplotype network
structure would be more complex for this taxon. Furthermore, sample size difference between the
two populations can also be invoked to explain in part the observed pattern: while the sample size
used for leucoptera might capture most, if not all, genetic diversity and present haplotypes in this
small population, the smaller n used for caledonica, a population supposed to be much larger than
leucoptera, might represent only a fraction of the total genetic and haplotypic diversity of New
Caledonian birds, especially so if this population is structured between colonies.
Conservation implications
In terms of conservation, it can be debated whether it is worth investing in management
actions of all sub-species or all populations within a species. Intraspecific units of conservation
were originally based on taxonomically defined subspecies (Moritz, 2002) but this has sometimes
led to incongruent subdivisions between molecular studies and recognised subspecies (Moritz,
2002). However, it has been argued that any independently evolving population is worth
conserving, even if not a species (Tobias et al., 2010). In the case of Gould’s petrel subspecies from
Australia and New Caledonia, we have shown here that they do not differ significantly genetically
in the five loci investigated, which questions the independent evolution of these two lineages and
consequently the conservation efforts directed towards one of the subspecies rather than the other.
However, ecological differences, notably in terms of breeding environment, and geographically
separated breeding grounds argue in favour of the retention of conservation efforts directed at both
populations. Indeed, even if they cannot be treated as different ESU (due to lack of mitochondrial
DNA monophyly), we argue that these populations need be conserved as different management
units for different reasons. First, the species as a whole is considered Vulnerable (Birdlife
International, 2010) and conserving geographically separated breeding colonies can greatly reduce
its extinction risk. Furthermore, even though these populations have probably exchanged genes
159
until recently and apparently do not exhibit independently evolving genetic lineages, it cannot be
ruled out that such independent evolution will eventually occur in the future. Thus investing in the
conservation of both populations can help achieve one of the goals of conservation biology, i.e. the
conservation of evolutionary potential of lineages. Finally, given the success of management and
recovery actions undertaken in Australia, and the relative ease to manage the leucoptera population
due to its location and restriction on two small islands, compared to the conservation management
difficulties inherent to the location of caledonica breeding colonies in New Caledonian mountains
and the many potential threats faced by this taxon (Bretagnolle, 2001) it is well worth preserving
what can be most easily be preserved, even if it does not represent the most important part of the
Gould’s petrel population.
Acknowledgements
This work was supported by the "Consortium National de Recherche en Génomique", and
the "Service de Systématique Moléculaire" of the Muséum National d'Histoire Naturelle (CNRS
UMS 2700). It is part of the agreement n°2005/67 between the Genoscope and the Muséum
National d'Histoire Naturelle on the project "Macrophylogeny of life" directed by Guillaume
Lecointre.
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165
Part 2: Preliminary taxonomic investigations in Gould’s
(Pterodroma leucoptera) and Collared (Pterodroma brevipes)
petrels
Gangloff et al., In Prep
Abstract
Phylogenetics and taxonomy of small Procellariformes (Aves) of genus Pterodroma present
numerous uncertainties leading to frequent revisions that can hamper conservation efforts in the
many threatened taxa. Gould’s (Pterodroma leucoptera) and Collared (Pterodroma brevipes) are
two closely related species living in the western Pacific whose differentiation based on colourations
is very complex. Gould’s petrel comprises two subspecies breeding in New Caledonia and
Australia, while Collared petrel is considered monotypic. Taxonomic confusion exists between the
two due to the presence in French Polynesia of yet to be identified birds similar in some ways to
both species, and within Collared petrel phenotypic variations question the presence of only one
taxon. In this study, based on museum samples we carried a preliminary investigation on the
phylogenetic relationships between and within the two species with the mitochondrial gene CO1. It
appears that the two species are separated based on this locus, supporting the actual taxonomic
differentiation. Within Collared petrel this preliminary evidence corroborates the assumption that
this species is likely to be made of several differentiated taxa, which could have conservation
implications.
Additional genetic analyses using complete sequences of CO1 and nuclear genes are necessary to
completely solve the phylogenetic relationships within the taxa.
Keywords
CO1, conservation, Barcode, Pterodroma, petrel, seabird, taxonomy
166
Introduction
The concept of DNA barcode, i.e. short sequences of mtDNA that consistently differentiate
species, has been advocated for several years now (Hebert et al., 2003a; Hebert et al., 2003b) and
studies using the methods have flourished, particularly in birds. In this last group, for which the
mitochondrial gene CO1 was selected as a good candidate for the Barcode project, extensive
investigations by Hebert et al. (2004), Kerr et al. (2007) or Jonhsen et al. (2010) in North American
and Scandinavian birds have proven the potential validity and utility of the method to distinguish
species. However much scepticism and critics have arisen over the years (e.g. Moritz & Cicero,
2004; Hickerson et al., 2006; or Will et al., 2005) stating among other impediments that the method
could be inappropriate to detect recently diverged, closely related sister species. This latter claim
was however refuted by Tavares & Baker (2008). In addition to its potential use in taxonomy, DNA
barcoding has already been proved useful in conservation biology, through detection of
underestimated species diversity (Lohman et al., 2010), or identification of illegal trade of protected
species, as shown in snakes (Dubey et al., 2010) or vertebrates in general (Eaton et al., 2010).
Gadfly petrels of the genus Pterodroma (family Procellariidae) are pelagic seabirds whose
conservation status is globally unfavourable, 21 of the 32 recognised Pterodroma species being
classified as Vulnerable, Endangered or Critically Endangered (Birdlife International, 2010).
Conservation actions are therefore particularly important for the survival of these taxa and
maintenance of their potential evolutionary history. However, as for many other Procellariidae, the
taxonomic statuses of these small gadfly petrels have long been debated and are still in motion
(Brooke, 2004), which complicates assessment of conservation needs of taxa.
Sub-genus cookilaria (Bonaparte, 1855; see also Imber, 1985) comprises six to eight species
(Bourne 1983, Imber 1985; Steeves et al., in prep) breeding in the south Pacific. Of these, only two
breed on more than one archipelago, i.e. Gould’s (Pterodroma leucoptera) and Collared
(Pterodroma brevipes; hereafter called brevipes) petrels (Fig. 5.2.1), two closely related taxa.
Although formerly considered a subspecies of a large Pterodroma leucoptera complex (Hindwood
& Serventy, 1941), the Collared petrel, breeding in Fiji and Vanuatu and Rarotonga (Birdlife
International, 2010), was considered a full species by Imber & Jenkins (1981) based on distribution,
polymorphic plumage and morphological variation, a classification subsequently followed by
Sibley & Monroe (1990) based on their DNA hybridisation data. This distinction was followed by
Brooke (2004) and accepted by Birdlife International in its IUCN Red List classification of birds
(Birdlife International, 2010). Gould’s petrel comprises two populations treated as subspecies
167
(Imber & Jenkins, 1981; de Naurois, 1978), one P. leucoptera leucoptera (hereafter called
leucoptera) breeding on two small islands in Western Australia, Cabbage Tree and, more recently,
Boondelbah (Priddel & Carlile, 1997), the other P. l. caledonica (hereafter called caledonica)
breeding in New Caledonia, in at least two mountain massifs (Bretagnolle, 2001). However, the
situation between these three taxa is rendered more complex by the presence in the Australs
archipelago of a Pterodroma colony (first discovered by R. & J. Seitre in 1989, identified by V.
Bretagnolle) whose taxonomic affiliation is still to be clarified but that could belong to caledonica
(V. Bretagnolle) and the capture in the Society archipelago of three birds belonging to one of these
taxa but needing to be identified by genetic means. Of these three birds, that could come from
breeding colonies established in the Society islands, two are supposedly Collared petrels, while the
third one could be a Gould’s petrel based on colourations (VB and HS pers. obs.). Furthermore, the
Collared petrel populations in Vanuatu are quite divergent in both plumage and size and might in
fact be two different taxa (Bretagnolle & Shirihai, in press). Gould’s petrel is classified as
Vulnerable, while Collared petrel is considered Near Threatened (Birdlife International, 2010),
classifications that are susceptible to change depending on the taxonomic statuses given to all these
populations. The Australian leucoptera population was estimated at around 200-500 breeding pairs
in the 1970’s (Fullagar, 1976) and about 250 breeding pairs in the 1990’s (Priddel & Carlile 2009).
Following intensive conservation actions started in the 1990’s, breeding success was improved and
the population slowly started to increase, reaching more than 750 breeding pairs (Priddel & Carlile,
2009). In New Caledonia seven colonies of caledonica are currently known, each with an
approximated 100-200 pairs, but sightings at sea suggest greater number of colonies and much
greater number of birds (Bretagnolle, 2001).
The exact relationships between Gould’s and Collared petrels and the exact phylogenetic
and taxonomic statuses of the different populations of the latter are not known at present and
investigations are needed to evaluate the degree of divergence between these taxa and reassess their
taxonomic status.
Here, using museum samples from brevipes from Fiji, Vanuatu and Rarotonga, as well as
samples from two of the three as yet unidentified birds collected in the Society archipelago (Table
5.2.1) and samples from one colony of caledonica and from leucoptera we investigated
phylogenetic relationships between these taxa with the mitochondrial gene CO1. This will
constitute the basis of a more detailed analysis to be undertaken when samples from all colonies,
including that of the Australs, are available and complete sequences of CO1 and other
mitochondrial and nuclear genes can be obtained.
168
Figure 5.2.1 Localisation of Gould’s and Collared petrels populations.
3
3
1
2
3
5
4
1
2
3
4
5
caledonica
leucoptera
brevipes
brevipes/caledonica
brevipes/caledonica
1 New Caledonia; 2 Cabbage Tree and Boondelbah Islands; 3 Vanuatu, Fiji, Cook Islands archipelagos (from
West to East); 4 Australs archipelago; 5 Society archipelago
Table 5.2.1 Origin of Museum samples used. AMNH: American Museum of Natural History, New
York USA; MTI: Musée de Tahiti et des Iles, Papeete, French Polynesia; Te Papa: Museum of New
Zealand TePapa Tongarewa, Wellington, New Zealand
Museum
AMNH
AMNH
AMNH
AMNH
AMNH
AMNH
AMNH
AMNH
AMNH
AMNH
AMNH
AMNH
AMNH
AMNH
MTI
MTI
Te Papa
Catalog number
Taxon
Origin
215400
216919
216920
216921
216923
222193
250893
250899
336700
336703
336705
336707
336709
528336
2004-3-12
2004-3-13
OR.023110
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma brevipes
Pterodroma sp
Pterodroma sp
Pterodroma brevipes
Vanuatu-Mera Lava Island
Fiji - Kandavu Island
Fiji - Kandavu Island
Vanuatu- Tanna Island
Fiji - Viti Levu Island
Society - Tahiti
Society - Tahiti
Cook - Rarotonga
169
Feathers were collected on live adult birds in New Caledonia in a single breeding colony, in
breeding season 2005-2006. They were stored in 70% ethanol then frozen at -20°C until processing.
Blood samples were collected on Australian birds from 2005 to 2008. Blood was collected from the
veins on the leg or wing using microcapillaries and, as for feathers, was stored in 70% ethanol then
frozen at -20°C until processing. For birds of Society Archipelago and brevipes specimens, small
pieces of skin were cut from the palm. Total genomic DNA was extracted using the DNeasy Tissue
Extraction Kit (Qiagen, Valencia, CA, USA) following manufacturer’s instructions except that we
increased the time of proteinase digestion to 8h overnight to ensure complete digestion.
Cytochrome
Oxydase
1
gene
(CO1)
was
amplified
using
primers
F1B
5’-
DNA of old museums samples from brevipes proved difficult to amplify and sequence.
DNA was degraded and fragment sizes for amplification were small (<200bp). Thus we used
additional primers (Table 5.2.2) to amplify these small DNA fragments. However, we nevertheless
failed to obtain useable gene sequences for some samples, and for old AMNH samples we could not
obtain whole sequences. Samples from Society Islands and Rarotonga yielded good PCR results.
Complete sequences are deposited on GeneBanks under accession numbers XXXXXX.
Sequencing was conducted under BigDyeTM terminator cycling conditions at the
“Genoscope - Centre National de Séquençage”, France. DNA sequences were aligned using
CodonCode Aligner 3.0.3 (CodonCode Corporation, 2009) and ClustalW (Thompson et al., 1994)
as implemented in Mega version 4 (Tamura et al., 2007) and checked by eye
170
Table 5.2.2 Primers used in amplification of CO1 in Pterodroma brevipes. All primers were
designed for this study
Primers CO1
R141 5’-AGCATGGGCGGTGACGATT-3’
F78 5’-ACTTATTCGTGCAGAACTTGGTC-3’
R208 5’-AGGGGGACTAGTCAGTTTCC-3’
F150 5’-CGCCCATGCTTTCGTAATAATTT-3’
R254 5’-AGCTTATGTTGTTTATACGTGGGA-3’
F207 5’-TGGAAACTGACTAGTCCCCCT-3’
R323 5’-ACCTGCTCCTGCTTCTACGG-3’
F288 5’-ACCTCCGTCCTTCCTCCTAT-3’
R416 5’-CCTGCCAGGTGGAGGGAGA-3’
F377 5’-ATCTAGCCCATGCCGGAGC-3’
R502 5’-AAGGGGGTTTGGTACTGTGA-3’
F453 5’-GGCAATCAACTTCATTACAACAGC-3’
R581 5’-AGCATGGTGATGCCTGCGG-3’
F537 5’-ACTCATCACTGCCGTCCTAC-3’
R678 5’-TGGGTGGCCGAAGAATCAG-3’
F642 5’-TGGCGGAGGAGACCCAGTC-3’
Corrected mean K2P distances between taxa, and within taxon K2P distance were calculated
in Mega v.4 (Tamura et al., 2007) using the “Pairwise Deletion” option. Groups were as follow:
caledonica, leucoptera, brevipes from Vanuatu, brevipes from Fiji and, because they were each
other closest relatives on the trees (see Results), birds from Tahiti and Rarotonga were grouped
together.
jModelTest v0.1.1 (Posada, 2008; Guindon & Gascuel, 2003) was used to evaluate the
model of nucleotide substitution that best fitted the data. We used Bayesian Information Criterion
(BIC), which penalises overparamerisation more heavily than AIC therefore avoiding unnecessary
complex models (Sullivan & Joyce, 2005).
Phylogenetic relationships were estimated using Bayesian Markov Chain Monte Carlo
(MCMC) phylogenetic analyses with MrBayes v3.1.2 (Huelsenbeck & Ronquist, 2001; Ronquist &
Huelsenbeck, 2003). Default priors of MrBayes 3.1.2 were used for MCMC parameters. We used
three heated chains and one cold chain for all analyses and runs were started with random trees.
Two independent MCMC runs were conducted with 4.106 generations for each run for all five
171
markers. Trees and parameters were sampled every 100 generation. Standard deviation of split
frequency were used to assess stationarity, with the average standard deviation of split frequencies
expected to approach zero when the two runs converge onto stationarity distribution (Ronquist et
al., 2005). Additionally the potential scale reduction factor should approach one when runs
converge. For each run the first 25% were discarded as burn-in.
In addition, Mega v.5Beta was used to build a Maximum Likelihood tree using the Nearest
Neighbour Interchange heuristic method. Missing data was treated with the “Partial Deletion”
option with a site coverage cutoff value of 95% and using all codon positions.
Both BI and ML trees were rooted with a sequence from Puffinus puffinus.
At the intraspecific level, processes such as recombination can produce reticulate
relationships. Such reticulations are not taken into account by molecular phylogenies because these
are constructed under the assumption that the data arose through a branching process. However,
phylogenetic networks can incorporate reticulations and display them (Huson, 1998; Posada &
Crandall, 2001). We therefore used SplitsTree 4.11.3 (Huson & Bryant, 2006) to produce a
phylogenetic network with our CO1 data. We used uncorrected ‘p’ genetic distances and the
NeighborNet method (Bryant & Moulton 2004). Besides, we performed a bootstrap analysis with
one thousand replicates. We also tested for recombination with the pairwise homoplasy index (PHI)
statistics (Bruen et al., 2006) implemented in SplitsTree 4.11.3.
Results
The mean intra-taxon K2P distances recovered are shown in Table 5.2.3. Caledonica and
leucoptera are virtually undifferentiated, based on net K2M distance (<0.01%), while they are both
differentiated from Polynesian (Society /Rarotonga) birds by distances of respectively 1.6% and
1.5% (Table 5.2.4). Birds from Rarotonga/Society show a striking difference in their net distance
with both brevipes groups defined here: while the difference is only 0.8% with Vanuatu birds, it is
twice as more important with Fiji birds.
172
Table 5.2.3 Mean within group K2P distances in Gould’s and Collared petrels populations (and
standard errors)
Rarotonga Society
brevipes Vanuatu
brevipes Fiji
caledonica
leucoptera
d
0.001
0.009
0.005
0.003
0.002
s.e.
0.001
0.003
0.004
0.001
0.001
Table 5.2.4 Net K2P distances between groups of CO1 sequences from Gould’s and Collared
petrels below diagonal and standard errors above diagonal.
Rarotonga_tahiti brevipes_Vanuatu brevipes_Fiji caledonica leucoptera
Rarotonga_Society
0.004
0.005
0.005
0.005
brevipes_Vanuatu
0.008
0.001
0.006
0.006
brevipes_Fiji
0.016
0.001
0.004
0.004
caledonica
0.016
0.013
0.018
0.000
leucoptera
0.015
0.013
0.017
0.000
Table 5.2.5 shows pairwise K2P distances between all but caledonica and leucoptera birds.
This table allows understanding the pattern of intra-group mean distance found in Table 5.2.3:
among birds from Fiji, two exhibit much higher genetic distance than other birds in the study, while
the third Fiji bird seems very close from specimens from Rarotonga/Society. A somewhat similar
pattern is found in birds from Vanuatu, four birds collected near Mera Lava Island showing distance
of less than 1% with Rarotonga/Society birds; three collected on Mera Lava and Tanna Island have
a K2P distance comprised between 1.2 and 1.5% with Rarotonga/Society birds, and three from
Tanna Island are separated by more than 2% from birds from Polynesia. The pattern between birds
from Fiji and Vanuatu is more complex (Table 5.2.5).
173
Table 5.2.5 Pairwise K2P distances between CO1 sequences from brevipes birds from Vanuatu, Fiji and Rarotonga below diagonal (standard errors
above diagonal).
OR.023110
OR.023110_Rarotonga
2004-3-12_ Tahiti
2004-3-13_ Tahiti
216923_Mera Lava
216920_Mera Lava
216921_Mera Lava
222193_Mera Lava
215400_Mera Lava
216919_Mera Lava
336700_Tanna
336703_Tanna
336707_Tanna
336709_Tanna
336705_Tanna
250899_Kandavu
528336_Viti Levu
250893_Kandavu
2004-3-12
0.001
0.001
2004-3-13 216923 216920 216921 222193 215400 216919 336700 336703 336707 336709 336705 250899 528336 250893
0.001
0.004
0.004
0.004
0.005
0.005
0.005
0.007
0.007
0.012
0.011
0.008
0.005
0.008
0.008
0.000
0.004
0.004
0.004
0.005
0.005
0.005
0.007
0.007
0.012
0.011
0.008
0.005
0.008
0.008
0.004
0.004
0.004
0.005
0.005
0.005
0.007
0.007
0.012
0.011
0.008
0.005
0.008
0.008
0.000
0.000
0.004
0.004
0.000
0.000
0.005
0.009
0.007
0.007
0.004
0.000
0.000
0.003
0.005
0.002
0.002
0.000
0.005
0.009
0.006
0.004
0.004
0.000
0.004
0.004
0.004
0.003
0.005
0.006
0.009
0.009
0.006
0.004
0.000
0.005
0.006
0.005
0.007
0.007
0.011
0.009
0.010
0.000
0.008
0.006
0.003
0.000
0.006
0.012
0.006
0.005
0.005
0.008
0.005
0.003
0.006
0.009
0.009
0.005
0.004
0.000
0.005
0.000
0.014
0.000
0.003
0.000
0.000
0.007
0.011
0.006
0.008
0.006
0.007
0.006
0.017
0.013
0.011
0.000
0.009
0.006
0.007
0.013
0.009
0.008
0.011
0.007
0.008
0.005
0.001
0.000
0.004
0.004
0.004
0.007
0.007
0.007
0.000
0.007
0.007
0.007
0.000
0.005
0.008
0.008
0.008
0.004
0.008
0.004
0.012
0.012
0.012
0.004
0.002
0.007
0.011
0.012
0.012
0.012
0.000
0.002
0.005
0.008
0.005
0.014
0.014
0.014
0.000
0.000
0.007
0.007
0.000
0.003
0.015
0.015
0.015
0.008
0.008
0.011
0.015
0.011
0.011
0.000
0.020
0.020
0.020
0.013
0.013
0.013
0.020
0.020
0.013
0.020
0.020
0.020
0.020
0.020
0.007
0.007
0.013
0.013
0.007
0.013
0.000
0.007
0.029
0.021
0.021
0.021
0.010
0.006
0.012
0.020
0.009
0.009
0.003
0.015
0.027
0.006
0.008
0.008
0.008
0.004
0.004
0.004
0.000
0.008
0.004
0.000
0.011
0.020
0.007
0.015
0.008
0.008
0.008
0.000
0.000
0.000
0.008
0.008
0.000
0.000
0.008
0.000
0.013
0.016
0.008
0.042
0.040
0.040
0.000
0.007
0.012
0.011
0.012
0.012
0.017
0.011
0.013
0.013
0.018
0.008
0.000
0.000
174
In both ML and BI trees built with CO1 data, caledonica and leucoptera are
differentiated from brevipes and from the birds of Tahiti. The ML analysis sorts the two taxa,
but with non significant bootstrap support (<50%), while the BI analysis does not sort these
specimens at all (Fig. 5.2.2). In both trees, birds from French Polynesia and Rarotonga are
grouped with strong support.
No significant recombination was detected by the PHI (P=0.29). Data are therefore
appropriate for a phylogenetic network analysis. The analysis recovers two groups with a
good support (bootstrap value =73.6) corresponding to the leucoptera/caledonica population
on one side and to the brevipes on the other (Fig 5.2.3). One brevipes sample from Viti Levu,
Fiji, is found with the leucoptera/caledonica. Within brevipes, the position of Polynesian
birds (French Polynesia, Rarotonga) is confirmed as being slightly differentiated from other
birds in the taxon. The network show a sorting of birds from Vanuatu: specimens from Mera
Lava are grouped together while birds from Tanna in the same archipelago, although related
to birds from Mera Lava, seem more distant. Specimens from Fiji exhibit a peculiar
distribution in the network, one individual being grouped with Gould’s petrel taxa, while one
is found related to birds from Mera Lava. The third specimen from Fiji is more distantly
related to the other brevipes and is linked to birds from Tanna.
Figure 5.2.3 Haplotype network obtained with CO1 in Pterodroma caledonica, P. leucoptera,
and P. brevipes. Labels indicate island of origin and reference number for birds from AMNH.
Boostrap support value for the split between caledonica/leucoptera and brevipes is shown
leucoptera/caledonica
P.spp Tahiti
caledonica
brevipes-Rarotonga
leucoptera
brevipes-Fiji
brevipes-Vanuatu
175
A
B
leucoptera
caledonica
brevipes Vanuatu
brevipes Fiji
unknown Society
brevipes Rarotonga
95
99
99
97
53
85
Tanna 336703
Viti Levu 528336
Tanna 336707
Tanna 336705
96
Tanna 336709
Tanna 336700
Kandavu 250893
56
Mera Lava 221193
96
Kandavu 250899
64 Mera Lava 216923
Mera Lava 216921
Mera Lava 216920
Mera Lava 216219
Mera Lava 215400
100
Mera Lava 216921
Mera Lava 222193
Kandavu 250899
Tanna 336703
Mera Lava 216923
Kandavu 250893
Mera Lava 215400
Mera lava 2116920
Tanna 336700
tanna 336705
Tanna 336709
Mera Lava 216919
Tanna 336707
Viti Levu 528336
100
176
0.01
0.5
Figure 5.2.2 Phylogenetic analysis of Pterodroma caledonica, P. leucoptera, P. brevipes and
as yet unidentified Pterodroma based on complete (caledonica, leucoptera, unidentified
birds) and partial sequences of CO1 mitochondrial gene. Both trees rooted with a sequence
from Puffinus puffinus. Island of origin and museum label are given for brevipes samples
from Fiji and Vanuatu
A- Molecular Phylogenetic analysis by Maximum Likelihood method, based on the
HKY+G model conducted in MEGA v.5Beta. Values above branches show bootstrap
support; branches with less than 50% support are collapsed.
B- Bayesian Inference phylogenetic analysis using the HKY+G model with MrBayes.
Posterior Probabilities superior or equal to 95% are shown above branches.
Discussion
The mean distance between all specimens of caledonica and leucoptera together was
0.23%, exactly the same value than found by Kerr et al. (2007) in 643 species of North
American birds. In addition, within group mean K2P distance was less than 0.3% in all groups
but brevipes and the mean distance between each of the Gould’s petrel taxa and the birds from
Rarotonga/Society and the brevipes groups was above 1.3%. In Scandinavian birds average
intraspecifc distance of 0.24% was found (Johnsen et al., 2010) and Hebert et al. (2004) found
a maximum intraspecific difference of 1.24% in 260 species of North American birds. Thus,
this CO1 data comfort the view that Gould’s and Collared petrels are different species, but
conclusions based on this single marker need to be treated cautiously and additional loci will
be needed to draw firm conclusions. In addition, birds from Rarotonga/Society are well
differentiated from caledonica and leucoptera and clearly placed within the brevipes group by
our data. This is corroborated by both phylogenetic trees, as well as by the haplotype network.
These birds from Polynesia are somewhat separated from other brevipes individuals, which
call for further investigation. In particular, it would be worth investigating whether these
Polynesian birds exchange genes with their Fijian and Vanuatu counterparts or if they
constitute a genetically isolated population. The relationships between brevipes birds seem
more complex. This might be due to the presence of different taxa, as expected based on
morphological differences observed between birds in Vanuatu (Bretagnolle & Shirihai, in
press). The haplotype network suggests that, although closely related, brevipes from North
(Mera Lava Island) and South (Tanna Island) Vanuatu might be slightly differentiated
genetically. In addition, the position of the three birds from Fiji in the haplotype network is
intriguing: although finding one bird in each “group” of birds from Vanuatu is not surprising
given the slight genetic difference between birds from North and South Vanuatu, the position
177
of the bird from Viti Levu grouped with the caledonica/leucoptera is unexpected. This could
be explained by a misidentification of the specimen. However this bird was closely inspected
by two of us (VB and HS) and its taxonomic identification did not seem erroneous based on
phenotypic inspection. Alternatively, this bird might bear the sign of a hybridisation between
Gould’s and Collared petrels, the hybrid exhibiting a brevipes-like phenotype (size,
colourations) despite carrying some genetic material from Gould’s petrel maternal lineage.
Cases of hybridisation between closely related Pterodroma taxa have been documented, of
which probably the most interesting is the hybridisation involving one taxon from the Atlantic
Ocean (P. arminjoniana) and one from the Pacific Ocean (P. neglecta) on Round Island,
Indian Ocean. From microsatellite data, Brown et al. (2010) showed that there is admixture
between the two taxa living on this island and that hybridisation is common. Brown et al.
(2010) also documented the case of a hybrid chick that hatched from a mixed morpho-type
pair of birds. Genetic analyses confirmed the chick to be a true hybrid. These authors consider
this case as an example of secondary contact between two closely related species following
long-distance dispersal. Given the closer proximity of Gould’s and Collared petrels breeding
grounds and their apparent close genetic relationship, hybridisation between these two taxa
seem plausible. However this hypothesis will need confirmation with more genetic data.
Furthermore, these results might also be caused by the small fragments of CO1 gene used that
could fail to recover the real divergence and relationships between the brevipes birds.
Nevertheless, this preliminary work strongly make the case for additional investigations using
complete sequences of CO1 gene, as well as multiple nuclear loci to investigate in more
details the relationships between brevipes populations and their relationships with Gould’s
petrel taxa.
This study used DNA extracted from old museum specimens. As could be expected
from working with specimens that were not prepared or stored with genetic studies in mind,
this DNA was degraded making it difficult to recover fragments of more than 200bp and
resulted in low amplification success, as also found by Zimmermann et al. (2008). Using
small fragments of the CO1 gene as barcodes has proved successful in several taxa,
particularly in insects (Hajibabaei et al. 2006). However these authors showed that these minibarcodes were not as successful as full-length barcodes and were likely to work only in
restricted taxonomic groups. Here, we found that our restricted fragments of CO1 gene
successfully segregated Gould’s and Collared petrels, two closely related species. Although
targeting smaller overlapping regions for amplification or using DNA repair enzymes (Evans,
178
2007; Patel et al., 2010) could have permitted to recover a complete sequence of CO1 from
our brevipes samples, this investigation show that even for closely related species limited
fragments of the cytochrome oxydase 1 gene can be useful to identify species. This also
points out the utility of CO1 barcode or mini-barcode to identify species or populations that
deserve deeper investigations, a result also highlighted by Campagna et al. (2009) in their
study of the genus Sporophila (Aves Procellariiformes).
Acknowledgements
This work was supported by the "Consortium National de Recherche en Génomique",
and the "Service de Systématique Moléculaire" of the Muséum National d'Histoire Naturelle
(CNRS UMS 2700). It is part of the agreement n°2005/67 between the Genoscope and the
Muséum National d'Histoire Naturelle on the project "Macrophylogeny of life" directed by
Guillaume Lecointre. We are deeply indebted to Joel Cracraft, Curator, Paul Sweet,
Collection Manager, and Margaret Hart at the American Museum of Natural History (AMNH)
for giving us access to the collections and collecting samples from brevipes. BG also
acknowledges receipt of a Collection Study Grant from the AMNH. H. Shirihai also provided
some samples collected in the AMNH and A. Cibois collected the samples on the birds in the
Musée de Tahiti et des îles, Tahiti. Gilllian Stone from TePapa Museum, New Zealand
provided the sample for the bird from Rarotonga.
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CHAPTER 6: DISCUSSION AND PERSPECTIVES
Evolutionary biology has a major, yet not fully recognised importance in conservation
(Hendry et al., 2010). Evolutionary biology can help to describe actual diversity accurately
(fields of systematics, deSalle & Amato, 2004), but is also a framework to predict future
patterns of biodiversity (fields of phylogeography and population genetics; Avise, 2000;
Edwards, 2009).
The objective of this PhD work was to unveil the complex evolutionary history and
phylogeographic patterns of threatened gadfly petrels in a conservation biology context. This
was achieved by investigating taxonomy, phylogenetic relationships, phylogeography and
population history in these birds. The conservation genetics tools used in the study were based
on
recent
methodological
developments
that
allow an
increased
integration
of
phylogeography, molecular phylogenetics and population genetics to understand and describe
the patterns of biodiversity. Indeed, Isolation with Migration models and species tree analyses
are relatively new methods that have not yet been extensively used in the study of
phylogenetic and phylogeographic patterns in Procellariiformes.
6.I Methodological aspects
Because species are made of many genes, each of these recording a particular
demographic history, all gene trees are embedded in the species tree depicting the
relationships between species. Due to factors such as lineage sorting (or deep coalescence),
horizontal gene transfer or gene duplication (Maddison, 1997), one particular gene tree will
not necessarily match the species tree in which it is embedded. For instance, in Fig. 6.1, while
the tree of gene 2 reflects the topology of the species tree for populations 1, 2 and 3, this is not
the case for gene 1. Therefore, in systematic studies aiming at depicting the phylogenetic
relationships between species and at defining species limits, integrating information carried
by several genes will lead to a better picture of populations/species history than the use of a
single marker (Edwards & Bensch, 2009). The use of nuclear DNA in addition to
mitochondrial markers is thus crucial to progress in the knowledge of phylogeographic
processes in taxa (Knowles & Carstens, 2007; Lee & Edwards, 2008; Edwards & Bensch,
183
2009). In this PhD, the use of mitcochondrial and nuclear DNA sequences with newly
developed methods based on multispecies coalescence (species tree reconstruction) and
multiloci Immigration with Migration model proved determinant in the analysis of complex
phylogenetic and phylogeographic patterns.
Figure 6.1 Potential discordance between species tree (in black) and genes trees (red and
blue): in this case the tree of gene 1 does not depict the same history than the species tree in
which it is embedded. Therefore, basing a phylogenetic study of populations 1, 2 and 3 only
on this gene would likely lead to inferring inappropriate population/species limits
Species tree reconstruction allowed inferring the validity of Beck’s vs. Tahiti petrel
differentiation (Chapter 3) and to reconstruct the phylogenetic tree in genus Pseudobulweria
while individual gene trees produced with the three markers used in this analysis did not
recover the same relationships scenarios within the genus.
In terms of phylogeographic and population differentiation pattern, the work presented
in Chapter 4 estimates divergence time between gadfly petrels of Cape Verde, Madeira and
Desertas much more recently (c. 37,000 and 110,000 years ago between feae and deserta and
184
between madeira and feae respectively) than what had been suggested before based on a
single marker (>1.5 million years ago; Zino et al., 2008; Jesus et al., 2009; Table 6.1).
Previous estimates of divergence between these taxa used a single mitochondrial marker
(cytochrome b), a divergence rate estimation (Nunn & Stanley, 1998) that has been recently
reassessed (Weir & Schluter, 2008) and estimated divergence based on this rate and on
corrected genetic distance between taxa with this locus. This type of methodology, which
albeit have proved useful over the years, can greatly mislead about the divergence and age of
these taxa because it estimates a gene divergence rather than a population divergence. When
using coalescent based analyses, such as bayesian estimation of time to most recent common
ancestor (TMRCA), estimations differ slightly from those obtained by the previously
described methodology, notably due to the uncertainty in the value of divergence rate of
cytochrome b. For instance, preliminary analyses with software BEAST (Heled &
Drummond, 2010) indicated a TMRCA of approximately 1.5 millions years ago between
Madeira and Feae petrels with cytochrome b. This corresponds to the late Pliocene/early
Pleistocene period and approximately matches previous estimates placing the split between
madeira and feae in the late Pliocene (Zino et al., 2008). However, TMRCA differs from time
of population divergence in that it represents the time that lineages shared a common relative,
while divergence time corresponds to when populations genetically diverged from one
another. Furthermore, TMRCA of different markers will differ, nuclear markers such as intron
showing older TMRCA than smaller effective population-sized mitochondrial DNA. Also,
TMRCA will usually be older than divergence time (Jennings & Edwards, 2005). However,
rapid population structuring linked to high dispersal capacities of some taxa and Pleistocene
climate fluctuations can lead to divergence times close to TMRCAs as found by Barton
(2010) in striped skunks (Mephitis mephitis) in North America.
Interestingly, in Table 6.1 all but Friesen et al.’s (2007a) divergence dates estimated gene
divergences, and all indicated divergence times older than the last major galaciation in the late
Pleistocene that started around 110-120,000 years ago. These estimates are also older than
those of Friesen et al. (2007a) and those in the present thesis. Although these differences
might reflect the reality, these estimates indicative of TMRCA rather than of populations
divergence potentially overestimate the true population divergence time. Re-estimating these
divergence dates with immigration with migration models (or other methodology calculating
population split rather than genes’ TMRCA) and additional nuclear loci would possibly
provide more recent estimates of differentiation.
185
Furthermore, all the studies presented in Table 6.1 used only one mitochondrial locus
to estimate divergence times. These estimates were therefore based only on the maternal
lineage histories rather than on the whole population histories. However, I found that the use
of nuclear introns in addition to mitochondrial data allowed identifying patterns of lineage
divergence more complex than expected in both Macaronesian and Gould’s petrels. The
incongruent patterns found between the types of markers in both cases revealed that different
genetics lineages within populations/species do not necessarily record the same signal, a
crucial point to understand phylogeographic and population differentiation patterns. This
limitation was pointed out by Friesen et al. (2007a) and Techow et al. (2009) and it would be
worth investigating whether adding nuclear DNA data would lead to different estimates of
population divergence and histories in the taxa studied by these authors.
Table 6.1 Divergence date estimates between sister taxon in Procellariiformes based on
mtDNA markers
Taxa
Divergence date
Study
(million years)
Pterodroma madeira/feae
2.5
Zino et al., 2008
Pterodroma feae/deserta
1.75
Jesus et al., 2009
Puffinus mauretanicus/yelkouan
1
Genovart et al., 2007
Calonectris diomedea diomedea/C.d. borealis
1
Gomez-Diaz et al., 2006
Procellaria aequinoctialis/conspicillata
0.9
Techow et al., 2009
Macronectes giganteus/halli
0.2
Techow et al., 2010
Hydrobates pelagicus pelagicus/H.p.melitensis
0.55
Cagnon et al., 2004
Oceanodroma castro*
0.18 – 0.075
Friesen et al., 2007a
Diomedea exulans/amsterdamensis
0.84
Milot et al., 2007
* sympatric populations in the Azores and Cape Verde
Having said this, some limitations remain with respect to the methods used in this
PhD.
First, the number of loci, although providing an improvement relative to previous studies as
mentioned above, is limited and it can be questioned whether more loci could have improved
the results and estimates found. In two studies on Australian landbirds, Jennings & Edwards
(2005) and Lee & Edwards (2008) used more than 30 nuclear loci to estimate population
demographic parameters, timing of divergence, and phylogeography of these taxa in Northern
Australia. Both studies found that the variance in most parameters estimates decreased
quickly when using more than five loci and stabilised around 10-15 loci. Lee & Edwards
(2008) pointed that estimating the number of loci to integrate to obtain parameters estimates
186
with as low a variance as possible is difficult to predict and that it is likely to change from one
study to another. Nevertheless, these cases indicate that integrating between five and 10 more
nuclear loci in the analyses presented in this thesis could probably allow to obtain more
accurate estimates of population history and divergence parameters. The same is true for the
species tree reconstruction analysis: adding more loci might allow to obtain better supported
species trees or to obtain better resolution of the tree, even at shallow nodes (Edwards, 2009).
However, limitation in the number of loci used will disappear altogether in the near future
with the advent of new generation DNA sequencing methods delivering huge amount of data
quickly and cheaply (Metzker, 2010).
Furthermore, one of the limitations of the Isolation with Migration model as
implemented in the software IMa2 resides in that several type of demographic history cannot
be investigated. Indeed this method, by implying constant population size and constant gene
flow (if any) between the populations prevents the estimation of parameters such as
population structure, population size changes over time and potential variations of gene flow
over time between the studied populations (Pinho & Hey, 2010). These authors suggest two
solutions to overcome these issues: first, to add more parameters to the model. However,
doing so requires having a great amount of data and computing time available, rendering this
option difficult to implement. In the case of this PhD, without adding a very significant
number of loci this option would not work: the data available was already very limited to
estimate the migration and population parameters and adding more parameters would result in
unreliable results; second, to simulate data with known violations of the assumptions of the
IM model and then evaluate the performance of the program on this simulated data set. Once
again, this requires a lot of computing time.
Over the last years, a tremendous progress has been made in the development of
statistical and computational tools for reconstructing population sizes and trends through time,
estimating divergence times, migration rates and integrating all these parameters. Some
promising methods are based on Approximate Bayesian Computation (ABC). These methods
allow overcoming two limiting factors of likelihood approaches based on coalescent theory,
i.e. the restriction to simple evolutionary models due to the difficulty to calculate likelihood
functions, and the use of ever increasing amount of molecular data that is supposed to
increase even more in the near future with the high-throughput DNA sequencing. For a
general discussion of ABC and its use see Beaumont (2010), Csillery et al. (2010) for a
187
presentation of the method and software implementing it, and Lopes & Boessenkool (2010)
for a review of conservation studies using ABC approach to infer population history of
endangered populations or Cornuet et al. (2010) for the presentation of a user-friendly
software, DIYABC. At this stage however, most ABC softwares still suffer from some of the
limitations of the IM model cited above. However, it is likely that those issues will be solved
soon.. For instance, DIYABC at the moment allows inferring effective population size, timing
of population size changes and timing of divergence but assumes the absence of migration
after population diverged. Next developments will aim at allowing estimation of migration
among diverged populations (Cornuet et al., 2010). This will then constitutes a very powerful
tool to investigate more realistically population history of related taxa.
6.II Taxonomic aspects
Species correspond to a fundamental biological reality (Mayr, 1982) and accurate
delineation of their limits and thorough taxonomic identification plays an essential role in
conservation biology (Brooks & Helgen, 2010). Indeed, because taxonomic identification can
sometime fail to capture all phylogenetic diversity, for instance in cases of morphologically
criptic lineages or single marker-based investigations (Rissler et al., 2006), lineages or species
can be overlooked and disappear along with their evolutionary history and diversity.
Many studies (cf. Introduction Part 2 II.2) have tackled the phylogenetic and
taxonomic issues in the order Procellariiformes with varying success. For instance, Browne et
al. (1997) found clear support for splitting Galapagos petrel populations from the Galapagos
and Hawaii in two different species (P. phaeopygia and P. sandwichensis), and Techow et al.
(2009) identified what they considered sufficient difference between spectacled petrel and
white-chinned petrels (Procellaria conspicillata and P. aequinoctialis respectively) to elevate
the former to full species status. On the other hand, Abbott & Double (2003) although finding
some arguments in support of species status of both shy and white-capped albatrosses
(Thallasarche cauta and T. steadi) reserved their judgement on the taxonomic treatment of
these birds due to insufficient evidence. Similarly, Austin et al. (2004) although clarifying the
phylogeny of the little/audubon’s shearwater complex (Puffinus assimilis/lherminieri) by
mean of mitonchondrial DNA analyses recommended more thorough analyses of the complex
with additional markers such as nuclear genes to refine and confirm their findings.
188
In the present study, the issues concerning the validity of the genus Pseudobulweria
and its close affinity with genera Bulweria and Puffinus within Procellariidae were solved by
using both nuclear and mitochondrial markers and species tree reconstruction that integrate
phylogenetic histories of several genes. Furthermore, this study showed that the recently
rediscovered Beck’s petrel should receive full species status. This ends the debate whether it
is a subspecies of Tahiti petrel. However, for Tahiti petrel, as was the case for albatrosses
(Abbott & Double, 2003) and shearwaters (Austin et al., 2004), the evidence found here was
not sufficient to fully solve the issue concerning the validity of its currently defined subspecies and additional investigations are needed in this respect.
Additionally, the multiloci analysis of Gould’s petrel showed that the two currently
recognised taxa (subspecies) surely do not deserve full species status and even the validity of
the two subspecies is questionable if based strictly on phylogenetic evidence (but see 6.V).
Furthermore, the investigation of sole mitochondrial CO1 data underlined the separation of
Gould’s petrel and Collared petrel. However, this separation did not appear in nuclear loci
between Gould’s taxa and birds collected in French Polynesia indicating a pattern similar to
that of Macaronesian gadfly petrels: a marked differentiation of mitochondrial lineages but no
differentiation of nuclear lineages. This investigation also pointed towards a complex
phylogenetic pattern in the Collared petrel, which reflects the morphological variations found
between different populations (Bretagnolle & Shirihai, in press). Hence, further investigations
with additional loci are needed.
Similarly, despite using several markers, the investigation on Macaronesian petrels did
not allow to settle the issue concerning the taxonomic status of gadfly petrels in Northeast
Atlantic. Previous work supported the definition of three species (Jesus et al., 2009). Here, as
suggested by Abott & Double (2003) for shy and white-capped albatrosses, the results
indicate that gadfly petrels of Macaronesia are seen now in the process of speciation (grey
zone on Fig 1.2; deQueiroz, 1998, 2007), so that for some species concepts diagnostic levels
of divergence (such as in vocalisations, morphology or nuclear DNA lineages) have not yet
been reached causing the taxonomic confusion.
189
6.III Genetic diversity in Procellariiformes
Table 6.2 summarises haplotype and nucleotide diversity found in several
Procellariiformes with the two mitochondrial genes used in the course of my PhD (CO1 and
Cytb) and with Control Region and ATPase genes.
A striking feature with CO1 is that all taxa (but deserta) investigated in this PhD
exhibit (much) higher haplotype (h) and nucleotide (π) diversity than Cook’s petrel
(Pterodroma cooki). Population size of the latter in the main breeding colony (Little Barrier
Island, New Zealand) was estimated at c. 286,000 breeding pairs (Rayner et al., 2010), a lot
more than the estimated Ne found for Gould’s and Macaronesian petrels. In another colony of
Cook’s petrel, Rayner et al. (2010) found a single haplotype for all birds sampled, indicating
that overall, this taxon has a relatively low genetic diversity despite its population size.
Interestingly, values found in Deserta petrel are quite close to those of Cook’s petrel despite
the much smaller population size of the former. Although the investigation of Macaronesian
petrels presented in Chapter 4 hypothesised a founder event with small number of birds or a
bottleneck to explain the pattern observed in deserta, this is unlikely to be the case in P.
cooki, a taxon formerly found in all New Zealand, whose present colonies are but the
remnants of the former breeding range (Rayner et al., 2010). In this case, low genetic diversity
observed could be the result of the fragmentation of a formerly larger breeding range: actual
Cook’s petrel breeding colonies correspond to both extremes of a formerly wider range that
encompassed all of New Zealand (Rayner et al., 2010). These authors suggested that
historically populations of Cook’s petrel were probably connected through a haplotype cline
with the populations at the range extremes showing different haplotypes. With the loss of the
intermediate populations, these two remaining populations retained only a small portion of the
former genetic diversity of the species and different haplotypes. A similar scenario could be
proposed to explain the lower genetic diversity of deserta petrel: considering the potential
presence of feae petrel in the Canary and Selvagens Islands as well as Cape Verde and the
Desertas Islands, these last two populations are located at both extremes of the species range.
Habitat fragmentation and disappearance of the intermediate populations leave the two
populations separated. The larger Cape Verde population retains a high genetic diversity and
multiples colonies on several islands of this archipelago, while the small population on Bugio
becomes isolated with low genetic diversity. However, given the genetic diversity pattern and
190
haplotype network structure observed in deserta, the scenario involving a bottleneck or
founder event seems more likely.
With Cytb, most taxa exhibit high h and low π values that are indicative of population
growth following a bottleneck or founder event (Grant & Bowen, 1998). The exceptions are
deserta and spectacled petrel (as well as some colonies of white-chinned petrel; Techow et al.,
2009) with low h and low π values indicative of a recent bottleneck or founder event (Grant &
Bowen, 1998). The Taiko (Lawrence et al., 2008a) and southern giant petrels (Techow et al.,
2010) have both high h and π (>0.5) that are the sign of large, stable populations (Grant &
Bowen, 1998). As underlined by Lawrence et al. (2008a, 2008b), high genetic diversity in the
Taiko is puzzling and probably the result of a very large population that collapsed quickly
thus still showing genetic diversity from before the bottleneck it is now experiencing. We saw
that the haplotype diversity in madeira is very high for a species with less than 30 breeding
pairs (Zino et al., 2008). This is likely due to a rapid decline of a formerly larger population
that experienced a Pleistocene population increase, as the balearic shearwater (Puffinus
mauretanicus; Genovart et al., 2007). Southern giant petrels have experienced bottlenecks in
the past but most likely before the last major ice-age (Techow et al., 2010) and thus enough
time have passed for the population to grow and stabilise.
Other studies of Procellariiformes using different markers (e.g. Control region of the
mitochondrial genome or ATPase) show a pattern of high genetic diversity with h and π
values (Table 6.2). Like madeira and the Taiko, the short-tailed albatross (Phoebastria
albatrus), is experiencing a severe bottleneck since the 1940’s (it was believed extinct in the
1940’s) and is now listed as Vulnerable (Kuro’o et al., 2010). Like the two former taxa, it
exhibits also very high h (0.96) and π (1.3%) despite having been as low as 50-60 individuals
in the 1950’s (Kuro’o et al., 2010).
All these studies show high levels of genetic diversity of Procellariiformes in general
which is corroborated by results obtained in this thesis, although Milot et al. (2007) found low
level of diversity in polymorphic loci and heterozygosity in wandering and Amsterdam
albatrosses (Diomedea exulans/amsterdamensis respectively) and using amplified fragment
length polymorphisms. These levels can be conserved for several generations even when
facing extreme bottlenecks, like experienced by short-tailed albatross, Taiko, balearic
shearwater or Madeira petrel. The different pattern shown by taxa like deserta or Spectacled
petrels could be explained by a prolonged bottleneck that lasted long enough to finally erode a
191
formerly larger genetic diversity. Alternatively such low genetic diversity can be the result of
a recent founder event from a small group of specimen through colonisation of a new habitat
or fragmentation of a large breeding range as proposed for the Cook’s petrel (see above).
Table 6.2 Haplotype (h) and nucleotide (π) diversity found in Procellariiformes with
mitochondrial genes CO1, Cytb, Control Region and ATPase. Numbers in brackets indicate
sample sizes; Ranges indicated for studies with multiples populations in which no mean
values were provided
CO1
Pterodroma deserta (94)
Pterodrma madeira (59)
Pterodroma feae (59)
Pterodroma caledonica (5)
Pterodroma leucoptera (16)
Pterodroma magentae(90)
Puffinus mauretanicus (105)
Procellaria aequinoctialis (6 to 30)
Procellaria conspicillata (23)
Macronectes giganteus (74)
Macronectes halli (51)
Hydrobates pelagicus (65)
Pterodroma magentae(90)
Oceanodroma castro (5 to 49)
Puffinus mauretanicus (105)
Thalassarche melanophrys (56)
Thalassarche chrysostoma (50)
Phoebastria immutabilis (358)
Phoebastria albatrus (44)
Fulmarus glacialis (9 to 20)
ATPase
Pterodroma deserta (89)
Pterodrma madeira (58)
Pterodroma feae (57)
Pterodroma caledonica (30)
Pterodroma leucoptera (43)
Pterodroma cooki (26)
Cytb
Taxon (n)
Control Region
Marker
Pterodroma phaeopygia (11 to 51)
h
π (%)
Study
0.45
0.78
0.69
0.82
0.71
0.34
0.07
0.23
0.15
0.35
0.38
0.1
Rayner et al, 2010
0.22
0.54
0.56
0.9
0.93
0.68
0.81
0-0.83
0.49
0.78
0.73
0.7
0.03
0.09
0.11
0.46
0.39
0.58
0.4
0-0.29
0.09
0.5
0.2
0.4
Lawrence et al., 2008b
Techow et al., 2009
Techow et al., 2009
Techow et al., 2010
Techow et al., 2010
Cagnon et al., 2004
0.92
0.78-1.0
0.97
0.91
0.97
0.99
0.96
0.76-0.92
2.23
0.5-5.7
3.72
2.21
3.0
4.5
1.3
-
Lawrence et al., 2008b
Friesen et al., 2007a
Burg & Croxall, 2001
Burg & Croxall, 2001
Young, 2009
Kuro’o et al., 2010
Burg et al., 2003
-
0-0.05
Friesen et al., 2006
192
6.IV Population differentiation in Procellariiformes
6.IV.1 Timing of divergence
The investigations conducted in this thesis found that taxa in Northeast Atlantic gadfly
petrels diverged recently, within the last 100,000 years, while the divergence time estimates
for Gould’s petrel fall around 40,000 years ago.
These divergence time estimates can be compared to investigations in other
Procellariiformes keeping in mind the caveats and limitations mentioned in 6.I. Techow et al.
(2009, 2010) estimate the timing of divergence of Spectacled and White-chinned petrels and
of Southern and Northern giant petrels around 0.9 and 0.8 million years ago respectively, in
the middle Pleistocene. In European storm-petrels, Cagnon et al. (2004) estimated that the two
Atlantic and Mediterranean lineages coalesced between 0.35 and 0.55 million years ago,
depending on the evolutionary rate used. In the same region, Gomez-Diaz et al. (2006) place
the separation between the two Cory’s shearwater subspecies (Calonectris diomedea
diomedea living in the Mediterranean Sea, and C.d. borealis found in the Atlantic Ocean) at 1
million year, and the separation of Cape Verde shearwater (C. edwardsii) from C.d. borealis
around 0.7 million years ago. In Puffinus, Genovart et al. (2007), estimated the divergence
between balearic and yelkouan (Puffinus yelkouan) shearwaters to about one million years
ago. In the Azores and Cape Verde, Frisen et al. (2007a) estimated the divergence between
hot and cool season populations of band-rumped storm-petrel (Oceanodroma castro) between
110,000 and 180,000 years ago in the Azores and around 73,000 years ago in Cape Verde.
All divergence events in these taxa occurred during the Pleistocene and, apart from
Oceanodroma castro and the divergence estimates in this thesis manuscript, all divergences
were dated well before the last major glaciation initiated around 110,000 years ago. These
estimates underline the importance of the Pleistocene glacial/interglacials cycles in the
differentiation of taxa. The exception concerns the estimates obtained in genus
Pseudobulweria (Chapter 3). Within this genus, divergence between the black-vented and
white-vented taxa was estimated around 6-7 millions years ago, i.e. at the end of Miocene,
during a period of marked ecological changes. However, this estimate is based on very low
samples sizes and only three loci and therefore need to be taken with great caution. It would
193
seem however that this genus did not experience as great a number of taxon divergence as
observed in other Procellariiformes genera like Pterodroma or Puffinus during the
Pleistocene.
6.IV.2 Differentiation
Important debates have occurred for the last fifty years about the main processes of
population differentiation, and geographic separation has appeared to be associated to most
speciation events (Fitzpatrick et al., 2009; Coyne & Orr, 2004), including in birds (Phillimore
et al., 2008). In a review on population differentiation in seabirds, Friesen et al. (2007b)
showed that land barriers to gene flow played an important role in these organisms:
populations separated by contemporary or historical land barriers showing genetic differences
and phylogeographic structure (e.g. case of masked boobies Sula dactylatra separation by the
isthmus of Panama; Steeves et al., 2005), suggesting that allopatric differentiation is common
in this group. However, Friesen et al. (2007b) also showed that, in the absence of physical
barriers such as land masses, geographic distance and simple isolation by distance model did
not provide conclusive explanations for the observed population genetic structure in seabirds.
For instance, within archipelagos, or even within islands, several species show genetic
structure between populations, including Procellariiformes, e.g. two storm-petrels (bandrumped storm-petrel in the Galapagos islands and the Azores in Macaronesia, Smith et al.,
2007; and Leach’s storm-petrel Oceanodroma leucorhoa on Guadalupe Island, Atkey &
Gulavita unpubl. in Friesen et al., 2007b) and one gadfly petrel, the Galapagos petrel (Friesen
et al., 2006).
With reference to the role of isolation by distance, the present work corroborated the points
made by Friesen et al. (2007b): (i) in both Gould’s and Macaronesian petrels geographic
separation and isolation by distance do not explain the observed structure, or lack thereof,
between populations: in Gould’s petrel, despite an approximate distance of 2,000 km between
breeding sites of the two taxa, populations are not genetically differentiated and only weakly
structured in nuclear loci, while in the Northeast Atlantic, birds breeding at only 40 km from
each other are more differentiated between them than with the third taxon breeding c. 1,000
km away, and; (ii) as is the case for storm-petrels, gadfly petrels show complex genetic and
phylogeographic structure in both the Galapagos Islands (Pterodroma phaeopygia) and
Macaronesia Islands (P. feae, P. madeira and P. deserta) in the absence of apparent barrier to
gene flow.
194
As one potential explanation to population structure and differentiation in the absence
of past or contemporary physical barrier to gene flow, Friesen et al. (2007b) suggested that
distribution at sea during the nonbreeding season provides a barrier to gene flow in these
organisms. Indeed, in their review, all but one species with phylogeographic structure also
had population specific nonbreeding distributions. However, although potential interpopulation mixing is necessarily reduced by differences in at-sea distribution, this
phenomenon alone can not explain most of the patterns observed. For example, in the Eastern
tropical Pacific Ocean, Spear & Ainley (2007) pointed that many of the 26 distinct taxa of
Hydrobatidae, though nesting allopatrically, mix strongly at sea in both breeding and/or
nonbreeding seasons.
In Gould’s and Macaronesian petrels, if the hypothesis of Friesen et al. (2007b) holds,
we can expect to find that Gould’s petrel taxa share the same range, while gadfly petrels from
Macaronesia do not during the non-breeding season.
In Gould’s petrel, caledonica are known to disperse in the Eastern Pacific during the non
breeding season and, when breeding, these birds go as far south as southern New Zealand,
while leucoptera is mostly observed in the Tasman Sea. Although much still need to be
investigated about the distribution at sea of these two taxa, it is likely that the two populations
share the same distributions, at least some times of the year. Work is underway with GLS to
define more precisely the movements of these birds. Similarly, in the Atlantic, distribution at
sea of gadfly petrels from Macaronesia during the nonbreeding season is mostly unknown and
analyses are underway from GLS data to identify their movements at sea.
In addition to geographic separation and nonbreeding distribution, Friesen et al.
(2007b) suggested alternative causes of population differentiation. In particular, the
importance of founder events and bottlenecks is underlined by several authors advocating that
these could cause drift-induced genetic differentiation, as suggested for the shy and whitecapped albatrosses (Abbott & Double, 2003) and gull (Larus cachinnans-fuscus; Liebers et
al., 2001) cases. However, as stated in the Introduction (Part 1 I.4.1), drift alone is unlikely to
shape population differentiation and other factors are expected to be involved, such as
adaptation to local environmental conditions and selection.
The results presented here do not allow inferring conclusions on this point. Although
bottlenecks occurred in populations of petrel studied in Northeastern Atlantic and Western
195
Australia/New Caledonia, evidence is lacking to disentangle the role of these population lows
and that of selection and local adaptation in the differentiation of taxa.
6.IV.3 Pleistocene and oceanographic conditions
This PhD work showed that both Gould’s petrel in Oceania and gadfly petrels of the
Northeast Atlantic diverged during the last major ice age of the Pleistocene. The influence of
Pleistocene, marked by a succession of intense glacial/interglacial periods, on organisms is
well known, and has been well documented in seabirds: e.g. Common Murres Uria aalge
Moum et al., 1991; Fairy prion Pachyptila turtur, Ovenden et al., 1991; Thick-billed Murre
Uria lomvia, Birt-Friesen et al., 1992; Lesser Black-backed Gulls Larus fuscus, Liebers &
Helbig, 2002; European storm-petrel Hydrobates pelagicus, Cagnon et al., 2004; Calonectris
shearwaters, Gomez-Diaz et al., 2006; Giant petrels Macronectes sp., Techow et al., 2010.
The usual Pleistocene-driven population differentiation scenario (Avise & Walker,
1998) involves range fragmentation during glaciations and survival of population in isolated
refugia that evolve separately under different environmental conditions. During interglacial
events, these populations can grow and secondary contact can occur, either erasing the
differences between populations when these have not diverged enough or reinforcing isolation
when divergence is more pronounced. The importance of Pleistocene on bird populations at
various latitudes is debated, some authors arguing that in tropical/subtropical regions, where
the influence of glaciations is likely to have been less important, one should expect a stronger
population differentiation than in temperate/high latitude regions that were only recently
recolonised following deglaciation (Friesen et al., 2007b).
In the Atlantic Ocean, Macaronesia Archipelagos are located in temperate/subtropical
latitudes. Oceanographic patterns around these islands are particularly complex: the Azores
Current splitting from the North Atlantic Current flows south and then eastwards. One branch
of the current passes north of and around Madeira, then heads south between Canary Islands
and Africa, before turning west at the level of Cape Verde and entering the North Equatorial
Current. (Jia, 2000; Hernández-Guerra et al., 2001). Cyclical and seasonal variation in current
strength, as well as formation of eddies add complexity to the system. In addition, westward
flow also occurs north and south of the Azores current (Cromwell et al. 1996; Alves &
Verdière 1999; Johnson & Stevens 2000; Zhou et al., 2000; Alves et al. 2002). These current
196
patterns were formed during and following the closure of the Panama isthmus (≈3 million
years ago) and are supposed to have stayed relatively stable since then, even during
Pleistocene glacial maxima (Pflaumann et al. 2003). Westward flows might have been
dominant at some stages in the Pleistocene due to a weaker Gulf Stream (Rind et al. 2001).
Furthermore, Madeira Island is located on the sub-tropical front where cold waters from the
north meet warm waters from the south and sea surface temperatures are markedly different
between north and south coasts of the island (Zhou et al., 2000; Caldeira et al., 2002). Also,
localised upwellings do occur near the coast of the islands, in particular between Madeira and
the Desertas Islands and several eddy corridors exist, north and south of the Azores front,
south of Madeira island, and near Canary Islands (Sangra et al., 2009). Furthermore, both
Azores and Canary current are constrained between narrow bands due to sea-level gradients
(Zhou et al., 2000). In addition, during the late Pleistocene, ice-cap instability in northern
Atlantic have several time led to the discharge of extensive amount of ice in the ocean, thus
perturbing the oceanic conditions, in particular sea surface temperatures, in the region
(Lambeck et al., 2002). Oceanographic conditions around Madeira are thus particularly
complex and resemble the situation described in the eastern Pacific by Spear & Ainley (2007)
where these authors proposed that these environmental gradients played a role in the
differentiation of the 26 storm-petrel taxa present in that region.
On the other hand, oceanic conditions in the Tasman Sea seem simpler. North of New
Caledonia, between Vanuatu and the Solomon islands, South Equatorial Current flowing from
the East collides with the Queensland plateau which creates the East Australian Current
(EAC). The EAC flows south and around 32°S its upperlayer turns east and crosses the
Tasman Sea, forming the Tasman Front. The latter forms different meanders and eddies
mostly related to the north/south ridges present in the basin (Lord Howe rise, New Caledonian
Trough, Norfolk ridge, from West to East). Some deeper layers of the EAC continue south
along with several eddies (Bostock et al., 2006). Several studies have shown that during the
last glacial maxima, the Tasman Front moved north, up to 26°S (Kawahata, 2002; Bostock et
al., 2006), but that some flow continued southward. Around 11,000 years BP the front moved
south progressively reaching its current location.
In this oceanic and historical context the investigation in this thesis found a strong
population differentiation in mitochondrial lineages in gadfly petrels of the Northeast Atlantic
despite the recent divergence estimated for these taxa. However that divergence corresponded
to Pleistocene climatic fluctuations impacting oceanic conditions (mainly SST). On the
contrary, in the simpler oceanic context of the Tasman Sea, no population divergence was
197
detected in Gould’s petrel taxa despite similar estimated divergence times. Although this
remains very speculative due to lack of evidence and is not directly based on the findings of
this PhD, one can then hypothesise that environmental conditions at sea combined with
geographic isolation were the primary factors of differentiation in gadfly petrels of
Macaronesia, following the above mentioned Pleistocene population differentiation scenario:
Pleistocene glaciation-induced isolation of populations were accompanied by an adaptation to
different oceanographic conditions promoting lineages divergence. On the other hand,
Gould’s petrel populations, which did not experienced as extreme climatic and oceanographic
variations due to their tropical distribution, did not differentiate, despite breeding
approximately 2,000 km apart.
6.V Implication of this research for conservation
In Pseudobulweria, given the conservation status of Beck’s petrel, as well as the
genetic, morphological (albeit based on small sample sizes) and behavioural differences found
between this taxon and Tahiti petrel, it seems necessary to suggest full species status for these
birds. By rendering conservation needs more visible for this taxon, species rank will help
promote conservation actions and measures that will beneficiate not only this taxon, but the
whole fragile forest ecosystems where it is supposed to breed in New Ireland. Full species
status might also avoid Beck’s petrel to suffer the same fate than the extinct Tuatara
subspecies: although two species of Tuatara (Sphenodon spp.) were described in 1875,
including one with two sub-species, subsequent taxonomic grouping together of these species
led to the demise of several populations due to lack of attention. When Daugherty et al.
(1990) reassessed the taxonomy of the group and reinstated the two species, one was
surviving only on one island due to extinction of its other populations and one subspecies
described in the 19th century had gone extinct.
In the case of gadfly petrels from Macaronesia on their way to speciation (if not
already full species), this study showed that these taxa are three different Evolutionary
Significant Units. Based on this conclusion, a reassessment of the conservation status of
Deserta and Feae petrels and further conservation actions are needed. Indeed, the two taxa are
still considered subspecies of Feae petrel by Birdlife International (2010) and the species is
considered Near Threatened. Taking into account their evolutionary differences and isolation
198
leads necessarily to changing the status of Deserta petrel given its very restricted range on
only one island and its small population size.
The situation in Gould’s petrel is even more complex. Winker & Haig (2010) argue
that good subspecies, even if not characterised with molecular characters, are worth
conserving and could be important to preserve the evolutionary history and potential of the
considered taxa. Based only on the genetic results presented in Chapter 5, the two taxa are not
differentiated. The validity of the subspecies is further questioned by the subtle morphological
variations used by Imber & Jenkins (1981) and the large overlap between the populations.
However, following Winker & Haig (2010), ecological differences observed between the two
taxa (different breeding ranges, different breeding dates) support the validity of the actual
taxonomy. Given these ecological differences, managing these populations as different units
is therefore needed. However managers should bear in mind the genetic characteristics
uncovered in this study, i.e. the lack of lineage sorting between the populations, the lack of
population structure in maternal lineages and the weak but significant structure in nuclear
lineages.
The work undertaken in this PhD confirms the importance of conservation genetics for
the protection of petrel taxa and for the conservation of biodiversity in general. Only by
identifying phylogenetic lineages and by understanding their history can it be possible to
effectively conserve the current biodiversity. Failing to incorporate aspects of phylogenetics
or phylogeography as shown in this PhD can lead to incomplete knowledge of the diversity to
be protected and to an inaccurate understanding of patterns of taxonomic diversity.
6.VI Perspectives
This work shed light on several taxonomic and phylogeographic aspects in gadfly
petrels. The results opened up many interesting and urgent questions which deserve further
attention to successfully protect the taxa in question. The presented investigations could, for
instance, be complemented by work outlined in the following.
In relation with the three groups of gadfly petrels studied in this thesis, specific
investigations would be needed: (i) in Pseudobulweria: population genetic analyses are
199
needed once the breeding grounds of the three Critically Endangered taxa are discovered to
describe their population structure and history and solve the issue surrounding the validity of
Tahiti petrels subspecies; (ii) in gadfly petrels from Macaronesia: larger genetic sampling (i.e.
more nuclear loci) and taxon sampling (i.e. adding related taxa from the Atlantic Ocean) will
help disentangling the complex population and phylogeographic history of Pterodroma in this
ocean basin; (iii) in Gould’s and Collared petrel, as for Macaronesian petrels, sampling of
more loci and of all populations is needed to elucidate the phylogenetic relationships between
these taxa.
In addition to these specific lines of research, three directions could be followed on a
larger scale:
1- To gain a better understanding of patterns of differentiation in petrel
populations, investigating widely distributed taxa such as the species complex Pterodroma
arminjoniana/neglecta/heraldica/alba and comparing results with those presented in this
thesis would be highly informative. These taxa are found the Pacific, Atlantic and Indian
Oceans and phylogenetic relationships between them are particularly difficult to disentangle.
Indeed, they exhibit large ranges, lack of mitochondrial lineage sorting (Brown, 2008;
Hakoun, 2009; Fig. 6.2) and apparent hybridisation of two or three taxa on Round Island in
the Indian Ocean following long distance colonisation by P. neglecta and P. arminjoniana
(Brown, 2010). Undertaking detailed investigation of evolution of populations and gene flow
in the various populations of these taxa would provide a comparison to the patterns observed
in the more restricted taxa studied in this work.
2- Current distribution of gadfly petrels and other Procellariidae is certainly
very different to pre-human colonisation of oceanic islands throughout the world (Steadman,
2006). Genetic analyses of bone subfossils have the potential to bring important information
about past distribution and genetic diversity of taxa. In seabirds this has already been
implemented in petrels (Lawrence et al., 2008a) and boobies (Steeves et al., 2010).
Undertaking such analyses on bones from different ocean basins (e.g. Atlantic Ocean; Olson,
1975, Rando & Alcover, 2008; or Pacific Ocean; Steadman, 2006) would help understanding
former petrel distributions and diversity. Compared to other such studies on seabird would
inform about past and current patterns of differentiation in highly vagile organisms such as
seabirds.
200
3- Sets of primers to amplify nuclear introns have now been produced (e.g.
Backström et al., 2008; Kimball et al., 2009) and techniques to amplify nuclear anonymous
markers described (Jennings & Edwards, 2005; Lee & Edwards, 2008). These cuold be used
with petrel DNA obtained during this PhD (more than 1300 DNA samples from 45 different
Procellariiformes taxa). Combining such data with published genes sequences with species
tree reconstruction techniques such as *Beast (Heled & Drummond, 2010) would make the
construction of a complete phylogeny of Procellariiformes resolved at the genus, and maybe
species, level at hand.
201
Figure 6.1. Haplotype networks obtained with P. arminjoniana, P. neglecta, P. heraldica and
P. alba in two distinct studies, with different mitochondrial markers.
A- From Brown, 2008. Cytochrome b statistical parsimony network of haplotypes from
Round Island (RI) and Trindade (T). Haplotype name (upper value) and haplotype frequency
(lower value) are given within each circle. Slashes diagonal to the lines show the number of
inferred base substitutions between haplotypes when >1. Small filled circles represent
hypothetical unsampled haplotypes. Shaded areas show the proportion of each haplotype
assigned to each population
B- From Hakoun, 2009. Median Joining haplotype network obtained with gene ND3 in birds
from Pacific, Indian and Atlantic oceans. Red numbers show position and number of
mutations between haplotypes. Circles size proportional to number of individual specimens
sharing that haplotype.
A
B
202
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Appendix 1: Paper not directly related to work presented in
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210
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A THOUSAND miles from land are we,
Tossing about on the roaring sea;
From billow to bounding billow cast,
Like fleecy snow on the stormy blast:
The sails are scatter’d abroad, like weeds,
The strong masts shake like quivering reeds,
The mighty cables, and iron chains,
The hull, which all earthly strength disdains,
They strain and they crack, and hearts like stone
Their natural hard, proud strength disown.
Up and down! Up and down!
From the base of the wave to the billow’s crown,
And midst the flashing and feathery foam
The Petrel finds a home,—
A home, if such a place may be,
For her who lives on the wide, wide sea,
On the craggy ice, in the frozen air,
And only seeketh her rocky lair
To warm her young, and to teach them spring
At once o’er the waves on their stormy wing.
O’er the Deep! O’er the Deep!
Where the whale, and the shark, and the sword-fish sleep,
Outflying the blast and the driving rain,
The Petrel telleth her tale—in vain;
For the mariner curseth the warning bird
Who bringeth him news of the storms unheard!
Ah! thus does the prophet, of good or ill,
Meet hate from the creatures he serveth still:
Yet he ne’er falters:—So, Petrel! spring
Once more o’er the waves on thy stormy wing!
adapted from Barry Cornwall