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\begin{document}

\title{Sea surface temperature, rather than land mass or geographical distance,
may drive genetic differentiation in a species complex of
highly-dispersive seabirds}



\author[1]{Lucas Torres}%
\author[2]{Eric Pante}%
\author[3]{Jacob González-Solís}%
\author[2]{Amelia   Viricel }%
\author[1]{Cecile Ribout}%
\author[4]{Frank Zino}%
\author[4]{Will MacKin}%
\author[4]{Carine Precheur}%
\author[5]{Julie Tourmetz}%
\author[6]{Licia Calabrese}%
\author[7]{Teresa Militão}%
\author[3]{Laura Zango}%
\author[4]{Hadoram Shirihai}%
\author[1]{Vincent Bretagnolle}%
\affil[1]{CEBC}%
\affil[2]{LIENSs}%
\affil[3]{University of Barcelona}%
\affil[4]{Affiliation not available}%
\affil[5]{SEOR}%
\affil[6]{Island Conservation Society}%
\affil[7]{Universitat de Barcelona}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
Seabirds, particularly Procellariiformes, are highly mobile organisms
with a great capacity for long dispersal, though simultaneously showing
high philopatry, two conflicting characteristics that may lead to
contrasted patterns of genetic population structure. Landmasses were
suggested to explain differentiation patterns observed in seabirds, but
philopatry, isolation-by-distance, segregation between breeding and
non-breeding zones, and oceanographic conditions (sea surface
temperatures) may also contribute to differentiation patterns. No study
has simultaneously contrasted the multiple factors contributing to the
diversification of seabird species, especially in the grey zone of
speciation. We conducted a multi-locus phylogeographic study on a
widespread shearwater species complex (Puffinus lherminieri/bailloni),
showing highly homogeneous morphology. We sequenced three mitochondrial
and six nuclear markers on all extant populations (five nominal
lineages, 13 populations). We found sharp differentiation among
populations separated by the African continent with both mitochondrial
and nuclear markers, while only mitochondrial markers allowed
characterizing the five nominal lineages. No differentiation could be
detected within these five lineages, questioning the strong level of
philopatry showed by these shearwaters. Finally, we propose that
Atlantic populations likely originated from the Indian Ocean. Within the
Atlantic, a stepping-stone process accounts for the current
distribution. Based on our divergence times estimates, we suggest that
the observed pattern of differentiation mostly resulted from variation
in sea surface temperatures.%
\end{abstract}%



\sloppy


\textbf{Keywords}

\emph{Puffinus} , divergence, phylogeography, multi-locus, philopatry,
mito-nuclear discordance

\textbf{Introduction}

Species are cornerstone in evolution and are used as study units in both
evolutionary and conservation biology (Butlin et al. 2012, Wake 2006).
Though divergence with gene-flow has been theorized and observed (review
in Pinho \& Hey 2010), the model of allopatric speciation predominates
by far in the literature (Stroud \& Losos 2016, Butlin et al. 2012). In
this model, a physical barrier to gene flow catalyzes genetic
differentiation between populations, through selection and/or genetic
drift, eventually followed by other pre- or post-zygotic barriers (Coyne
\& Orr 2004). In practice however, the mechanisms that impede gene flow
and promote differentiation are multifactorial and still poorly
understood (Butlin et al. 2012, Price 2008). In particular, geographic
barriers alone may not explain the differentiation of populations in
highly dispersive species, e.g. marine birds (e.g. Genovart et al.
2007), birds of prey (e.g. Doyle et al. 2016), mammals (Hassanin et al.
2018) or plants (Sanz et al. 2014). Seabirds are a case in point: their
wide geographic distribution and dispersal ability should theoretically
maintain high levels of gene flow, but many seabirds show surprisingly
strong geographic population structure, a pattern attributed to their
high degree of philopatry (Abbott \& Double 2003, Smith et al. 2007a).

Present or historic landmasses were identified as the most important
barriers to gene flow in seabirds (Friesen 2015, Friesen et al. 2007a)
though they cannot explain every differentiation patterns, and other
factors such as philopatry, isolation-by-distance, or segregation
between breeding and non-breeding zones may play a role. So far,
however, no study has contrasted all these processes simultaneously.
Moreover, most previous studies were based on maternally-inherited
mitochondrial markers (Friesen 2015), while gene flow is expected to
vary between sexes given stronger male philopatry in seabirds and other
birds in general (Greenwood 1980). Sex-specific patterns of divergence
are expected, and indeed mito-nuclear discordance has been detected in
seabirds, with more genetic structure in mt than in nuDNA (Burg \&
Croxall 2001, Deane 2013, Gangloff et al. 2013, Silva et al. 2015, Welch
et al. 2011, Friesen et al. 2006, Welch Fleicher 2012, Genovart 2012,
Sonsthagen et al. 2016; but see Pons et al. 2014). Such discordance was
suggested to result from incomplete lineage sorting in nuDNA due to a
higher effective sample size than mtDNA (McKay \& Zink 2010), but other
mechanisms were proposed such as adaptive introgression of mtDNA,
demographic disparities and sex-biased asymmetries (review in Toews \&
Brelsford 2012). Disentangling these latter processes can be difficult
(McKay \& Zink 2010), thus a combined use of mitochondrial and
biparentally-inherited nuclear markers, as well as coalescent
theory-based methods using the lineage species concept coupled with
population genetics within a phylogeographic context, are advocated
(Hudson \& Coyne 2002).

Shearwaters (order Procellariiformes) are long-lived birds showing slow
demographic rates (Warham 1996). They breed in large colonies on remote
oceanic islands, are pelagic (Gonz\selectlanguage{ngerman}ález-Solís et al. 2007) and highly
philopatric (Brooke 2004). Geographic structuring of populations can be
strong (Genovart et al. 2012, Gómez-Díaz et al. 2009), and the
systematics of some taxa is still highly controversial, especially for
the little and Audubon's Shearwaters Puffinus assimilis-lherminieri
complex (Austin et al. 2004). This widespread small-sized,
black-and-white shearwater breeds from equatorial to sub-arctic seas
(see map in Fig. 1). Previous studies, based only on the mitochondrial
gene \emph{cytb} , indicated that population genetic clusters matched
with geographic distribution (Austin et al. 2004, Kawakami et al. 2018).
Using this single marker, three distinct lineages were recognized in the
North Atlantic: lherminieri in the Caribbean and off Brazil, baroli in
the Azores, Canaries and Madeira, and boydi in Cape Verde (Fig. 1).
These taxa are characterised by non-overlapping breeding and
non-breeding distributions at sea (Ramos et al. in review), and are thus
geographically and genetically (for at least one marker) separated.
Still, they are morphologically and ecologically highly similar
(Precheur et al. 2016, Calabrese et al. in review), a pattern typical of
the first stages of the speciation process, the so-called grey-zone (De
Queiroz 2007). Unsurprisingly, their taxonomic ranking has been hotly
debated, e.g. baroli being considered as belonging to assimilis
(Shirihai et al. 1995), lherminieri (Austin et al. 2004) or a species of
its own (Sangster et al. 2005). Lineages belonging to this complex are
also found in the Indian and Pacific Ocean, with populations breeding in
the Seychelles (nicolae), Réunion (bailloni) and many islands in the
Pacific Ocean (dichrous). Breeding populations of bailloni are
characterised by different breeding phenologies on the northern and
southern parts of Réunion (Bretagnolle \& Attie 1996), potentially
impacting genetic structuration (Friesen et al. 2007b). Indian Ocean
birds were alternatively considered as a P. lherminieri subspecies
(Warham 1990) or subspecies of a bailloni pan-tropical taxon (Austin et
al. 2004). The exact taxonomic status of these six lineages is thus
largely unresolved.

Here we consider these six lineages, covering Atlantic and the
Indo-Pacific branches of the Puffinus assimilis-lhermineri complex. We
sampled birds from all but one known breeding localities in the North
Atlantic (four Caribbean breeding sites for lherminieri, two main
breeding sites in Cape Verde for boydi, one breeding site in Azores,
Madeira, Selvagens Canary Islands for baroli) and in the northern Indian
Ocean (northern and southern populations of bailloni in Réunion and
Seychelles for nicolae; Fig. 1). Only a single breeding locality was
unsampled, the Fernando de Noronha archipelago, off Brazil (Fig. 1). We
analysed three mitochondrial and six nuclear markers to delineate
genetic units and investigated patterns of genetic differentiation and
divergence among populations. As conflicting geographic patterns between
mitochondrial and nuclear markers (hereon referred to as mito-nuclear
discordance; Toews \& Brelsford 2012) was systematically found in
petrels so far (references above), we inferred population structure
among females and males independently to test for sex-biased dispersal
on nuDNA. We then used multispecies coalescent inference and an ABC
framework to investigate evolutionary scenarios of breeding site
colonisation over the last million years, trying to disentangle
contrasted processes shaping evolutionary history and the contemporary
population structure of this complex, such as landmass presence,
isolation-by-distance, oceanographic conditions (sea surface
temperature) and climatic oscillations.

\textbf{Material and Methods}

\textbf{2.1 Sampling, extraction and PCR amplification of gDNA}

A total of 276 birds (all adults, i.e. having already dispersed)
spanning the entire known breeding distribution of \emph{Puffinus
lheminieri} and four populations of interest of \emph{P}
.\emph{bailloni} were selected (Supplementary Material 1). Four
individuals from the Pacific Ocean (taxon \emph{dichrous} , by far the
most widespread lineage in the Pacific (Onley \& Scofield 2007); Fig. 1)
were also used. Individuals were sexed using PCR amplification (with the
2250F and 2781R primers (Fridolfsson \& Ellegren 1999)). The overall sex
ratio was unbiased (120 females, 107 males; Pearson's Chi² with Yates'
continuity correction, p=0.42; 49 individuals could not be sexed
successfully, see Supplementary Material 1).

Total genomic DNA was extracted from blood samples (except for the
population of the Bahamas, for which samples were derived from toepads
collected on dead birds, \emph{lherminieri} 1 and 2 on Figure 1) using
NucleoSpin® Tissue XS Kit (Macherey \& Nagel, Düren, Germany). Samples
were incubated overnight in 4 mg of Proteinase K. Purified genomic DNA
was eluted twice in 50 \selectlanguage{greek}µ\selectlanguage{english}L of TE buffer pre-heated at 70\selectlanguage{ngerman}°C. DNA
concentration was measured using Nanodrop spectrophotometry. Three
mitochondrial markers (\emph{cox1} , \emph{cytb} , and the mitochondrial
Control Region, CR) and six nuclear markers (Beta-fibrinogen exon6
through 8, \selectlanguage{greek}\emph{βfib}\selectlanguage{english} ; Cold Shock Domain-containing E1
intron5,\emph{csde} ; Interferon Regulatory Factor 2 intron2,
\emph{irf2} ; PAX interacting protein 1 intron20, \emph{pax} ;
Recombination activating protein 1, \emph{rag1} ; and Tropomyosin 1
alpha exon7, \emph{tpm} ) were targeted (primer sequences, PCR profile
and conditions: Supplementary Material 2). These markers were previously
shown to be polymorphic within and between petrel species (Gangloff et
al. 2013, Silva et al. 2011).

\textbf{2.2 Quality control of genetic data}

While checking chromatograms we found several cases of double peaks on
the sequences of \emph{cox1} , \emph{cytb} and CR (Supplementary
Material 3). Bird blood contains relatively few mitochondria, and it is
therefore likely to amplify nuclear copies of mitochondrial markers, or
``numts'' (Sorenson \& Quinn 1998). Such nuclear copies may diverge from
the original mitochondrial genes since they are non-coding, which result
in double-peaks on the chromatograms. To check this scenario and avoid
such copies, we digested nuclear DNA with the ExonucleaseV (ExoV;
NEB-M0345S) and sequenced again the mitochondrial markers for all
individuals showing double-peaks for \emph{cox1} , plus 5 individuals
showing no double-peaks randomly chosen, using a protocol modified from
(Jayaprakash et al. 2015, see Supplementary Material 3). Before running
analyses, we checked that coding sequences contained no stop codons or
indels. Some analyses required phased data (e.g. *BEAST analysis), so
the gametic phase of nuclear markers was determined probabilistically
using PHASE 2.1 (Stephens et al. 2001) implemented in DNAsp v.5.10.01
(Librado \& Rozas 2009). Additional Genbank sequences (Supplementary
Material 1) were aligned to our sequences using MAFFT v 7.187 (Katoh et
al. 2002).

\textbf{2.3 Population diversity, differentiation, and divergence}

Each population was described by calculating haplotype frequencies,
inter-haplotype distances, haplotype diversity and nucleotide diversity
(\selectlanguage{greek}π\selectlanguage{english}), as well as expected and observed heterozygosity for nuclear markers
using DNAsp v.5.10.01 (Librado \& Rozas 2009) and Genetix v4.05.2
(Belkhir et al. 2004). We evaluated signals for departures from
neutrality or demographic changes by estimating Tajima's D (Tajima 1989)
and Fu's Fs (Fu 1997) for each locus, with Arlequin v.3.1 (Excoffier et
al. 2005). Differentiation among populations was estimated by performing
AMOVAs, and calculating pairwise \emph{F} \textsubscript{ST} and
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} and the population average pairwise differences
D\textsubscript{XY}, using Arlequin. For AMOVAs, samples were stratified
into five groups, corresponding to the five nominal lineages
(\emph{lherminieri} , \emph{boydi} and \emph{baroli} in the
Atlantic,\emph{nicolae} and \emph{bailloni} in the Indian Ocean), and
populations (i.e. sampling localities; Fig. 1) within these groups. The
matrix of genetic distances among all pairs of haplotypes was computed
using the K2P model of substitution for concatenated mitochondrial
markers, and TN93 for concatenated nuclear markers, as determined using
jModelTest2. We used a Mantel test to measure the level of correlation
among genetic distances and geographic distances (Smouse et al. 1986).
Geographic distance was calculated as the shortest distance between two
populations without crossing land. Statistical significance (AMOVAs,
Pairwise\emph{F} \textsubscript{ST} and Mantel tests) was estimated
using 1000 permutations. To visualize relationships among lineages, we
inferred NeighborNet networks using SplitsTree v 4.14.2 (Huson \& Bryant
2006), with different dataset combinations: all markers independently,
concatenated mitochondrial markers, concatenated nuclear markers.

\textbf{2.4 Estimation of sex-biased dispersal using nuclear markers}

To detect sex-biased dispersal, we separated sequences from females and
males into two separate datasets, excluding three populations
(Saint-Barth\selectlanguage{ngerman}élémy, Funchal and Selvagem) represented by fewer than five
individuals from each sex. Sex-biased dispersal was tested at both
intra- and inter-lineage scales, expecting females to be less structured
than males since males are presumably more philopatric (Greenwood 1980).
We calculated average pairwise relatedness for each sex, within each
population, using the triadic likelihood estimator (Wang 2007)
implemented in Coancestry (Wang 2011). To test if the difference of mean
relatedness between males and females of each population was
significant, we used the test of difference between sex by bootstrapping
samples 1000 times and recalculating difference in means between sex for
each bootstrap. Observed and simulated differences was then compared,
and if the observed difference fell outside of the 95\% confidence
interval, we considered it to be significant.

If females disperse more than males, females sampled from a single
population will be a mixture of residents and immigrants. The female
sample will therefore deviate from the Hardy-Weinberg equilibrium and
show a deficit of heterozygotes (Wahlund effect).\emph{F}
\textsubscript{IS} calculated for the female sample is thus expected to
be larger than the male \emph{F} \textsubscript{IS} (Goudet et al.
2002). We estimated \emph{F} \textsubscript{IS} separately for females
and males for all tested populations, and evaluated its significance
using 1000 permutations with Genetix. Conversely, we expect\emph{F}
\textsubscript{ST} (Goudet et al. 2002) to be higher in philopatric
males than in females. We calculated\emph{F} \textsubscript{ST} for each
pair of populations within the two datasets, with Arlequin.

\textbf{2.5 Phylogeographic scenarios}

Reciprocal monophyly was inferred on all markers as partition by gene
trees, which were used to evaluate the degree of divergence among
lineages using MrBayes v. 3.2.6 (Ronquist et al. 2012; Supplementary
Material 4). To reconstruct the scenario of divergence among the
different populations, we used species trees, inferred using two
different methods. We first ran an analysis with *BEAST v 2.2.0
(Bouckaert et al. 2014) using the three mitochondrial markers. We choose
to link time-trees for the three mitochondrial markers, since they are
on the same plasmid, where no recombination is expected at the time
scale considered in this study. However, the three markers have
different composition and different evolutionary rates, so we did not
link the molecular clock and evolution models. We tested the hypothesis
of molecular clock with the Clock Test using ML implemented in MEGA
v7.0.20 (Kumar et al. 2016). This hypothesis was rejected (p-value
\textless{} 0.0005). We therefore used, for each marker, an uncorrelated
lognormal relaxed clock model. The clock rate was fixed to 0.00553 ±
0.00115 substitution per site per million year for \emph{cox1} and
0.00631 ± 0.0035 for \emph{cytb} (rates inferred for Procellariiformes
by (Nabholz et al. 2016)). The rate for the control region was estimated
by the model as no rate is published for Procellariiformes. Existing
rates for other birds could not be used either because they belong to
groups that are too distant (e.g. Moas (Baker et al. 2005) or Peafowls
(Kimball et al. 1997)), and the control region is highly variable among
groups (Ruokonen \& Kvist 2002). Published rates for the CR were however
set in *BEAST as priors and did not affect the results (data not shown).
We used for each marker a model consistent with the result of
jModelTest2, and a Yule process species tree prior with a continuous
population size model. As for the MrBayes analysis, we ran each MCMC
chain with 50*10\textsuperscript{6} generations, sampled every 1,000
generations, the first 25\% of generations were discarded as burn-in and
we inspected the stationarity of the chains using Tracer (Rambaut et al.
2018). To test whether the colonization of the Atlantic could result
from birds of the Pacific passing through the Panama Isthmus,
individuals from the central Pacific (Marquesas archipelago,
taxon\emph{dichrous} ) were added to the *BEAST inference. This taxon
can be considered as the best representative taxon for the central
Pacific, as it is the most widespread and numerous, since
\emph{polynesiae} is considered synonym to \emph{dichrous} (Austin et
al. 2004), while\emph{bannermani} is best recognised as a species on its
own (Kawakami et al. 2018). The taxon \emph{gunax} (from Vanuatu) has
never been sequenced, and actually the location of its breeding colonies
is unknown. Three \emph{dichrous} \emph{cytb} sequences retrieved from
Genbank (AY219949-AY219951) and three individuals from our own
collection were used. We ran a second *BEAST analysis by adding all the
nuclear markers independently to the three mitochondrial markers, using
the same MCMC parameters. Clock rates priors were set to 0.019
substitutions per site per million year for \selectlanguage{greek}\emph{βfib}\selectlanguage{english} and 0.013
substitution per site per million year for \emph{rag1} , since these
rates were estimated for birds (Groth \& Barrowclough 1999; Prychitko \&
Moore 1997). The clock rate for nuclear markers was estimated by the
model as no rate has been produced for petrels. In this latter case, we
kept only the individuals for which we had sequences for all markers. To
investigate the demographic history of lineages, we estimated population
size through time by estimating Extended Bayesian Skyline plot as
implemented in *BEAST, for each genetic unit previously delimited,
considering a generation time of 15 years (Precheur et al. 2016).

We also used a coalescent-based ABC approach to explore the best
demographic scenario describing the dataset of the combined
mitochondrial and nuclear markers using the program DIYABC v. 2.1.0
(Cornuet et al. 2014). ABC methods consist in the simulation of datasets
similar to the real dataset in terms of population and marker sizes.
First, in the Indian Ocean, we tested if the three populations emerged
simultaneously in a radiation event or in two disjoint events by
comparing the posterior probability of these three scenarios, and in the
case of the latter, which population was ancestral to the two others and
which one of the two remaining populations was ancestral to the other.
This hierarchical strategy was applied to each lineage independently,
then to the three Atlantic lineages, and finally considering the five
lineages together (see Supplementary Material 5 for a description of all
tested scenarii). For each possible scenario,
10\textsuperscript{6}pseudo-observed datasets were simulated, with the
same ploidy and number of loci per population as observed in the real
dataset. We fixed uniform priors for population sizes, times of size
variation and divergence and mutation and admixture rates priors (see
Supplementary Material 4 for details), from which we simulated the
datasets. Summary statistics were calculated from the simulated datasets
and compared to the same statistics obtained from the real dataset. The
Euclidean distance was calculated between the statistics obtained for
each normalized simulated dataset and those for the observed dataset
(Beaumont et al. 2002). Posterior probability of each scenario was then
calculated using a logistic regression on summary statistics produced by
the 1\% of the simulated datasets closest to the real dataset. To reduce
the dimensionality of the data, a linear discriminant analysis was
preliminarily applied to the summary statistics (Estoup et al. 2012).
The scenario with the highest posterior probability value with a
non-overlapping 95\% confidence interval (95\% CI) was selected.

\textbf{Results}

\textbf{3.1 Patterns of genetic diversity, numts and the presence of a
duplicated region}

We obtained an average of 192 sequences per marker (length range
307-1323 bp; Table 1). Mitochondrial data produced 148 polymorphic sites
yielding 150 haplotypes, while nuclear data exhibited a total of 111
variable sites and 150 alleles (see Table 1 for summary of polymorphic
sites, haplotypes and diversities per marker). Mitochondrial markers
were twice as variable as nuclear markers, though nuclear haplotype and
nucleotide diversity in \selectlanguage{greek}\emph{βfib}\selectlanguage{english} reached a level similar to
mitochondrial haplotype diversity (Table 1).

None of the coding markers (\emph{cox1} , \emph{cytb} and \emph{pax} )
presented any insertion, deletion, nonsense or stop-codon following
translation (see Supplementary Material 1). Double peaks on Sanger
chromatograms were however detected for each of the three mtDNA markers.
While all double peaks at \emph{cox1} were removed by the exonuclease
treatment, 60 CR sequences (33\%) still showed double-peaks at 73
positions, as well as 37 positions for 22 individuals (10\%)
for\emph{cytb} . Double-peaks were not specific to any population or
sex, and were not linked to the position of the individuals in the
sequencing plate (see Supplementary Material 3). Only 12 (5\%)
individuals showed double-peaks both at CR and \emph{cytb} , so the
presence of double peaks seemed unlinked between the two markers.
Replicating DNA extractions, PCR and sequencing confirmed these results,
making laboratory contamination unlikely. Contamination in the field was
also unlikely since new sampling supplies were used for every sample.
Given that only 10\% of the \emph{cytb} sequences presented such
ambiguities (which may be due to heteroplasmy, Torres et al 2018), we
removed such sequences for further analyses expecting little impact on
the analyses. However, for the CR, since a third of the total sequences
were involved, we kept all CR data in further analyses, considering two
haplotypic phases for MrBayes and *BEAST analyses (although this is a
violation of the assumption that mixed sequences to be phased are under
Hardy-Weinberg equilibrium; see discussion below).

\textbf{3.2 Population structure and sex-biased dispersal}

Mitochondrial and nuclear results from Fu's Fs indicated no deviation
from neutrality (Supplementary Material 7). However, for Tajima's D
tests, three localities displayed significant negative Tajima's D for
all mitochondrial and nuclear markers: South Reunion (Indian Ocean, see
map in Figure 1), Saint-Barth\selectlanguage{ngerman}élemy (W Atlantic) and Raso (E Atlantic).
In addition, three localities presented significant negative Tajima's D
at mitochondrial loci only (Selvagem and Funchal, E Atlantic; South
Reunion) and two at nuclear loci only (Vila, E Atlantic; North Reunion).
Patterns of population structure at seven out of 13 localities might
therefore be influenced by selection and/or recent demographic changes,
in addition to neutral processes.

Gene trees and phylogenetic inference with *BEAST and MrBayes revealed a
hierarchical structure composed of two well-supported (posterior
probabilities PP \selectlanguage{english}[?] 0.95) reciprocally monophyletic clades corresponding
to the two oceans, within which individuals from the five lineages
further clustered into monophyletic sub-clades (Fig. 2b,c and S7). All
except one of these sub-clades (East Atlantic \emph{boydi} ) were
supported in *BEAST (PP \selectlanguage{english}[?] 0.95) using all concatenated markers. For both
mtDNA markers, and all markers concatenated, the
central-Pacific\emph{dichrous} lineage was nested within
the\emph{bailloni} /\emph{nicolae} clade, although node supports
for\emph{dichrous} position within Indian Ocean clade were weak (between
0.27 and 0.73). Assignment to an ocean basin based on nuclear haplotype
networks was however discordant from the mitochondrial data for 33
individuals, for at least one nuclear locus (Fig. 3b and S8): 15
Atlantic individuals fell closely to the Indian phylogroup, and 18
Indian Ocean individuals clustered within the Atlantic group. All of
these 33 individuals showed the mitochondrial signature expected based
on their geographical sampling location. Interestingly, a
\emph{baroli}individual showed one haplotypic phase clustering with the
\emph{baroli}phylogroup (mother) while the other haplotypic phase
(father) clustered with the \emph{nicolae} phylogroup for four nuclear
markers (the two remaining could not be assigned to any particular
lineages). This individual might be the result of hybridization,
although further analyses based on additional markers would be necessary
to detect more robustly hybridization among lineages. The ambiguous
assignment of the other individuals might be due to introgression or
incomplete lineage sorting (see below).

In parallel, we used an AMOVA framework with the five nominal lineages
now defined a priori, to examine how genetic variants partitioned among
and within these taxonomic units. Most of the genetic variance was due
to inter-lineage differentiation (88.5\% and 58.4\% for mitochondrial
and nuclear markers, respectively). The variance among sampling
localities within lineages accounted for 0.5 to 4.1\%, while variance
within sampling localities represented 11.0 to 37.5\%. Pairwise\emph{F}
\textsubscript{ST} showed consistently higher values among, than within
lineages for both marker types, with mostly non-significant values
within each lineage (Table 2a). Moreover, 24 nuclear F\textsubscript{ST}
values were found non-significant versus 10 mitochondrial
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} values (Table 2a). Population average pairwise
differences led to similar results, with high structuration for the five
nominal lineages (Supplementary Material 7).

Genetic distance increased clearly with geographic distance (Fig. 4a),
but Mantel tests were performed only between pairs within the same Ocean
(given that each Ocean taxon is likely different species). Tests
confirmed that genetic and geographic distances were strongly correlated
to each other, both for mtDNA and nuclear markers when analyzing pairs
of populations within an Ocean basin (r=0.88 and 0.70, n=45, p
\textless{}0.005 for mtDNA and nuDNA, respectively, Fig. 4a,b). Between
breeding sites and within lineages, isolation by distance could not be
reliably tested as the number of populations was too low, but visually
it seemed that there was no relation between geographic and genetic
distances (Fig. 4).

Indian Ocean populations (\emph{nicolae} and \emph{bailloni} ) showed
stronger female dispersion as indicated by significantly stronger
deficit of heterozygotes and a significantly lower average relatedness
in females (Table 3). Conversely, in \emph{baroli} ,\emph{F}
\textsubscript{IS} was significantly higher for males and they were less
related to each other than females, suggestive of male-biased dispersal.
Finally, an ambiguous pattern was found for both\emph{lherminieri}
(males had stronger deficit of heterozygotes and were significantly more
related to each other than females) and \emph{boydi}(female \emph{F}
\textsubscript{IS} was significantly higher than male\emph{F}
\textsubscript{IS}, though females were more related to each other than
for males at one sampling locality). In addition, population structure
within lineages, as measured with \emph{F} \textsubscript{ST}, was
similar between sexes, but between oceans a larger range of\emph{F}
\textsubscript{ST} values was observed for males with higher maximum
values, suggesting that males were more structured at least for some
pairs of populations (e.g. \emph{lherminieri} vs. Indian Ocean lineages;
Table 2b). Overall males seemed more structured than females between
oceans, suggesting that females disperse farther, but genetic signal for
sex-biased dispersal varied geographically: female-biased in the Indian
Ocean, male-biased or inconclusive in the Atlantic Ocean.

\textbf{3.3 Reconstructing scenario of breeding site colonization}

A split between ancestral Atlantic and Indian populations (Fig. 2a)
occurring 1.76 My ago (95\% CI range 0.99-2.60) was inferred based on
all 9 gene trees (2.71 My ago (1.17-4.72) using only mitochondrial
markers). West and east Atlantic ancestral populations split around 1.38
My ago (0.78-2.04), \emph{baroli} and \emph{boydi} split at 0.85 My ago
(0.44-1.32), and \emph{nicolae} and \emph{bailloni} at 0.70 My ago
(0.33-1.13). The *BEAST analysis based on all nine markers showed the
same topology though with generally lower divergence times and higher
confidence intervals (Supplementary Material 8). ABC analyses also
supported a similar scenario of ancestral population divergence: best
retained topologies using mtDNA markers and all nine markers suggested,
starting from oldest to newest splits, \emph{nicolae} being ancestral,
leading to the appearance of \emph{boydi} (Fig 2d, Supplementary
Material 5). Then \emph{lherminieri} diverged from \emph{boydi} ,
and\emph{baroli} diverged from \emph{boydi} . Finally,
\emph{bailloni}diverged from \emph{nicolae} (Fig. 2d Supplementary
Material 4). Our phylogenetic trees placed the Central Pacific taxon
\emph{dichrous}within the Indian clade\emph{,} thus supporting the
putative scenario of Atlantic lineages diversifying from Indian Ocean
rather than from Pacific ancestors (Fig. 2b,c). Within
\emph{lherminieri} , ABC analyses suggested a northerly stepping stone
colonization process, from Martinique to the Bahamas (Supplementary
Material 5). Similarly, the most likely scenario of population
divergence within \emph{baroli} was colonization from the Canaries to
the more northerly Azores.

Finally, Bayesian Skyline analyses inferred current effective population
sizes to be around 10\textsuperscript{4} individuals (see Supplementary
Material 10). Mean Ne seemed to increase slowly over time, but high
confidence intervals precluded detection of any sudden change in
population sizes. Confidence intervals on current population sizes were
also very large, in particular for the individuals breeding on R\selectlanguage{ngerman}éunion
(see Supplementary Material 10). The demographic parameter estimations
were consistent with a constant population size over time for each
lineage, as suggested from Fu's Fs results (which are more suitable than
Tajima's D to estimate population expansion; Ramirez-Soriano et al.
2008; Supplementary Material 7).

\textbf{Discussion}

\textbf{4.1 Mito-nuclear discordance and sex-biased dispersal}

At the inter-lineage scale, we observed more genetic structure at
mitochondrial than at nuclear loci. This dissimilarity has been observed
for numerous Procellariiformes species (e.g. Gangloff et al. 2013, Silva
et al. 2015, Welch et al. 2011) as well as other organisms (see Toews \&
Brelsford 2012 for a review). One likely explanation is a difference in
the pace of molecular evolution between mitochondrial and nuclear
markers, with the latter having a slower substitution rate than the
former, and the former being more polymorphic at the intra-specific
scale (see Brown 1985), at least in birds (Helm-Bychowski 1984; Mindel
et al. 1996). However, we did observe high levels of intra-lineage
diversity for some nuclear loci such as \selectlanguage{greek}\emph{βfib}\selectlanguage{english} (see also Gangloff
et al. 2013, Silva et al. 2015), and therefore the difference of
structuration cannot be solely attributed to a difference of marker
variability. We therefore suspect that incomplete lineage sorting and
retention of ancestral polymorphisms at nuclear loci also contribute.
Indeed, effective population size of mitochondrial DNA is four times
smaller than that of nuclear DNA due to uniparental inheritance. Lineage
sorting will therefore be faster in mtDNA than in nuDNA, being inversely
proportional to the effective population size (Funk \& Omland 2003).
Incomplete lineage sorting is actually thought to be the main cause of
mito-nuclear discordance when associated to a pattern of loss of
geographic differentiation on nuclear markers (McKay \& Zink 2010; Toews
\& Brelsford 2012). We also found patterns suggestive of introgression
in nuclear markers. Hybridisation with introgression has been documented
in shearwaters (Genovart et al. 2007, G\selectlanguage{ngerman}ómez-Díaz et al. 2009), other
Procellariiformes (Brown et al. 2010,) and other seabirds (Gay et al.
2009; Morris-Pocock et al. 2011; Pons et al. 2014). The likelihood of
Indian Ocean petrels visiting breeding Atlantic Petrels may be supported
by recent tracking of \emph{Pterodroma arminjoniana} breeding on Round
Island (Mauritius), which showed that some individuals foraged around
South Trinidad Is, off Brazil, and even in the northern Atlantic (Booth
Jones et al. 2017), although flight capacities of \emph{Pterodroma} are
far higher than \emph{Puffinus} . Introgression can also blur
phylogeographic signals by mixing alleles from distinct populations, and
is considered as the second main cause of mito-nuclear discordance
(McKay \& Zink 2010). Incomplete lineage sorting and introgression are
however difficult to distinguish, and additional unlinked markers would
be required to disentangle these phenomena. Finally, as the
mitochondrial markers represent only the female evolutionary history,
sex-biased dispersal favoring females may alternatively explain why the
population structure inferred based on nuclear markers conflicts with
female-inherited mtDNA markers (see Petit \& Excoffier (2009). These
authors suggested that the markers associated with the most dispersing
sex should better delimitate species, as they will show stronger
intra-specific gene flow from colonizing lineages, reducing the effects
of genetic drift and lowering the probability of fixating introgressed
alleles. Dispersal was indeed stronger in females in some populations,
particularly in the larger and the putatively ancestral
lineage,\emph{nicolae} . Sex-biased dispersal was however more uncertain
for\emph{lherminieri} and \emph{boydi} , while for \emph{baroli}
dispersal was inferred to be male-biased. The sample size for
\emph{baroli} was theoretically large enough to robustly detect a bias
in\emph{F} \textsubscript{ST}, \emph{F} \textsubscript{Is}, and
Relatedness (Goudet et al. 2002). Sex-biased dispersal may therefore
have further contributed to the observed mito-nuclear discordance, at
least in some lineages.

\textbf{4.2 Sequencing artifacts due to mtDNA duplication and
uncertainties about molecular clock rates}

The mitochondrial genome of several Procellariiformes presents tandem
repeats (Abbott et al. 2005; Lounsberry et al. 2015), including \emph{P.
lherminieri} (Torres et al. 2018). In this taxon, a duplicated region
comprising two copies of the CR was found, yielding the presence of
double peaks on chromatograms. By treating gDNA with an exonuclease
(which effectively removed all linear DNA), we did not observe triple-
or quadruple-peaks on chromatograms, and thus hypothesized that only two
copies of the CR sequences were amplified by PCR. Therefore we
considered two haplotypic phases for all CR sequences, assuming that
removing all CR sequences would have led to a loss of information and of
statistical power in the analyses. To check the robustness of this
approach however, we replicated all our analyses using two other data
subsets: one in which all ambiguous CR sequences were removed, and
another one from which all CR sequences were removed. Removing all CR
sequences led to a strong loss of information, an increase of the
estimated differentiation and a decrease of the estimated divergence
times (Supplementary Material 6). Removing only the ambiguous sequences
led to estimations close to the estimations of the complete dataset.
This suggests that the noise caused by the multiple copies of CR was
swamped by the signal contained in that marker and so our analyses using
all the individuals are not significantly biased by these sequences.

Another major issue concerns the choice of a molecular clock rate for
dating the splitting events. We found a clear hierarchical structuration
of populations in the Atlantic and Indo-Pacific Oceans, which diverged
around 1.76 to 2.71 My ago either using all markers or only mtDNA
markers, respectively. In contrast, using the same taxa (but fewer
specimens and only \emph{cytb} ), Austin et al. (2004) dated this split
at 3.2-3.8 My, and rather suggested that the closure of the Isthmus of
Panama erected a barrier to gene flow between Indo-Pacific and Atlantic
populations. This difference in divergence times might be due to taxon
sampling, gene sampling, or the calibration of the molecular clock
(0.631\%/My here, vs. 0.9\%/My in Austin et al). This latter parameter
is probably a major contributor to the difference observed between our
studies, since using the same value as Austin et al, and only sequences
from \emph{cytb} , we found a divergence time of 2.2-3.8 My, thus very
close to Austin et al. Many substitution rates for both Mt DNA markers
were proposed for petrels, ranging from 0.19\%/My (Pacheco et al. 2011)
to 1.544\%/My (Pereira \& Baker 2006) for \emph{cox1} , and from
0.18\%/My (Pacheco et al. 2011) to 0.88-0.92\%/My (Nunn \& Stanley 1998;
the latter rate was used by Austin et al), 1.022\%/My (Pereira \& Baker
2006) and 1.89\%/My (Weir \& Schluter 2008) for \emph{cytb} . Using
lowest rates (Pacheco et al. 2011) resulted in a divergence time between
Atlantic and Indo-Pacific lineages around 12.8 My ago (Supplementary
Material 10), an unlikely value given the presence of an ocean between
the Americas before the Isthmus of Panama erected, despite the fact
that\emph{Puffinus} is dating back to the Oligocene (Henderson \& Gill
2010). Here we used the substitution rates that were estimated in
Nabholz et al. 2016, as the most recent review. There are two further
arguments against Austin et al. (2004) scenario: first, the closure of
the Isthmus of Panama has been actually dated at 2.8 My ago (O'Dea et
al. 2016), thus later than Austin et al's scenario. Second, we found
that the pacific taxon, \emph{dichrous} , was not ancestral to the
Atlantic taxon, but was embedded within Indian ocean taxa. The use of
precise fossil calibration would bring a more robust estimation of
divergence times and so a supplemental evidence of the origin of the
colonization.

\textbf{4.3 Inferring key drivers of diversification in the small
Puffinus}

Based on our inferred date of the Indian --Atlantic lineage split, gene
exchange between Indian and Atlantic birds have apparently occurred
after the closure of the Isthmus of Panama: we suggest that this
happened through individuals going around South Africa, since African
continent is an insurmountable barrier for Procellariformes (Silva et
al. 2015). Indeed, these shearwaters are tropical or subtropical species
(at least currently). Off South Africa, until 1 My ago, sea surface
temperatures (SST) were approximately 2°C higher than today (Bell et al.
2015), suggesting that migration between Atlantic and Indian oceans may
have remained possible for such birds. From 2.0, a strong decrease of
SST occurred in both oceans (Bell et al. 2015), and gene flow between
Indian and Atlantic may thus have ceased, in agreement with our
estimated time of divergence (1.76 My ago). Once the Atlantic birds were
isolated from Indo-Pacific populations, differentiation started to occur
among Atlantic lineages 1.38 to 1.90 My ago (respectively for the 9
markers or only the mtDNA markers). This period also corresponds to a
further decrease of the SST in the Atlantic (Bell et al. 2015), a
southward shift of the subtropical front and warmer waters in the
Southern Ocean (Maiorano et al. 2009), and sea ice development in the
North extending southwards from the Arctic to the current Great lakes in
the USA (Webb \& Bartlein 1992). Cold temperatures, preventing the
colonization of potential northern breeding sites such as the Azores,
may have forced shearwaters to spread over the two sides of the Atlantic
(Fig. 2d). The third step in the colonization of Atlantic breeding
grounds concerns the divergence of \emph{baroli} from \emph{boydi,}which
would have occurred around 0.85-1.26 My ago, a period that corresponds
to a stabilization of the SST at the current level in the North
Atlantic. The ice melt may then have allowed northward colonization on
both sides of the Atlantic, from Cape Verde to the Canaries, Madeira and
the Azores, and from the lesser Antilles to the Bahamas and Bermuda.
Similar timing of divergence has been suggested between
\emph{Calonectris edwardsii} (Cape Verde) and \emph{C. diomedea}(North
Atlantic and Mediterranean) 0.7-0.9 My ago (Gómez-Díaz et al. 2006) and
\emph{Puffinus olsoni} (Canaries) and \emph{Puffinus puffinus}(North
Atlantic) 0.2-1.0 My (Ramirez et al. 2010). In the Indian Ocean, a
reversed pattern of southward colonization occurred, shearwaters
colonizing from Seychelles, partly continental in origin, to Réunion
0.70-1.01 My ago, precisely at the time strong volcanic activity ended
on Réunion (Gillot \& Nativel 1989). Mauritius was probably colonized
long before (Mauritius age: 8My; McDougall \& Chamalaun 1969), then
Rodrigues and Réunion (both about the same age, 2 My; McDougall 1971),
but shearwaters are now extinct on Mauritius and Rodrigues, following
human colonization since the 17th century, so no sample is available.
The southward movement was however not necessarily related to changes in
SST, but rather to availability of volcanic islands that eventually
emerged in a southward direction.

Mantel tests showed a strong correlation between geographic and genetic
distance between lineages, i.e. at large scale. Indeed Little
shearwaters are poor flyers compared to other Procellariiformes.
However, isolation-by-distance within each lineage was visually not
detected, therefore distance alone could not be a factor of population
divergence at this smaller scale; we argue that sea temperature could
rather be the most important factor of divergence in our case at this
scale. Seabirds indeed depend on both sea and the islands where they
breed, thus sea temperature has strong impacts on seabird phenology,
breeding, survival and abundance (see Sydeman et al. 2012 for a review).
We suggest that foraging ecology, which strongly depends on SST, is an
important process shaping divergence among lineages. The segregation of
foraging areas among populations is an important factor of
differentiation among seabirds (Friesen 2015; Friesen et al. 2007), as
shown for other shearwaters (Genovart et al. 2007; Gómez-Díaz et al.
2006), petrels (Gangloff et al. 2013; Welch et al. 2012), storm-petrels
(Deane 2013; Smith et al. 2007) and albatrosses (Alderman et al. 2005;
Burg \& Croxall 2001). Assessment of non-breeding and breeding
distributions at sea of the little shearwaters complex revealed that all
three Atlantic taxa show rather separated foraging and wintering areas
(Ramos et al. in review), and further suggest that \emph{boydi} rather
than \emph{lherminieri} was ancestral in the North Atlantic.
Indeed,\emph{boydi} is more flexible in its foraging ecological niche,
suggesting ancestral behavior, with a far larger potential distribution
at sea covering all central North Atlantic (Zajková et al. 2017, see map
in Ramos et al. in review). In addition, over the last My, SST
oscillations gradually increased in amplitude with up to five degree
difference in range (Bell et al. 2015; Herbert et al. 2011). These
oscillations were showed to correlate with changes in marine
productivity (Mart\selectlanguage{polish}ı \selectlanguage{english}et al. 2009), prey species diversity (Yasuhara \&
Cronin 2008), atmospheric circulation (Chang et al. 2000) and sea level
(e.g. in Atlantic Nascimento et al. 2011, Zazo et al. 2010). It is
likely that these oscillations also contributed to divergence in the
North Atlantic, and we suggest that significant Tajima's D tests found
in almost half of the populations studied represent traces of past
bottlenecks and population expansions as it has already been shown in
other taxa (Ramakrishnan et al. 2005; Weber et al. 2004; Zhu et al.
2006).

A last interesting side effect of our study is that it suggests that the
small black and white shearwaters have shown a very recent radiation
speciation event, with not less than 13 species radiating in just 1.46
million years since \emph{P. puffinus} , \emph{P. assimilis} and
\emph{P. newelli} clades are either embedded in \emph{lherminieri}
or\emph{bailloni} clades. All these species are rather coastal
shearwaters (compared to more pelagic species such as the larger
shearwaters), and such high rate or speciation may be the result of the
high climatic oscillation that occurred over the last 2 million years
which may have favored high rates of colonization and extinction on
coastal islands.

\textbf{4.4 Sea surface temperature as a major diversification driver in
marine organisms?}

Conversely to pelagic fishes that show no or low inter-ocean structure
(D\selectlanguage{ngerman}íaz-Jaimes et al. 2010, Ely et al. 2005) and no intra-ocean structure
(Nomura et al. 2014, Taguchi et al. 2015), sea mammal's gene flow is
shaped by sea temperature which drives structuration between ocean
basins and among breeding areas (Alexander et al. 2016, Jackson et al.
2014, Richard et al. 2018, Fontaine et al. 2014, Viricel \& Rosel 2014).
SST plays also a role in the diversification of sea turtles, since only
cold-adapted species are able to exchange genes among oceans (Dutton et
al. 1999). Finally, organisms with a pelagic larval phase show globally
low structuration (Kelly \& Palumbi 2010) but when detected,
structuration is often linked to sea temperature (Benestan et al. 2015;
Teske et al. 2005, 2018). Therefore, SST appears as a generic driver of
diversification in marine organisms, though their patterns of
structuration are generally considerably weaker (see Bowen et al. 2016
for a review) than those found here. We suggest that the reason for this
discrepancy lies in the fact that these seabirds are central place
foragers, i.e. they still depend on terrestrial habitats for breeding,
the latter being impacted for instance by glaciations. They are
therefore highly sensitive to any latitudinal change of SST in
comparison to island distribution, which acts as a constraint since an
optimal area in regard to SST may lack any island for breeding. Indeed,
in marine organisms with low dispersal abilities, patterns of
structuration and divergence are more similar to the patterns found here
on shearwaters. For instance, timing of population divergence between
Atlantic and Indian Ocean lineages and within the Atlantic in the
seahorse \emph{Hippocampus} 'kuda complex' (Floeter et al. 2007) fits to
our estimates. This species has no planktonic larval duration (Lourie et
al. 2005) and long dispersal events are considered rare and implying a
few individuals (Teske et al. 2005). Moreover, Cape Agulhas is known to
be a phylogeographic break among several costal species, due to the
difference of currents and sea temperatures between the two oceans
(review in Teske et al. 2011). On the other extreme, seabirds do not
compare either to terrestrial organisms living on islands, despite being
highly philopatric, simply because they can disperse easily, if an
island is available to being colonised.

In this small shearwater complex, geographical barriers and/or isolation
by distance may have been a major driver of differentiation at large
scale (typically, between Oceans) while SST has been a more important
driver at smaller scale (within Oceans), with shearwaters shifting their
breeding latitudes with a changing SST. However, since these seabirds
depend on the geographical distribution of their breeding islands and
because these seabirds are not the best seabird fliers, this
distribution becomes a major constraint resulting in the present
geographical structure, promoting local adaptation to small scale
ecological constraints and reducing gene flow. Therefore, petrels and
shearwaters present an interesting case study where diversification
processes rely more (or at least equally) on ecological factors, in
particular sea surface temperature, rather than distance or continental
barriers, in contrast to either ``true'' marine organisms or terrestrial
organisms. Strict marine organisms can disperse far more, or
alternatively are unconstrained by island distribution, and thus show
much less geographical structure within taxa. The terrestrial organisms
tend to disperse far less, and isolation-by-distance tends to be a main
driver of population differentiation (Meirmans 2012; Vekemans \& Hardy
2004). Indeed terrestrial organisms, such as lizards or birds in
Macaronesia (AlmalkI et al. 2017; Brehm et al. 2003), geckos in Cape
Verde (Arnold et al. 2008) or birds in America (Patel et al. 2011), have
revealed strong splits between islands with no shared haplotypes for the
same mitochondrial markers.

\textbf{Acknowledgments}

We thank the University of La Rochelle (ULR) for funding LT PHD, and the
ULR and CEBC molecular core facilities for lab support. We also thank
the Parc Naturel Regional de la Martinique and the DEAL Martinique for
help and funding of the research program on shearwaters. Blood samples
of Audubon's shearwaters of St Barth were obtained with the agreement of
Agence Territoriale de l'Environnement de Saint-Barthélémy. We are also
grateful to the Direção Nacional do Ambiente of Cape Verde, the Cabildo
de Lanzarote and Gobierno de Canarias of Spain, and the Direcção
Regional do Ambiente from Azores of Portugal for permission and
logistical support in conducting fieldwork in the respective countries
and to the Spanish Government (CGL2009-11278/BOS, CGL2013-42585-P,
CGL2016-78530-R) and Fondos FEDER for funding. We warmly thank
Pierre-André Crochet, Sarah Samadi and Joan Ferrer-Obiol for great
comments on previous draft that very much improved our manuscript.

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\textbf{Data accessibility statement}

DNA sequences: currently submitted to Genbank

\textbf{Author's contributions}

VB, LT and EP conceived the study. VB, LC, WM, CP, HS, JS, JT, FZ, JGS,
TM and LZ collected samples. CR and LT performed the labwork. LT, EP and
AV analyzed the data. LT, EP, VB and AV wrote the manuscript. All
authors approved the final manuscript.

\textbf{Conflict of Interest}

The authors declare no conflict of interest.

Table 1: Summary statistics of polymorphisms for the nine markers used
in this study.

N is the number of sequences obtained (with numbers in parentheses
referring to the sequences obtained from this study, obtained from
previous studies and downloaded from Genbank); L is the length of the
sequences in bp; S is the number of polymorphic segregating sites; h /
a: h is the number of corresponding haplotypes for mitochondrial
markers, and a is the number of alleles for nuclear markers; hd is the
haplotype diversity and \selectlanguage{greek}π \selectlanguage{english}is the nucleotide diversity. Due to the
presence of ambiguities in the sequence for CR, two haplotype phases
were considered here.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
Marker & N & L & S & h / a & hd & \selectlanguage{greek}π & He & Ho\tabularnewline
\midrule
\endhead
\emph{cox1} & 225 (212, 10, 3) & 577 & 18 & 12 & 0.843 & 0.01301 & - &
-\tabularnewline
\emph{cytb} & 230 (205, 3, 22) & 877 & 50 & 48 & 0.951 & 0.01394 & - &
-\tabularnewline
CR & 181 & 307 & 80 & 98 & 0.993 & 0.07497 & - & -\tabularnewline
\emph{tpm} & 184 & 427 & 2 & 3 & - & 0.00011 & 0.0041 &
0.0008\tabularnewline
\emph{irf2} & 182 & 542 & 6 & 6 & - & 0.00048 & 0.0133 &
0.000\tabularnewline
\emph{csde} & 223 & 542 & 15 & 13 & - & 0.0006 & 0.0007 &
0.0003\tabularnewline
\emph{pax} & 255 (227, 28) & 515 & 9 & 10 & - & 0.00159 & 0.0055 &
0.0008\tabularnewline
\emph{rag1} & 162 & 1323 & 29 & 65 & - & 0.00337 & 0.0109 &
0.0026\tabularnewline
\selectlanguage{greek}\emph{βfib}\selectlanguage{english} & 92 & 1067 & 50 & 53 & - & 0.0083 & 0.0074 &
0.0014\tabularnewline
\bottomrule
\end{longtable}

Table 2: Population differentiation, according to the types of genetic
markers and sex.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
b. & & lherminieri & lherminieri & lherminieri & boydi & boydi & baroli
& baroli & bailloni & bailloni & nicolae\tabularnewline
\midrule
\endhead
& & \textbf{Allencay} & \textbf{Longcay} & \textbf{Martinique} &
\textbf{Raso} & \textbf{Cima} & \textbf{Mclara} & \textbf{Vila} &
\textbf{North Reunion} & \textbf{South Reunion} &
\textbf{Seychelles}\tabularnewline
lherminieri & \textbf{Allencay} & - & 0 & 0.33* & 0.55*** & 0.49*** &
0.5*** & 0.47*** & 0.81*** & 0.83*** & 0.8**\tabularnewline
lherminieri & \textbf{Longcay} & 0 & - & 0 & 0.47*** & 0.4** & 0.42*** &
0.43*** & 0.81*** & 0.83*** & 0.78***\tabularnewline
lherminieri & \textbf{Martinique} & 0.45** & 0.38* & - & 0.2* & 0.35** &
0.42** & 0.41*** & 0.71*** & 0.77*** & 0.7**\tabularnewline
boydi & \textbf{Raso} & 0.6*** & 0.48*** & 0.24* & - & 0 & 0 & 0.29* &
0.81*** & 0.84*** & 0.78**\tabularnewline
boydi & \textbf{Cima} & 0.49*** & 0.34*** & 0.19* & 0 & - & 0 & 0 &
0.77* & 0.82*** & 0.75*\tabularnewline
baroli & \textbf{Mclara} & 0.67*** & 0.56*** & 0.52** & 0.34** & 0 & - &
0 & 0.76*** & 0.81*** & 0.74**\tabularnewline
baroli & \textbf{Vila} & 0.71*** & 0.61*** & 0.69** & 0.44** & 0 & 0 & -
& 0.77*** & 0.79*** & 0.7**\tabularnewline
bailloni & \textbf{North Reunion} & 0.8*** & 0.77*** & 0.86*** & 0.9***
& 0.81*** & 0.92*** & 0.94*** & - & 0 & 0.6*\tabularnewline
bailloni & \textbf{South Reunion} & 0.69*** & 0.64*** & 0.65*** &
0.75*** & 0.69*** & 0.73*** & 0.77*** & 0.3* & - &
0.53***\tabularnewline
nicolae & \textbf{Seychelles} & 0.78*** & 0.75*** & 0.8*** & 0.85*** &
0.73*** & 0.86*** & 0.89*** & 0.67*** & 0.31*** & -\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllllll@{}}
\toprule
\begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
a.\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
lherminieri\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
lherminieri\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
lherminieri\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
lherminieri\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
boydi\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
boydi\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
baroli\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
baroli\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
baroli\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
baroli\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
bailloni\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
bailloni\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
nicolae\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Allencay}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Longcay}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Martinique}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{St} \selectlanguage{ngerman}\textbf{Barthélémy}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Raso}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Cima}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Funchal}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Mclara}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Selvagem}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Vila}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{North} \textbf{Reunion}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{South} \textbf{Reunion}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Seychelles}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
lherminieri\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\textbf{Allencay}\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.18*\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.09\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.47***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.25***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.45*\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.5***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.46***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.47***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.69***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.68***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.41***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
lherminieri\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\textbf{Longcay}\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.08*\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.11\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.12\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.42***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.19***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.45***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.44***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.39***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.41***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.69***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.65***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.37***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
lherminieri\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\textbf{Martinique}\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.22***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.24\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.16*\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.11\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.38*\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.39***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.42***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.43***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.78***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.65***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.61***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
lherminieri\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{St} \selectlanguage{ngerman}\textbf{Barthélémy}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.19*\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.35***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-0.13\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-0.28\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-0.21\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-0.11\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-0.07\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-0.43\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-0.48\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.52***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.71***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.39*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
boydi\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\textbf{Raso}\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.81***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.83***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.82***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.77***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.11\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.02\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.29***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.37*\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.35***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.79***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.77***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.52***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
boydi\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\textbf{Cima}\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.76***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.78***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.74***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.71***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.01\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.22\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.15*\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.15*\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.11*\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.66***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.7***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.42***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
baroli\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\textbf{Funchal}\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.81***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.84***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.85***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.72***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.7***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.56***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.04\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.48\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.3\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.78***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.77***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.51*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
baroli\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\textbf{Mclara}\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.78***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.81***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.82***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.73***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.67***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.61***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.08\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.33***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.06\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.76***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.75***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.48***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
baroli\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\textbf{Selvagem}\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.8***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.83***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.83***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.73***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.64***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.53***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.36\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.10\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.14*\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.68***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.69***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.39*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
baroli\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\textbf{Vila}\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.85***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.86***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.84***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.81***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.69***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.65***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.06\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-0.04\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.03\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.75***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.73***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.47***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
bailloni\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{North} \textbf{Reunion}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.93***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.93***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.89***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.91***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.87***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.84***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.89***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.85***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.88***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.89***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-0.05\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.05\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
bailloni\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{South} \textbf{Reunion}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.93***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.94***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.9***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.92***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.89***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.87***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.91***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.88***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.88***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.91***\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-0.16\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
-0.13\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
nicolae\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
\textbf{Seychelles}\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.93***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.93***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.9***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.91***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.89***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.87***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.91***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.89***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.9***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.92***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.76***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
0.79***\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

a. Pairwise \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{ST} values for mitochondrial markers (below
diagonal) and \emph{F} \textsubscript{ST} for nuclear markers (above
diagonal).

Border indicates the separation between intra and inter lineage
comparisons. Triple band indicates the separation between intra- and
inter-ocean comparisons. *: p\textless{}0.05; ***: p\textless{}0.001

b. Pairwise \emph{F} \textsubscript{ST} for nuclear markers for females
(below diagonal) and males (above diagonal).

Table 3: Sex-specific \emph{F} \textsubscript{IS} and Relatedness
indices

Mean and confidence interval of \emph{F} \textsubscript{IS} and
relatedness are indicated for each population with a sufficient sample
size and for each lineage The observed difference of mean is considered
as significant when not in the confidence interval (indicated in bold
and by an asterisk)\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Lineage\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Population\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Female Sample Size\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Male Sample Size\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\emph{F}\textsubscript{IS} Female\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\emph{F}\textsubscript{IS} Male\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Observed Relatedness Female\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Observed Relatedness Male\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Observed difference of relatedness\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
95\% CI of inferred difference\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{lherminieri}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Allencay\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.79 {[}0.77-0.90{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.35 {[}0.27-0.43{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.45\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.55\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.14~: 0.13\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{lherminieri}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Longcay\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.19 {[}0.13-0.20{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.60 {[}0.55-0.69{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.62\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.71\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.09\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.08~: 0.09\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{lherminieri}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Bahamas\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
22\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.63 {[}0.59-0.67{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.76 {[}0.75-0.79{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.55\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.62\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.07\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.07~: 0.07\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{lherminieri}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Martinique\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
19\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
22\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.71 {[}0.64-0.71{]}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.62 {[}0.59-0.66{]}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.70*}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.63*}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.07}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{-0.04~: 0.04}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{boydi}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Raso\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.53 {[}0.49-0.58\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.51 {[}0.49-0.55{]}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.49*}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.38*}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.11}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{-0.11~: 0.10}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{boydi}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Cima\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.77 {[}0.76-0.78{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.66 {[}0.64-0.73{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.56\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.57\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.01\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.08~: 0.09\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{baroli}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Mclara\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.36 {[}0.29-0.39{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.58 {[}0.55-0.60{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.59\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.55\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.04\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.08~: 0.08\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{baroli}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Vila\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.48 {[}0.44-0.54{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.64 {[}0.63-67{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.47\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.45\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.02\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.09~: 0.09\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{bailloni}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
North Reunion\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.77 {[}0.75-0.82{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.33 {[}0.23-0.43{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.68*}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.78*}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{-0.10}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{-0.06~: 0.07}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{bailloni}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
South Reunion\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
17\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.58 {[}0.58-0.66{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.42 {[}0.34-0.49{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.6*}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.72*}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{-0.12}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{-0.07~: 0.08}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{nicolae}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
All populations\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.61 {[}0.59-0.68{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.42 {[}0.39-0.46{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.6*}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{0.72*}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{-0.12}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{-0.09~: 0.09}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Figure 1: World map distribution of \emph{Puffinus lherminieri} complex
(see Austin et al. 2004 for \emph{assimilis} ).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Numbers represent breeding localities that were sampled for this study
(colour codes identical across figures). The size of filled circles
corresponds to sample size (number of individuals). ``X'' represents the
only breeding colony from North Atlantic that was not sampled here.
Other letters (A to E) for other taxa within the complex.

Figure 2: Gene trees and scenario of breeding site colonization

a. Gene trees obtained by Bayesian inference for all markers, node bars
correspond to the 95\% confidence interval of the estimated divergence
times. The scale corresponds to time before present in Million years
(My). b. Gene trees obtained using *BEAST for all mitochondrial markers
with dichrous haplotypes in yellow. c. Gene trees obtained using *BEAST
for all mitochondrial and nuclear markers with dichrous sequences in
yellow. In b. and c. only individuals sequenced for all mitochondrial
markers and all markers respectively are showed. Only the posterior
values \textgreater{}0.90 are shown. d. Scenario of colonization
inferred based on DIYABC. Branch colours correspond to ancestral
populations, dates to mean divergence times of trees in My inferred by
*BEAST analyses. Each bifurcation corresponds to a
divergence-colonization event. * Indicates the split between Atlantic
and Indian ancestral populations. The analysis excludes the population
of Funchal, due to low sample size (4 individuals).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\emph{}

Figure 3: NeigborNet networks obtained using mitochondrial markers (a.)
and nuclear markers (b.).

The scale bars indicate the sequence divergence (number of substitutions
per site) represented by the length of a branch.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 4: Correlation between gentic and geographic distance\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Relationships between genetic (\emph{F} \textsubscript{ST}/(1-\emph{F}
\textsubscript{ST})) and geographic distances. Genetic distances were
calculated for all concatenated mitochondrial markers (a.) and all
concatenated nuclear markers (b.). Geographic distances were calculated
as the shortest distances between pairs of populations without crossing
land. Black squares: pairs of populations from Atlantic and Indian
oceans. Light squares: pairs of population between \emph{lherminieri}
and\emph{baroli-boydi (crossed)} , \emph{boydi} and \emph{baroli (in
blue)}and \emph{bailloni} and \emph{nicolae (in red)} . Coloured circles
include only pairs of populations belonging to the same lineage.

\selectlanguage{english}
\FloatBarrier
\end{document}

