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Increasing evidence suggests foraging segregation as a key mechanism promoting genetic divergence within seabird species. However, testing for a relationship between population genetic structure and foraging movements among seabird colonies can be challenging. Telemetry studies suggest that Flesh-footed Shearwater Ardenna carneipes that breed at Lord Howe Island or New Zealand, versus southwestern Australia or Saint-Paul Island in the Indian Ocean, migrate to different regions (North Pacific Ocean and northern Indian Ocean, respectively) during the non-breeding season, which may inhibit gene flow among colonies. In this study, we sequenced a 858-base pair mitochondrial region and seven nuclear DNA fragments (352–654 bp) for 148 individuals to test genetic differentiation among colonies of Flesh-footed Shearwaters. Strong genetic divergence was detected between Pacific colonies relative to those further West. Molecular analysis of fisheries’ bycatch individuals sampled in the Sea of Japan indicated that individuals from both western and eastern colonies were migrating through this area, and hence the apparent segregation of the non-breeding distribution based on telemetry is invalid and cannot contribute to the population genetic structure among colonies. The genetic divergence among colonies is better explained by philopatry and evidence of differences in foraging strategies during the breeding season, as supported by the observed genetic divergence between Lord Howe Island and New Zealand colonies. We suggest molecular analysis of fisheries’ bycatch individuals as a rigorous method to identify foraging segregation, and we recommend the eastern and western A. carneipes colonies be regarded as different Management Units.
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Vol.:(0123456789)
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Conserv Genet (2018) 19:27–41
DOI 10.1007/s10592-017-0994-y
RESEARCH ARTICLE
Genetic divergence betweencolonies ofFlesh-footed Shearwater
Ardenna carneipes exhibiting different foraging strategies
AniceeJ.Lombal1 · TheodoreJ.Wenner1· JenniferL.Lavers2· JeremyJ.Austin3·
EricJ.Woehler2· IanHutton4· ChristopherP.Burridge1
Received: 24 January 2017 / Accepted: 4 July 2017 / Published online: 7 July 2017
© Springer Science+Business Media B.V. 2017
indicated that individuals from both western and eastern
colonies were migrating through this area, and hence the
apparent segregation of the non-breeding distribution based
on telemetry is invalid and cannot contribute to the popu-
lation genetic structure among colonies. The genetic diver-
gence among colonies is better explained by philopatry
and evidence of differences in foraging strategies during
the breeding season, as supported by the observed genetic
divergence between Lord Howe Island and New Zea-
land colonies. We suggest molecular analysis of fisheries’
bycatch individuals as a rigorous method to identify forag-
ing segregation, and we recommend the eastern and west-
ern A. carneipes colonies be regarded as different Manage-
ment Units.
Keywords Oceanic seabirds· Ardenna carneipes· Gene
flow· Genetic divergence· Foraging segregation· Genetic
assignment· Conservation management
Abstract Increasing evidence suggests foraging segre-
gation as a key mechanism promoting genetic divergence
within seabird species. However, testing for a relationship
between population genetic structure and foraging move-
ments among seabird colonies can be challenging. Telem-
etry studies suggest that Flesh-footed Shearwater Ardenna
carneipes that breed at Lord Howe Island or New Zealand,
versus southwestern Australia or Saint-Paul Island in the
Indian Ocean, migrate to different regions (North Pacific
Ocean and northern Indian Ocean, respectively) during the
non-breeding season, which may inhibit gene flow among
colonies. In this study, we sequenced a 858-base pair
mitochondrial region and seven nuclear DNA fragments
(352–654 bp) for 148 individuals to test genetic differen-
tiation among colonies of Flesh-footed Shearwaters. Strong
genetic divergence was detected between Pacific colonies
relative to those further West. Molecular analysis of fish-
eries’ bycatch individuals sampled in the Sea of Japan
Electronic supplementary material The online version of this
article (doi:10.1007/s10592-017-0994-y) contains supplementary
material, which is available to authorized users.
* Anicee J. Lombal
anicee.lombal@utas.edu.au
Theodore J. Wenner
theodore.wenner@utas.edu.au
Jennifer L. Lavers
jennifer.lavers@utas.edu.au
Jeremy J. Austin
jeremy.austin@adelaide.edu.au
Eric J. Woehler
eric.woehler@utas.edu.au
Ian Hutton
ian@ianhuttontours.com
Christopher P. Burridge
chris.burridge@utas.edu.au
1 School ofBiological Sciences, University ofTasmania,
Hobart, TAS7001, Australia
2 Institute forMarine andAntarctic Studies, University
ofTasmania, Hobart, TAS7004, Australia
3 Australian Centre forAncient DNA, School ofBiological
Sciences, University ofAdelaide, Adelaide, SA5005,
Australia
4 Lord Howe Island Museum, PO Box157, LordHoweIsland,
NSW2898, Australia
28 Conserv Genet (2018) 19:27–41
1 3
Introduction
Understanding evolutionary processes and population
dynamics within species is crucial to predict their long-
term persistence and resilience to environmental pertur-
bations (Avise and Hamrick 1996). This requires inves-
tigating gene flow among populations to assess local
extinction risk (Chepko-Sade and Halpin 1987; Ibrahim
et al. 1996; Wright 1931). Isolation of populations can
lead to genetic divergence, and often a decrease in genetic
diversity through stochastic events such as genetic drift
and increased inbreeding in small populations (Frankham
2010; Moritz 1999). This may increase extinction risk by
reducing the potential for adaptation to future changes such
as environmental variations and anthropogenic stressors,
although gene flow can also inhibit adaptation by swamp-
ing favoured alleles (Frankham 2005). Consequently, pre-
dicting gene flow between populations based on factors
such as wind-dispersed seeds in plants (Hamrick et al.
1992) or presence of pelagic larvae versus direct devel-
opment in fishes (Dawson etal. 2014; Kyle and Boulding
2000) is highly desirable for identifying conservation pri-
orities and maintaining viability of species (DeSalle and
Amato 2004; Greenwood etal. 1978).
Among seabirds, several non-physical factors are asso-
ciated with restricted movement and spatial structuring
of genetic variation among colonies (Friesen etal. 2007).
Although most seabird species have the ability to travel
long distances (Shaffer et al. 2006), they also usually
exhibit a high level of philopatry (Coulson 2001; Warham
1990), which appears to restrict gene flow among colo-
nies in some species (Friesen et al. 2007; Friesen 2015).
However, a number of seabird species showing philopatry
do not present genetic structure among colonies (Austin
etal. 1994; Avise etal. 1992; Lombal et al. 2016; Pearce
etal. 2004; Roeder etal. 2001), such that philopatry is not
always a predictor of population genetic structure or has not
been accurately quantified in those species. Seabirds from
different colonies also often display discrete foraging distri-
butions during breeding or non-breeding seasons that may
limit gene flow among populations and promote local dif-
ferentiation (Catard etal. 2000; Peck and Congdon 2005).
Some seabirds that migrate to population-specific non-
breeding areas appear to have less opportunity for gene
flow among populations than those that have a single com-
mon non-breeding area (Burg and Croxall 2001; Friesen
et al. 2007; Friesen 2015; Kidd and Friesen 1998). For
example, Burg and Croxall (2001) found that black-browed
albatrosses Thalassarche spp. showing distinct foraging
grounds during the non-breeding season differ genetically
despite a lack of physical barriers to dispersal among colo-
nies. However, segregation during the non-breeding season
per se is unlikely to always explain restrictions in gene flow
among seabird colonies (Rayner etal. 2011a), as 63% of
species whose populations overlap in non-breeding distri-
bution show evidence of restrictions in gene flow among
colonies (Friesen 2015). Population-specific foraging dis-
tribution during the breeding season may also restrict gene
flow among colonies (Friesen et al. 2007; Wiley et al.
2012). Hawaiian Petrels Pterodroma sandwichensis nesting
on Hawaii versus Kauai and foraging in different areas dur-
ing the breeding season exhibit significant spatial genetic
structure (Wiley etal. 2012). However, a greater number of
studies are required to test whether differences in foraging
distributions influence genetic divergence between popula-
tions, and to provide insights into behavioural and ecologi-
cal mechanisms underlying the population genetic diversifi-
cation of highly mobile taxa, such as seabirds.
Understanding the relationship between population
genetic variation in seabirds and the foraging distributions
of individuals from different colonies requires detailed
information on the latter, yet these are often constrained
by limited observations. Detailed observations of foraging
movements are provided by telemetry studies, but these are
typically restricted to a low number of individuals over a
relatively short time interval (e.g., a single season), produc-
ing temporally and spatially limited insights at best (Gen-
ovart et al. 2007). Small rates of gene flow can strongly
influence population genetic structure (Mills and Allendorf
1996; Slatkin 1987), and therefore foraging observations
from a small number of individuals may be uninformative
about rarer individual movements that can significantly
influence genetic variation among colonies. In addition to
telemetry, molecular analysis of fisheries’ bycatch indi-
viduals can test foraging segregation by assigning birds
to breeding colonies (Edwards etal. 2001) assuming that
genetic structure exists among colonies; this approach has
the potential to reject foraging segregation as a contributor
to genetic structure.
The Flesh-footed Shearwater Ardenna carneipes is a
species of oceanic seabird listed as vulnerable under the
New South Wales (NSW) Threatened Species Conservation
Act (1995) http://www.legislation.nsw.gov.au/. The species
is a trans-equatorial migrant that breeds in northern New-
Zealand, Lord Howe Island (Pacific Ocean), on islands
off southwestern Australia and Saint-Paul Island (Indian
Ocean) (Lavers 2014; Marchant and Higgins 1990; Waugh
et al. 2013), and exhibits high fidelity to natal breeding
sites as do most Procellariiformes (Brooke 2004; Warham
1990). Geolocation loggers deployed on 61 birds breeding
in New Zealand (Rayner etal. 2011b; Waugh etal. 2016)
and 57 breeders from Lord Howe Island (Reid etal. 2013b;
Tuck and Wilcox 2010) showed that they transit through
the central Pacific Ocean to the Sea of Japan for the non-
breeding season. Conversely, GPS transmitters deployed
on 13 breeders from southwestern Australia (Powell 2009;
29Conserv Genet (2018) 19:27–41
1 3
Lavers, unpublished data) indicated migration in a north-
western direction across the southern Indian Ocean to the
Arabian Sea. Differences in foraging distribution during the
breeding season have also been reported. Individuals breed-
ing east of Australia are believed to forage in more inshore
waters (<1000 km from land, Reid et al. 2012) and at a
higher trophic level than individuals breeding on islands in
Western Australia (Bond and Lavers 2014; Lindsey 1986;
Taylor and Unit 2000).
Here, we generated a dataset of DNA sequences from
one mitochondrial region and seven nuclear DNA frag-
ments to test the hypothesis that eastern and western A.
carneipes breeding colonies form two independent genetic
clusters, as suggested by observed high philopatry and
evidence of different foraging distributions and strategies
during the breeding and non-breeding season. However, to
more rigorously test the assumption of foraging segrega-
tion during the non-breeding period, we inspected mtDNA
sequences from fisheries’ bycatch individuals obtained in
Japanese waters in the North Pacific Ocean. In addition,
as Flesh-footed Shearwater colonies are threatened by
anthropological-driven changes, such as fisheries bycatch
of individuals around their breeding colonies and dur-
ing their transequatorial migration (Baker and Wise 2005;
Waugh etal. 2016), which has led to a decline of ~40% of
the world’s population (Lavers 2014; Reid et al. 2013a),
we tested for historical population size variation to assess
whether different foraging populations have experienced
and survived similar demographic changes in the past.
Materials andmethods
Sample collection
We collected blood samples from A. carneipes individu-
als (n = 139) from 12 breeding colonies (Fig. 1; Table1).
Colonies were pooled into five geographic regions for
analysis, with maximum inter-colony distance <150 km
within a region and >2000 km between regions: Lord
Howe Island (n = 43), New Zealand (n = 30), South Aus-
tralia (n = 20), Western Australia (n = 45), Saint-Paul Island
Fig. 1 Sampling locations
of Flesh-footed shearwaters
(A. carneipes) and mtDNA
haplotype network based on
the TCS algorithm. a Sam-
pling locations of breeding
individuals: LHI Lord Howe
Island, NZ New Zealand, SA
South Australia, WA Western
Australia, SPI Saint-Paul Island.
NPO fisheries’ bycatch from the
North Pacific Ocean. Pie charts
representing shared versus pri-
vate haplotypes of Cytochrome
b. b Haplotype network of
mtDNA sequences based on
the TCS algorithm. Haplotypes
are represented by circles,
where the size of each circle is
proportional to the frequency
of the corresponding haplotype.
Lines on connecting branches
represent mutations
0 1250 2500 km
N
800E 1000E 1200E 1400E1600E 1800E
50
0
S40
0
S30
0
S20
0
S10
0
S
SP
WA SA
LHI NZ
NPO
LHI
NZ
SP
NPO
SA
WA
10 sample
s
1 sample
(a)
(b)
Hap_1 Hap_2
30 Conserv Genet (2018) 19:27–41
1 3
(n = 1). Feathers sampled from a non-breeding area in the
Sea of Japan, North Pacific Ocean (birds caught as fisheries
bycatch in Japanese waters, n = 9), were provided by The
Burke Museum. All blood samples from Lord Howe Island,
Western Australia, South Australia and Lady Alice Island
(New Zealand) were collected from Flesh-footed Shearwa-
ters under Animal Ethics Permit number AEC 021028/02
issued by the Department of Environment, Climate Change
and Water (DEWNR). The National Institute of Water and
Atmospheric Research (NIWA) provided samples from
the Coromandel Peninsula (New Zealand), and the Paris
Museum provided the sample from Saint-Paul Island.
Blood was preserved in Queen’s lysis buffer (Seutin etal.
1991). Museum Identification numbers are shown in the
Electronic Supplementary Information SI 1.
Mitochondrial andnuclear DNA sequencing
Genomic DNA was extracted from 148 individuals using
a Qiagen DNeasy® Blood and Tissue kit following the
manufacturer’s protocol. Extracted DNA was quanti-
fied using a NanoDrop 8000 spectrophotometer (Thermo
Fisher Scientific, USA). We determined the nucleotide
sequences of a 858 bp fragment of the mitochondrial
Cytochrome b gene for 145 A. carneipes individuals
using primers L14841 and H15547 (Kocher etal. 1989),
and 101–132 individuals for ~500bp fragments of seven
nuclear DNA fragments (4080, 18,503, 20,454, 22,519,
Pema01, Pema07,Pema14) (Backström etal. 2008; Pat-
terson et al. 2011; Silva et al. 2011). Amplification of
nuclear DNA from fisheries’ bycatch samples was unsuc-
cessful. The exact number of individuals sequenced for
each locus from the five regions (Lord Howe Island, New
Zealand, South Australia, Western Australia, Saint-Paul
Island) and the non-breeding area in the North Pacific
Ocean and associated GenBank Accession numbers are
shown in the Electronic Supplementary Information SI
2. Primer sequences, optimal annealing temperatures
and approximate locus length for seven nuclear DNA
Table 1 Sampling sites and characterization of genetic diversity in A. carneipes for Cytochrome b and seven nuclear DNA fragments
Number of birds sampled (n), localities and geographic coordinates from breeding colonies of A. carneipes. Genetic statistics for each breeding
location as mean haplotypic diversity (Hd), haplotype ratios (XH), nucleotide diversity (Pi) and nucleotide ratios (πR)
Bold values represent total number (n) of birds sampled per region
a Reid etal. (2013a)
b Waugh etal. (2013)
c Taylor and Unit (2000)
d Lavers (2014)
e Roux (1985)
f Pi and Hd for Cytochrome b
Locality Pop. size
(Breeding pairs)
nCoordinates Haplotypic diversity Nucleotide diversity
Latitude Longitude HdXHPiπR
Lord Howe Island (LHI) 14,800–18,800a43 0.489 0.441 0.00395 0.740
Ned’s Beach 34 31°51S 159°07E
Clear Place 3 31°52S 159°08E
Middle Beach 6 31°52S 159°07E
New Zealand (NZ) 10,000–15,000b30 0.652 0.444 0.00412 1.201
Lady Alice Island ~1000c15 35°54S 174°44E
Coromandel Peninsula <1000c15 36°80S 175°48E
South Australia (SA) 800–3000d20 0.608 0.486 0.00420 0.871
Lewis Island 211 ± 121d13 34°57S 136°01E
Smith Island 1613 ± 924d7 35°00S 136°01E
Western Australia (WA) 18,300–35,900d45 0.608 0.656 0.00471 1.023
Shelter Island 827 ± 690d13 35°03S 117°41E
Sandy Island 3439 ± 1917d23 34°51S 116°02E
Breaksea Island 1862 ± 12,226d6 35°04S 118°03E
Coffin Island <200d3 35°00S 118°12E
Saint Paul Island (SP) ~100e138°84S 77°83E − − −
North Pacific Ocean (NPO) 90.583f0.250f0.0008f0.264f
Total 148
31Conserv Genet (2018) 19:27–41
1 3
fragments in A. carneipes are shown in the Electronic
Supplementary Information SI 3.
All fragments were PCR amplified with MangoTaq™
DNA polymerase following the manufacturer’s protocol
(Bioline Inc.). PCR reactions were performed in 25 µL
using 50–100 ng DNA, and final concentrations of 0.5 U
DNA polymerase, 0.2mM of each dNTP, 1.5mM MgCl2
and 0.3 µM of each primer. The thermal cycling profiles
included an initial denaturation at 95 °C for 1min followed
by 29 cycles of denaturation at 95 °C for 30s, annealing for
40s, and extension of 72 °C for 90s, with a final extension
of 72 °C for 10min. Negative controls were included with
each set of PCRs.
Nucleotide sequences were determined on both strands
of PCR products using a 3730xl DNA Analyzer (Applied
Biosystem®) at Macrogen Inc., Korea. Sequences were
aligned using the MUSCLE algorithm (Edgar 2004) in
CODONCODE ALIGNER v3.7.1.1 (CodonCode Cor-
poration). For nuclear DNA sequences containing multi-
ple heterozygous positions, we used the Bayesian method
implemented in PHASE v2.2.1 (Stephens et al. 2001) to
reconstruct the haplotype phase of the sequences. We ran
the algorithm three times from different starting points to
verify convergence with 10,000 iterations per locus, and
discarded the first 1000 samples as burn-in and the out-
put probability threshold was set to 80%. The program
SEQPHASE (Flot 2010) was used during this process.
Quantifying andtesting assumptions ofgenetic
variation
Haplotypic diversity h (Nei 1987) and nucleotide diversity
π (Tajima 1983) were calculated for mtDNA and nuclear
DNA sequences with SPADS v 1.0 (Dellicour and Mardu-
lyn 2014). To detect potential hotspots of genetic diversity
(e.g., refuge or secondary contact zones), haplotype ratios
XH, and nucleotide diversity ratios πR (Mardulyn et al.
2009) were calculated for each region. To test whether pat-
terns of genetic variation deviated from neutral expecta-
tions, Tajima’s D (Tajima 1983) and Fu and Li’s D* (Fu
and Li 1993) tests were performed using DNASP v 5.10
(Librado and Rozas 2009) for each region, and for all indi-
viduals grouped as a single population, for each genetic
marker.
Population genetic structure
Estimates of population differentiation (Fst, Gst and Nst)
among four regions (Lord Howe Island, New Zealand,
South Australia and Western Australia) were determined
for Cytochrome b and seven nuclear DNA fragments using
SPADS. Fisheries’ bycatch individuals sampled during the
non-breeding period in the Sea of Japan were only included
in population differentiation analyses for Cytochrome b to
assess their genetic connectivity with individuals sampled
from the four breeding regions. The statistical significance
of indices was assessed by 10,000 random permutations of
individuals among geographical regions. TCS haplotype
networks (Clement etal. 2000) were inferred for mitochon-
drial and nuclear DNA sequences, and the frequencies of
haplotypes depicted using PopART (http://popart.otago.
ac.nz).
To define best clustering (K) of regions a posteriori
based on genetic differentiation (Lord Howe Island, New
Zealand, South Australia and Western Australia), AMOVA
Φ-statistics (ΦSC ΦST ΦCT) (Excoffier et al. 1992) were
calculated on all loci for K = 2 (seven possible group-
ings), K = 3 (six possible groupings) and K = 4 (Table 3)
with 10,000 permutations of individuals among regions
using SPADS. AMOVA Φ-statistics were also calculated
for Cytochrome b only for K = 2–4 following the same
procedure.
Gene flow anddivergence times
As Fst cannot distinguish between a situation of high gene
flow among colonies that have diverged a long time, from
one of a relatively recent shared history but no ongoing
gene flow, we used the isolation with migration model
(Hey 2010) to assess the demographic history of A. car-
neipes colonies. Two methods were used for comparison.
First, we estimated the time of divergence between eastern
(Lord Howe Island and New Zealand) and western (South
Australia, Western Australia and Saint Paul Island) regions
considering the best genetic clustering as K = 2 (Fig. 2a).
We used IMa and its model of isolation with migration
(Hey and Nielsen 2007) to simultaneously estimate migra-
tion (m) and lineage divergence time (t) between these two
groups of colonies. Second, we assessed demographic his-
tory of A. carneipes considering K = 4, with four regions
(Lord Howe Island, New Zealand, South Australia, West-
ern Australia) (Fig.2b). Here, we used IMa2 (Hey 2010),
that allows analysis of more than two regions. We defined
the topology of the population tree implemented for the
four distinct regions, and information on the ordering of the
internal nodes in time, based on F-statistics and AMOVA
Φ-statistics (ΦSC ΦST ΦCT). Alternate topologies were also
tested (Fig. 2b) to investigate potential bias of the results
due to incorrect assumption of the topology and the order-
ing of internal nodes. Only gene flow between sister pop-
ulations was allowed to reduce the number of parameters
and the size of the overall model (Hey 2010).
The isolation with migration model is based on sev-
eral assumptions including neutrality, random mating in
ancestral and descendent populations, and free recombina-
tion between but not within loci (Hey and Nielsen 2004;
32 Conserv Genet (2018) 19:27–41
1 3
Nielsen and Wakeley 2001). Lack of recombination within
nuclear DNA fragments was tested using the four-gamete
test as described by Hudson and Kaplan (1985). Three
loci suspected to have experienced recombination (4080,
18503, 20454) were discarded. Mutation rates were given
as priors to the analysis with µ = 1.89 × 10−8 and 3.6 × 10−9
substitution/site/year for Cytochrome b and nuclear DNA
fragments respectively, as recommended for other sea-
birds (Axelsson et al. 2004; Weir and Schluter 2008).
To assess the estimates of demographic parameters, we
assumed a generation time T = 18.3 years (BirdLife Inter-
national http://datazone.birdlife.org/) We implemented the
Hasegawa-Kishino-Yano (HKY) (Hasegawa et al. 1985)
model for the mitochondrial data, and the infinite sites
mutation model (IS) (Kimura 1969) for the nuclear DNA
fragments. IMa/IMa2 exploratory runs were performed to
assess a range of prior distributions that include most of the
range over which the posterior density is not trivial. Analy-
ses were then run three times with different seed numbers
to test for convergence, with 200,000,000 sampled steps
following a discarded burn-in of 20,000,000 steps, with a
two-step linear heating scheme with five chains. Parameter
trend line plots and values of effective sample sizes (ESS)
were inspected after each run, and results were discarded
based on a selection criterion ESS < 200 to assure accurate
estimates of posterior distributions.
Effective population size change analyses
A Bayesian coalescent MCMC model was used to estimate
historical demographic fluctuations of A. carneipes colo-
nies, grouped in four regions, over time using Cytochrome
b and seven nuclear DNA fragments as implemented
in BEAST2 v.2.4.4. (Bouckaert et al. 2014). For our
demographic model, we applied the Coalescent Extended
Bayesian Skyline Plot (EBSP). This model is based on
the generalized skyline plot, which, assuming a single
panmictic population, estimates fluxes in population size
(N) through time but allows the analysis of multiple loci
(Drummond et al. 2005). As violations of panmixia can
lead to false signals of population decrease under EBSPs
(Heller etal. 2013), we performed analyses of regions sep-
arately. The nucleotide substitution model that best fit the
data was selected using the lowest Bayesian Information
Criteria (BIC) in jMODELTEST v2.1.10 (Guindon and
Gascuel 2003) for each genetic marker as recommended by
Posada and Buckley (2004). Three BEAST runs were con-
ducted for each geographic region under a strict molecu-
lar clock (nTOT runs = 12) with substitution rates as above.
Additional runs were performed after having discarded the
three nuclear loci suspected to have experienced recombi-
nation (4080, 18503, 20454) under the same parameters
(nTOT runs = 12). The scale factor for the population size
was set at 0.5 for Cytochrome b and 2 for nuclear DNA
fragments, reflecting their different inheritance and ploïdy.
MCMC chains were run for 200,000,000 iterations, sam-
pling the posterior distribution every 20,000 iterations with
the first 10% discarded as burn-in. The XML file for each
set of analyses generated with BEAUti v.2.4.4 are available
as Supplementary Material SM. Parameter trend line plots
and values of effective sample sizes (ESS) were inspected
after each run and results were discarded based on
ESS < 200 using TRACER v1.6 (Rambaut et al. 2015) to
assure accurate estimates of posterior distributions. MCMC
analyses for the same dataset (groups of population/loci)
were combined with LogCombiner v.2.4.4. To characterise
the magnitude of Ne change in each lineage, we fit median
Ne and time, obtained from the Bayesian skyline posterior
Fig. 2 Hypotheses of demo-
graphic history of A. carneipes
colonies: LHI Lord Howe
Island, NZ New Zealand, SA
South Australia, WA Western
Australia, SPI Saint-Paul Island
as implemented in IMa and
IMa2. a Hypothesis imple-
mented in IMa. b Hypothesis
implemented in IMa2. θ popula-
tion size, m migration rate and t
divergence time
Present
Past
t2t
2
t
1
t0
SA + WA + SPLHI + NZ LHI NZ SA WA
m1
m
1
m3
m4
N2N3N4
a b
2
AA
01
2 3 4 5
m
2
1
m2
SA WA LHI NZ
TOPO-1:
TOPO-2:
33Conserv Genet (2018) 19:27–41
1 3
distribution, to a linear model using a modified version of
the plotEBSPR script (http://beast2.org/tutorials/) imple-
mented in R v.3.1.2.
Results
We sequenced 858bp of the mtDNA Cytochrome b gene,
and a total of 3328 bp comprising seven nuclear DNA
fragments in up to 148 A. carneipes individuals from five
breeding regions (Lord Howe Island, New Zealand, South
Australia, Western Australia and Saint-Paul Island), and
one non-breeding area (Sea of Japan in the North Pacific
Ocean). No length mutations were observed. A total of
16 (Cytochrome b), 24 (4080), 21 (18503), 40 (20454), 7
(22519), 5 (Pema01), 10 (Pema07) and 14 (Pema14) hap-
lotypes were defined. Variable sites in the mtDNA marker
Cytochrome b, shared (Hap_1 and Hap_2) vs. private
(Hap_A–N) haplotypes, nucleotide and codon positions of
variable sites, and substitution type (all are transitions), are
shown in Supplementary Information SI 4. Global haplo-
typic diversity (Hd) and nucleotide diversity (Pi) for each
region are reported in Table1. Hd and Pi for each genetic
marker and each colony are reported in Supplementary
Information SI 5. No significant difference in global nucle-
otide diversities (π) among regions was detected (One-way
ANOVA; H0 = means of π are equal in all regions; F1,4 =
0.033; p value = 0.992; see π values combined over all loci
in Table1). Tajima’s D showed significant positive values
in one nuclear locus (20454) for all regions and when all
individuals were grouped as a single population (Supple-
mentary Information SI 6). Fu and Li’s D* tests showed
positive values for the same nuclear locus (20454) in New
Zealand and South Australia (Supplementary Information
SI 6).
Population genetic structure
Haplotype networks (mtDNA, Fig.1b; nuclear DNA frag-
ments, Supplementary Information SI 7), mtDNA haplo-
type frequencies (Fig. 1a) and significant F-statistic val-
ues over eight loci (global Fst = 0.202, p < 0.005; global
Gst = 0.118, p < 0.005) for mitochondrial and nuclear
DNA sequences support strong structure of genetic vari-
ation among regions. A significant phylogeographic sig-
nal (global Nst = 0.132, p < 0.005) supports these results.
The Fst pairwise matrix indicated a greater genetic struc-
ture between the eastern (Lord Howe Island and New Zea-
land) and the western (South Australia, Western Australia
and Saint-Paul Island) parts of the breeding distribution,
but still with significant difference between New Zealand
and Lord Howe Island (Table2). The magnitude of popu-
lation genetic differentiation at the mtDNA marker was
in all cases higher than at nuclear DNA. No significant
genetic structure was observed between Western Australia
and South Australia. For Cytochrome b, significant Fst
was observed among fisheries’ bycatch individuals from
the North Pacific Ocean and eastern regions (Lord Howe
Island, New Zealand), but not from western regions (South
and Western Australia). The common haplotype from
western regions was represented in the single Saint-Paul
Island individual, but further samples are required to assess
whether the Saint Paul Island colony is distinct from other
western regions. Two private haplotypes were observed in
the fisheries’ bycatch samples.
AMOVA Φ-statistics on combined genetic markers
showed that greatest spatial structuring of genetic variation
for clustering K = 2 was eastern and western regions (Group
1 = Lord Howe Island, New Zealand; Group 2 = South Aus-
tralia, Western Australia, Saint-Paul Island, Table3) where
17.7% of variance was explained by the region grouping.
For K = 3, the greatest spatial structuring was as above
but Lord Howe Island and New Zealand separated (Group
1 = Lord Howe Island, Group 2 = New Zealand, Group
3 = South Australia, Western Australia, Saint-Paul Island,
Table 3), where 20.2% of variance was explained by the
region grouping. AMOVA Φ-statistics for Cytochrome b
showed the same greatest spatial structures as above for
K = 2 where 71.4% of variance was explained by the region
grouping, and K = 3 where 70% of variance was explained
by the region grouping (Supplementary Information SI 8).
Gene flow anddivergence times
Implementations of the isolation-with-migration models
(IMa and IMa2) using nuclear DNA fragments and mito-
chondrial DNA resulted in unimodal posterior density
curves of migration parameters, which were convergent
across runs. The time of divergence between eastern and
western colonies as implemented in IMa was ~28,000 years
ago (11,700–100,000, 90% HPD), and very low gene flow
Table 2 Pairwise differentiation matrix among A. carneipes colonies
including fisheries’ bycatch individuals from the North Pacific Ocean
Below diagonal: pairwise Fst matrix combined for Cytochrome b and
seven nuclear DNA fragments. Above diagonal: pairwise Fst matrix
for Cytochrome b
*p values <0.00833 after sequential Bonferonni correction
LHI NZ SA WA NPO
Lord Howe Island (LHI) 0.457* 0.964* 0.768* 0.904*
New Zealand (NZ) 0.139* 0.824* 0.609* 0.667*
South Australia (SA) 0.333* 0.253* 0.080 0.119
Western Australia (WA) 0.238* 0.190* 0.019 −0.085
North Pacific Ocean
(NPO)
–––––
34 Conserv Genet (2018) 19:27–41
1 3
was inferred (~0) (Fig. 3; Supplementary Information SI
9). Time of divergence between eastern and western colo-
nies as implemented in IMa2 showed similar results to
the one obtained with IMa (~28,000 years; 9800–76,000,
90% HPD). The time of divergence between Lord Howe
Island and New Zealand, and between Western and South
Australia under Topology 1 and Topology 2 (Fig.2) were
convergent, ~3000 years (2000–7000, 90% HPD) and
~2100 years (800–6000, 90% HPD) respectively (Fig. 4;
Supplementary Information SI 10). Gene flow was higher
between western than eastern colonies, but was still very
low (Fig.4; Supplementary Information SI 10).
Effective population size change analyses
All models tested with the Extended Bayesian Skyline
Plot (EBSP) showed similar results, although models
integrating loci suspected to be undergoing recombina-
tion (4080, 18503, 20454) showed broader 95% HPD for
Lord Howe Island (Fig.5, Supplementary Information SI
11). Our demographic reconstruction indicates two dis-
tinct phases for the four regions, including one phase of
stability followed by one phase of population expansion
with the last 5kyr (Fig.5).
Table 3 AMOVA Φ-statistics (ΦSC ΦST ΦCT) (Excoffier et al.
1992) for A. carneipes breeding regions for Cytochrome b and seven
nuclear DNA fragments
A total of seven (K = 2), six (K = 3) and one (K = 4) groupings of
breeding regions were tested. p values for ΦSC are based on permu-
tations of sampled sequences across regions within the same group,
p values for ΦST are calculated based on permutations of sampled
sequences among regions without regard to their original group, p
values for ΦCT are based on permutations of whole regions among
groups
Bold values represent greatest spatial clustering of genetic variation
among regions
*p < 0.05
Groups ΦSC ΦST ΦCT
K = 2
[LHI, NZ][SA, WA] 0.083* 0.246* 0.177*
[LHI, SA][NZ, WA] 0.251* 0.177* −0.099
[LHI, WA][SA, NZ] 0.244* 0.173* −0.094
[LHI] [NZ, SA, WA] 0.169* 0.221* 0.063*
[NZ] [LHI, SA, WA] 0.218* 0.182* −0.046
[SA] [LHI, NZ, WA] 0.203* 0.205* 0.003
[WA] [LHI, NZ, SA] 0.210* 0.195* −0.019
K = 3
[LHI][NZ][SA, WA] 0.024* 0.221* 0.202*
[LHI][SA][NZ, WA] 0.204* 0.200* −0.005
[LHI][WA][NZ, SA] 0.249* 0.197* −0.069
[NZ][SA][LHI, WA] 0.240* 0.183* −0.075
[NZ][WA][LHI, SA] 0.310* 0.187* −0.178
[SA][WA][LHI, NZ] 0.126* 0.219* 0.106
K = 4
[LHI][NZ][SA][WA] – 0.202* –
0.00.5 1.01.5
Marginal posterior probability
2,000 8,000
4,000
Ne
a
0
0.00 0.05 0.10 0.15
m
Marginal posterior probability
8.10-4
4.10-4 12.10-4
0
02468
Marginal posterior probability
40 80 120 160
t(ky)
East
West
East > West
West > East
T[West][East]
b
c
Fig. 3 Marginal posterior distribution of the parameters for the Isola-
tion with Migration model estimated for eastern and western A. car-
neipes breeding regions (K = 2). a Population sizes (Ne). b Migration
(m). c Time of divergence (t2, years) between eastern and western
colonies
35Conserv Genet (2018) 19:27–41
1 3
Fig. 4 Marginal posterior
distribution of the parameters
for the Isolation with Migration
model estimated for A. car-
neipes breeding regions (K = 4).
a Time of divergence between
A. carneipes colonies (t, years).
LHI Lord Howe Island, NZ New
Zealand, SA South Australia,
WA Western Australia. b Time
of divergence between eastern
and western colonies (t2, years).
c Population sizes (Ne). d
Migration (m)
0102030405060
Marginal posterior probability
0123456
Marginal posterior probability
0
0.00.5 1.0 1.5
Marginal posterior probability
0
0.06 0.08 0.10 0.12
m
Marginal posterior probability
24
06810
t(ky)
040 80 120 160
t2(ky)
N
e
2.10-4 4.10-4 6.10
-4
SA
NZ
WA
LHI LHI>NZ
NZ>LHI
SA>WA
WA>SA
ab
cd
T[LHI][NZ]
T[SA][WA]
T[SA+WA][LHI+NZ]
5,000
10,000
Fig. 5 Extended Bayesian
Skyline Plot (EBSP) show-
ing demographic reconstruc-
tion of A. carneipes effective
population size (Ne) through
time for Cytochrome b and
seven nuclear DNA fragments.
Dotted curves and solid curves
represent the median Bayesian
Skyline reconstruction and 95%
HPD intervals, respectively
Present
0.00 8,000 16,000
Time (year)
Population (N
e
)
Present
0.00 3,000 6,000
Time (year)
Population (Ne)
Present
0.00 4,000 8,000 12,00
0
Time (year)
Population (N
e
)
Present
0.00 4,000 8,00
0
Time (year)
Population (Ne)
Western Australia South Australia
Lord Howe Island
New Zealand
40,000 80,000 4,000 8,000
4,000 8,000 1,000
2,000
3,00
0
36 Conserv Genet (2018) 19:27–41
1 3
Discussion
Genetic differentiation amongA. carneipes colonies
Haplotype networks, AMOVA F-statistics, and Isola-
tion with Migration models for mitochondrial and nuclear
DNA sequences, indicated low gene flow and long diver-
gence between A. carneipes colonies breeding east of Aus-
tralia (Lord Howe Island and New Zealand) and western
breeding colonies (Western Australia, South Australia,
and Saint-Paul Island). The divergence between these
regions (~28,000 years) roughly corresponds to the begin-
ning of the Last Glacial Maximum (LGM). Previous stud-
ies showed that in seabirds, availability of new breeding
habitat due to sea level change and the latitudinal migra-
tion of oceanic fronts influenced their global distribution
and phylogeographic structure (Techow etal. 2010). Since
the Flesh-footed Shearwater populations diverged, gene
flow between eastern and western colonies has possibly
been restricted by unsuitable breeding areas throughout
the southeastern coast of Australia (Byrne 2008; Peck and
Congdon 2004), as well as sea level changes leading to the
relocation of breeding colonies during periods of warming
and exposure of Bass Strait (Dartnall 1974; Lambeck etal.
2002). Northward range shifts may also have increased the
isolation of eastern and western populations, as has been
hypothesized for other temperate marine Australian taxa
(Burridge 2000; Fraser et al. 2009). In addition, the star-
like phylogeny of both the western and eastern regions may
reflect spatially distinct refugia, which is consistent with
areas of climatic suitability from species distribution mod-
eling with projected LGM climatic conditions (Buckley
etal. 2010; Nistelberger etal. 2014), followed by a range
expansion. However, old divergences and an apparent lack
of contemporary gene flow between eastern and western
regions provide evidence of signatures of both historical
and contemporary processes affecting the genetic structure
of A. carneipes colonies.
Overlap innon-breeding distributions betweencolonies
Despite suggestions from telemetry of distinct routes of
migration and distribution of individuals from western and
eastern colonies during the non-breeding season, individu-
als sampled during the non-breeding period in the Sea of
Japan were assigned to eastern and western colonies. A
majority of fisheries’ bycatch specimens (66%) possessed
Haplotype_1, which has only otherwise been observed
from Western and South Australia, where it is common
(75% of individuals). As this haplotype was not observed
among 73 individuals sampled from Lord Howe Island
and New Zealand, if it exists there, its frequency is less
than 1.5%. Therefore, it is unlikely that the presence of
Haplotype_1 in a high proportion of bycatch individuals
from the Sea of Japan can be explained in the absence of
migration by Western or South Australian individuals. The
only possible alternate explanation is that western birds
founded a new and presently unsampled and unknown
colony in the eastern part of the species range, and these
birds have adopted the migration route of Lord Howe and
New Zealand individuals, and somehow constituted a large
proportion of our bycatch sample, despite their source col-
ony being undocumented. However, each of these required
events (establishment of new colony in the east by western
individuals, presently unknown colony, adoption of new
migration route, and majority composition of the bycatch
samples despite their source colony being unknown to sci-
ence) seems unlikely, and in combination, discount the
possibility that Western or South Australian birds are not
migrating to the Sea of Japan in the North Pacific Ocean.
Furthermore, the stable isotope and trace element analysis
conducted by Lavers etal. (2013) also suggested that some
Sea of Japan bycatch individuals were derived from West-
ern and South Australian breeding colonies. Based on our
observations, previous studies may have falsely invoked
distinct non-breeding distributions for population genetic
structure in seabirds in instances where there are limited
data supporting distinction of non-breeding distributions.
As a result, genetic structure among Flesh-footed Shearwa-
ter colonies can hardly be explained by distinct non-breed-
ing distribution.
Foraging distinction betweeneastern andwestern
colonies duringthebreeding season
Significant genetic differentiation inferred between Lord
Howe Island and New Zealand A. carneipes breeding colo-
nies, as previously observed in a study using microsatel-
lite markers (Hardesty et al. 2013), supports differences
in foraging strategy during the breeding season as a fac-
tor influencing dispersal between A. carneipes colonies,
rather than geographic distances or differences in migration
routes. The Flesh-footed Shearwater is a central place for-
ager restricted within a certain range of their breeding site
(Ashmole 1971), and has been shown to return to similar
foraging areas during the breeding season from 1 year to
the next (Kinsky 1957; Reid 2011). Individuals breeding
on Lord Howe Island forage off the east coast of Australia
not further than 1000 km from their breeding sites (Reid
etal. 2012; Thalmann et al. 2009), as expected in migra-
tory central-place foragers (Orians and Pearson 1979). This
preference for foraging areas likely reflects western bound-
ary currents moving south along the east coast of Australia
that drive strong oceanographic features such as up-well-
ings in the Tasman Sea, increasing prey availability in this
area (Ridgway and Dunn 2003). Conversely, A. carneipes
37Conserv Genet (2018) 19:27–41
1 3
individuals breeding on New Zealand islands mostly forage
over continental shelves north of the sub-tropical conver-
gence (Rayner etal. 2011b; Waugh etal. 2016).
Isotopic ratio analysis and shipboard observations sug-
gest that individuals from eastern and western regions have
distinct foraging strategies. Individuals breeding east of
Australia may forage in more inshore waters (<1000km,
Reid et al. 2012) and at a higher trophic level than indi-
viduals breeding at western colonies, believed to forage in
offshore waters (Bond and Lavers 2014; Lombal, unpub-
lished data). This may be explained by El Niño–Southern
Oscillations (ENSO) events affecting the Cape Leeuwin
Current near the western coast of Australia and lower asso-
ciated prey availability, and the increase of industrial fish-
ing in this region (Bond and Lavers 2014; Cheung etal.
2012; Lindsey 1986; Taylor and Unit 2000). These obser-
vations are compatible with distinct foraging distributions
during the breeding season affecting gene flow among
Flesh-footed Shearwater colonies, which is consistent
with Friesen (2015) showing that 91% of seabird species
that either feed inshore or have population-specific forag-
ing areas show some evidence of restriction in gene flow
among colonies.
Intraspecific morphological variation andtaxonomic
status
Our observations of spatial genetic variation are consist-
ent with previous morphological differences observed
between Flesh-footed Shearwaters. Hindwood (1945) rec-
ognized two subspecies: Puffinus carneipes carneipes at
the Recherche Archipelago and other islands of south-west
Western Australia, and Puffinus carneipes hullianus for
Lord Howe Island and New Zealand, differing from the
nominate subspecies by a more robust bill and longer wing.
While Flesh-footed Shearwaters from eastern and western
colonies do not show reciprocal monophyly for mtDNA
alleles, this could be explained by the rapid evolution of
phenotypic variation compared to sorting of MtDNA vari-
ation in abundant taxa, which can take tens of thousands of
years to evolve (Avise 2000; Mayr 1970). In addition, as
both petrels and albatrosses show unusually low levels of
genetic divergence (Nunn and Stanley 1998), the power of
genetic analysis to resolve taxonomic uncertainties is usu-
ally reduced in Procellariiformes (Abbott and Double 2003;
Burg and Croxall 2001).
It is often difficult to determine whether populations
have diverged to the extent that they should be consid-
ered as distinct species (Harrison 1998; Sites and Mar-
shall 2004). Speciation is thought to be a gradual process
in animals, with complete reproductive isolation develop-
ing at the final stage (Mayr 1963). As a result, the Biologi-
cal Species Concept (BSP; Mayr 1942) defines species as
reproductively isolated groups of populations, which can
only be directly observed if populations coexist in space
and time, and therefore does not apply to allopatric popula-
tions (Helbig etal. 2002; Mayr and Short 1970; McKitrick
and Zink 1988). Conversely, the Phylogenetic Species Con-
cept (PSC; Cracraft 1983) argues that reproductive isola-
tion should not be a part of species concepts, and instead
requires that (a) species be monophyletic groups, and (b)
species be distinguishable from other such groups in one
or more characters (i.e., diagnosability, Helbig etal. 2002).
The recognition of Evolutionary Significant Units (ESU,
Avise 1989) also requests reciprocal monophyly for mtDNA
alleles but only significant divergence of allele frequencies
at nuclear loci, as this concept considers phylogenetically
unsorted alleles at nuclear loci. These requirements are
comparable to the Agreement on the Conservation of Alba-
trosses and Petrels (ACAP) guidelines adapted from Helbig
etal. (2002), which has adapted the concept of monophyly
by the condition that taxa are ‘likely to retain their genetic
and phenotypic integrity in the future’ (ACAP Document
11 of AC2).
Moritz (1994) stressed that populations that do not show
reciprocal monophyly for mtDNA alleles, yet have diverged
in allele frequencies, are significant for conservation in that
they represent populations connected by such low levels of
gene flow that they are functionally independent. Therefore,
Management Units (MUs) are recognized as populations
with significant divergence of allele frequencies at nuclear
or mitochondrial loci, regardless of the phylogenetic dis-
tinctiveness of the alleles; MUs address current popula-
tion structure, allele frequencies and short-term manage-
ment issues (Moritz 1994). Eastern and western colonies of
Flesh-footed Shearwaters may not represent cryptic species
under the PCS given their lack of phylogeographic struc-
ture but evidence of morphometric differences and strong
divergence in allele frequencies among colonies give strong
support that they are functioning as separate entities and
that they should be considered as independent MUs. The
Flesh-footed Shearwater likely represents a case of incipi-
ent speciation, and for which taxonomic decision remains
difficult.
Detection ofdemographic changes
Overall, the similarity of coalescent histories among colo-
nies indicates that, although genetically independent, they
have homologous demographic histories, including one
phase of stability followed by one recent period of popu-
lation expansion, occurring in the last 5kyr. This may be
explained by population expansion associated with the
LGM, as observed in many high latitude seabird popu-
lations (Congdon et al. 2000; Hewitt 2000), although the
timing of expansion is much more recent in this case.
38 Conserv Genet (2018) 19:27–41
1 3
Furthermore, while coalescent theory is a powerful tool
to extract historical demographic information from DNA
sequences (Hudson 1990), Grant (2015) observed that
small and very recent population expansions often appear
in Bayesian Skyline Plots of simulated populations that did
not experience a sudden recent change in size. In fact, this
pattern frequently reflects random sampling of the MCMC
haplotype trees (Grant 2015). The flat portion of the BSP,
usually interpreted as population stability, is also often
misleading as population contractions can promote extinc-
tions of haplotype lineages leading to the loss of informa-
tion about earlier population history (Grant 2015). In addi-
tion, as slightly deleterious mutations are slowly eliminated
by selection, preventing low-frequency mutations from
moving to higher frequencies (Charlesworth etal. 1993),
haplotype frequency distributions shaped by selection are
difficult to distinguish from distributions produced by a
population expansion. For these reasons, and because of
the lack of additional significant evidence of demographic
changes in Flesh-footed Shearwater colonies, our BSPs
results need to be interpreted cautiously, and may not repre-
sent evidence for novelty of the presently observed popula-
tion declines.
Consequences forconservation status ofFlesh-footed
Shearwaters
In this study, we show a lack of gene flow between A.
carneipes colonies from Lord Howe Island, New Zea-
land and localities to the west, indicating that popula-
tions have clearly experienced independent evolution for
a long time, which may greatly affect long-term viability
and persistence of the species within these regions owing
to local adaptation and demography independence. The
Flesh-footed Shearwater is now listed as Near Threatened
in Birdlife International (2017) IUCN Red list for birds
(http://www.birdlife.org), and therefore there is an urgent
need to develop a suite of mitigation measures that would
reduce the level of bycatch currently being experienced in
each of these regions.
Acknowledgements South Australia Nature Foundation, Trading
Consultants (V. Wellington), Pennicott Wilderness Journeys and the
Winifred Violet Scott Charitable Trust provided funding for the field
and laboratory components of this research. Special thanks go to C.
& G. Biddulph, P. Collins, A. Fidler, S. Goldsworthy, M. Stadler,
the South Australian Department of Environment, Water & Natural
Resources (DEWNR), and Western Australian Department of Parks
and Wildlife (DPaW) for generously providing data and logistical
support. This research was undertaken with animal ethic permissions
from DPaW (SF009585), the University of Tasmania Animal Ethics
committee (A13598 and A13836), DEWNR Resources permits (AEC
021028/02), and Lord Howe Island Board permits (LHIB 07/12 &
LHIB 02/14). We thank the anonymous reviewers for their careful
reading of our paper.
Author contributions AL, JL and IH performed the sample collec-
tion. AL and TW collected the molecular data. AL performed the sta-
tistical and Bayesian analyses. CB and JL designed the study. AL, CB,
JL, IH, JA and EW contributed to the manuscript.
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... Given all the above, we will investigate levels of genetic diversity and population differentiation using genome-wide single nucleotide polymorphisms (SNPs), across the same breeding colonies surveyed here. Differences in foraging area during the breeding season was considered as the main factor influencing the divergence between those two populations rather than the geographic distance between them (Lombal et al., 2018). ...
... The scale on the right side of the figure indicates the relationship between the size of the circles and the frequency of the haplotypes. Lines on connecting branches represent mutations and black dots represent inferred intermediate steps between Lord Howe Island and New Zealand(Lombal et al., 2018). ...
Article
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• Many seabird breeding colonies have recovered from heavy anthropogenic disturbance after conservation actions. The widely distributed red‐tailed tropicbird, Phaethon rubricauda, was used as a model species to assess potential anthropogenic impacts on the genetic diversity of breeding colonies in the Pacific Ocean. • Cytochrome c oxidase subunit I and control region sequences analyses were conducted across the range of the species in the Pacific Ocean. The study sites were at islands without human‐related disturbance (non‐impacted islands) and with human‐related disturbance (impacted islands). We hypothesized that (i) breeding colonies of the red‐tailed tropicbird on impacted islands have lower genetic diversity compared with colonies on non‐impacted islands, and (ii) breeding colonies of the red‐tailed tropicbird show significant fine and broad‐scale genetic structure across the Pacific Ocean. Bayesian skyline analyses were conducted to infer past changes in population sizes. • Genetic diversity was similar between impacted and non‐impacted islands. There was significant broad‐scale genetic structure among colonies separated by over 6,000 km, but a lack of significant fine‐scale genetic structure within Australasia and Hawai'i, although a significant level of differentiation was found within Chile with ΦST analyses. Skyline analyses showed that effective population sizes remained relatively constant through time, but experienced either a slight decrease or the end of an expansion event through the last 1,000 years. These changes may be related to the arrival of humans on Pacific islands. • Impacted islands may have received immigrants from other relatively close islands, buffering the loss of genetic diversity. However, it is also possible that colonies have retained ancestral variation or that a large effective population size coupled with a long generation time (13 years) has prevented the loss of genetic diversity in human‐impacted islands. Future research using higher‐resolution markers is needed to resolve the population genetic structure of the red‐tailed tropicbird in an ecological time‐scale.
... When the Fst could not be calculated based on mitochondrial DNA (mtDNA) sequences available in GenBank, it was reported as obtained in genetic studies (see Table 1). Lombal et al. (2018); 2, Genovart et al. (2007); 3, Austin et al. (1994); 4, Gómez-Díaz et al. (2009); 5, Burg & Croxall (2004); 6, Burg et al. (2003); 7, Quillfeldt et al. (2017); 8, Cagnon et al. (2004); 9, Techow et al. (2010); 10, Techow et al. (2010); 11, ; 12, Bicknell et al. (2012); 13, Quillfeldt et al. (2017); 14, Quillfeldt et al. (2017); 15, Ovenden et al. (1991); 16 Silva et al. (2015); 17, Young (2010); 18, Walsh & Edwards (2005); 19, Techow et al. (2009); 20, Brown et al. (2010); 21, Rayner et al. (2011); 22, Gangloff et al. (2013); 23, Welch et al. (2011); 24, Wiley et al. (2012); 25, Lombal et al. (2017); 26, Abbott & Double (2003b); 27, Burg & Croxall (2001); 28, Burg & Croxall (2001); 29, Abbott & Double (2003b); 30, Steeves et al. (2003); 31, Levin & Parker (2012); 32, Morris-Pocock et al. (2010); 33, Taylor (2011a); 34, Morris-Pocock et al. (2010); 35, Taylor et al. (2011b); 36, Barlow et al. (2011); 37, Calderón et al. (2014); 38, Mercer et al. (2013); 39, Marion & Le Gentil (2006); 40, Calderón et al. (2014); 41, Younger et al. (2015); 42, Clucas et al. (2016); 43, Boessenkool et al. (2009b); 44, Banks et al. (2006); 45, Grosser et al. (2015); 46, Ritchie et al. (2004); 47, Clucas et al. (2014); 48, Clucas et al. (2014); 49, Bouzat et al. (2009); 50, Sonsthagen et al. (2012); 51, Liebers et al. (2001); 52, Sonsthagen et al. (2012); 53, Liebers & Helbig (2002); 54, Sonsthagen et al. (2012); 55, Sonsthagen et al. (2012); 56, Pons et al. (2013); 57, Patirana et al. (2002); 58, Yeung et al. (2009); 59, Faria et al. (2010); 60, Miller et al. (2013); 61, Draheim et al. (2010); 62, Avise et al. (2000); 63, Pshenichnikova et al. (2015); 64, Pshenichnikova et al. (2017); 65, Moum & Arnason (2001); 66, Wojczulanis-Jakubas et al. (2015); 67, Birt et al. (2011b); 68, Friesen et al (1996b), 69, Wallace et al. (2014); 70, Pearce et al. (2002) 71, Birt et al. (2011a); 72, Morris-Pocock et al. (2008); 73, Tigano et al. (2015). Sample sizes implemented in the generalized linear models (GLMs) were adjusted to the number of sequences available in GenBank as used in the calculation of F-statistics where this differed from the number of sequences reported in the publication. ...
... † † Variation in morphological traits between genetically undifferentiated Indo-Pacific colonies.1,Hindwood (1945);Lombal et al. (2018); 2,Genovart et al. (2007Genovart et al. ( , 2012;Guilford et al. (2012); 3,Austin et al. (1994);Weimerskirch & Cherel (1998); 4, González-Solís et al. (2007); Gómez-Díaz et al. (2009); Dias et al. (2011); 5, Burg & Croxall (2004); 6, Weimerskirch et al. (2001); Mallory & Forbes (2007); Hatch et al. (2010); 7, Navarro et al. (2013); Quillfeldt et al. (2017); 8, Wojczulanis-Jakubas & Jensen (2015); Medeiros et al. (2012); 9, Blanco & Quintana(2014); 10,Warham (1990); 11,Monteiro & Furness (1998);; see additional references on the variation in phenology among colonies inFriesen et al. (2007b); 12,Pollet et al. (2014); 13 & 14,Cherel et al. (2002);Quillfeldt et al. (2017); 15,Del Hoyo et al. (1992); 16,Silva et al. (2015); 17 & 18,Fischer et al. (2009); 19,Weimerskirch et al.(1999);Mackley et al. (2011); 20,Brooke & Rowe (1996);Krüger et al. (2016); 21,Rayner et al. (2008Rayner et al. ( , 2010b; 22,Gangloff et al. (2013);Ramírez et al. (2013);Ramos et al. (2016Ramos et al. ( , 2017; 23, Friesen et al. (2006); Welch et al. (2011); 24, Wiley et al. (2012); Adams & Flora (2010); 25, Bester (2003); 26, Abbott & Double (2003a,b); 27 & 28, Prince et al. (1994); Burg & Croxall (2001); Wakefield et al. (2011); 29,Petersen et al. (2008); 30,Nelson (1978); O'Brien & Davies (1990);Pitman & Jehl (1998); 31,Nelson (1978); 32,Nelson (1978); Morris-Pocock et al. (2010); 33, Nelson (1978); Taylor et al. (2011a); 34, Steeves et al. (2003); Morris-Pocock et al. (2010); Burger & Shaffer (2008); 35, Taylor et al. (2011b); 36,Barlow et al. (2011);Grist et al. (2014); 37,Rasmussen (1994);Calderón et al. (2014); 38,Palmer, 1962;Mercer et al., 2013;Scherr et al. 2010 39 Grémillet et al. 2000Marion & Le Gentil, 2006;Gienapp & Bregnballe, 2012 40 Siegel-Causey, 1997 41 & 42 Scheffer et al. 2012Baylis et al., 2015 43 Boessenkool et al. (2009a; 44,Hull (1999);Pütz et al. (2002Pütz et al. ( , 2003;Jouventin et al. (2006); 45,Banks et al. (2002);Overeem (2005); 46,Whitehead et al. (1990);Davis et al. (1996Davis et al. ( , 2001; Clarke et al. (2003); Dunn et al. (2011); Lyver et al. (2011); 47, Trivelpiece et al. (2007); 48, de Dinechin et al. (2012); Black (2016); Vianna et al. (2017); 49, Putz et al. (2000); 50, Liebers et al. (2004); Huettmann & Diamond ...
... Comer, pers comm). Thus, while leaving wildfires to burn on islands may have little detrimental effect on island plant communities in the short-term (Pearson et al., 2004), island preservation must consider rare and/or threatened species or those with lifehistory strategies that mean recovery from perturbations is slow ( (Lombal et al., 2018;Sarker et al., 2021). Given the distance between the Short-tailed Shearwater breeding sites in the Recherche, and the next nearest colony (Penguin Island, South Australia, > 1500 km to the east; Marchant & Higgins 1990), exchange between populations is unlikely, and the genetic status of the Western Australian population warrants investigation. ...
Article
Traditional burning regimes have long been employed to enhance biodiversity and mitigate high-intensity wildfires. The link between changes in the distribution, success, and timing of breeding in seabirds and climatic and oceanographic variation in the marine environment has been established, with migratory seabirds less able to respond to climate variability than resident species. While climate-driven changes can also occur on seabird breeding islands, few data are available regarding potential impacts. Here we investigate the frequency and severity of bushfires on seabird breeding islands in Western Australia, regarding the 2020 fire on Figure of Eight Island in the Recherche Archipelago. A lack of quantitative, historical surveys limited our ability to quantify the number of shearwaters lost in this event. However, a review of available data suggests thousands of birds die due to burning every one or two years across the Archipelago. On Figure of Eight, shearwater burrow occupancy and density were low 12 months post-burn (0.25 and 0.02 ± 0.03, respectively), with minimal evidence of recovery (very few burrows detected) 23 months post-burn. We discuss opportunities to develop an adaptive, community-based program for reinstating collaborative, cultural methods of fire management and monitoring regimes on seabird breeding islands in Australia.
... Fifth, abiotic and biotic factors are known to promote speciation in the shearwaters and related Procellariiformes; for instance, palaeoceanographic changes such as the Pleistocene climatic oscillations can act as historical drivers of speciation (Gómez-Díaz et al., 2006;Silva et al., 2015) and intrinsic biotic factors such as different foraging strategies and allochrony can also promote speciation (Friesen, Smith, et al., 2007;Lombal et al., 2018;Rayner et al., 2011). Sixth, species limits are controversial, mostly due to high morphological stasis (Austin, 1996;Austin et al., 2004); indeed, only a few phenotypic traits, such as vocalisation characteristics, slight plumage colour differences and in particular, body size, may differ between closely related species. ...
Article
Full-text available
Aim Palaeoceanographic changes can act as drivers of diversification and speciation, even in highly mobile marine organisms. Shearwaters are a group of globally distributed and highly mobile pelagic seabirds. Despite a recent well-resolved phylogeny, shearwaters have controversial species limits, and show periods of both slow and rapid diversification. Here, we explore the role of palaeoceanographic changes on shearwaters' diversification and speciation. We investigate shearwater biogeography and the evolution of a key phenotypic trait, body size, and we assess the validity of their current taxonomy. Location Worldwide. Taxa Shearwaters (Order Procellariiformes, Family Procellariidae, Genera Ardenna, Calonectris and Puffinus). Methods We generated genomic (ddRAD) data to infer a time-calibrated species tree for the shearwaters. We estimated ancestral ranges and evaluated the roles of founder events, vicariance and surface ocean currents in driving diversification. We performed phylogenetic generalised least squares to identify potential predictors of variability in body size along the phylogeny. To assess the validity of the current taxonomy, we analysed genomic patterns of recent shared ancestry and differentiation among shearwater taxa. Results We identified a period of high dispersal and rapid speciation during the Late Pliocene–early Pleistocene. Species dispersal appears to be favoured by surface ocean currents, and founder events are supported as the main mode of speciation in these highly mobile pelagic seabirds. Body mass shows significant associations with life strategies and local conditions. The current taxonomy shows some incongruences with the patterns of genomic divergence. Main Conclusions A reduction of neritic areas during the Pliocene seems to have driven global extinctions of shearwater species, followed by a subsequent burst of speciation and dispersal probably promoted by Plio-Pleistocene climatic shifts. Our findings extend our understanding on the drivers of speciation and dispersal of highly mobile pelagic seabirds and shed new light on the important role of palaeoceanographic events.
... The species is also listed nationally vulnerable in New Zealand (Robertson et al. 2013) and has been recommended for listing under the Agreement on the Conservation of Albatrosses and Petrels (Copper and Baker 2008;ACAP 2019). However, molecular based studies on the A. carneipes are also very limited, and only partial mitochondrial sequences of this species are available in the NCBI database (Nunn and Stanley 1998;Penhallurick and Wink 2004;Lombal et al. 2018). This work intended to (i) generate and assemble the first mitogenome data of A. carneipes from Australia using a next-generation sequencing platform, and (ii) reveal the phylogenetic relationships of A. carneipes utilizing selected mitogenome sequences available in GenBank. ...
Article
Full-text available
Resolution of the phylogenetic relationship of the vulnerable flesh-footed shearwater (Ardenna carneipes) seabird using a complete mitochondrial genome ABSTRACT Flesh-footed shearwater (Ardenna carneipes) is recognized as vulnerable seabird species in Western Australia and New South Wales, Australia, and its genetic variability and a well-resolved phylogeny is imperative for the species' conservation. Here, we report the first sequenced mitogenome of the Australian A. carneipes. The mitogenome of A. carneipes was 16,370 bp in total length and encompassed 13 protein-coding genes, two ribosomal RNAs, 22 transfer RNAs, and one non-coding region (D-loop). All of the genes were encoded on the H-strand with the exception of ND6 and eight tRNAs, which is a conserved pattern of the mitogenome for other vertebrates. The mitogenome of A. carneipes was dominated by higher AT (56.5%) than GC (43.5%) content. In the resulting phylogenetic tree using complete mitogenome sequences, flesh-footed shearwater and gray petrel (Procellaria cinerea) grouped together despite the high genetic distance (11.0%) between them, belonging to family Procellariidae. However, the phylogenetic tree was consistent with a previous study using partial nucleotide sequences of the cytochrome b gene. These results highlight that further mitogenome sequences will be required from the closely related species under the genus Ardenna to delineate well-resolved phylo-genetic classification at the genus and or species level. The present study provides a reference mito-chondrial genome of flesh-footed shearwater for further molecular studies. ARTICLE HISTORY
... Fourth, their high mobility makes them an ideal model to evaluate the roles of founder events and vicariance using biogeographic analyses. Fifth, abiotic and biotic factors are known to promote speciation in the shearwaters and related Procellariiformes; for instance, paleoceanographic changes such as the Pleistocene climatic oscillations can act as historical drivers of speciation (Gómez-Díaz, González-Solís, Peinado, & Page, 2006;Silva et al., 2015) and intrinsic biotic factors such as different foraging strategies and allochrony can also promote speciation (Friesen, Smith, et al., 2007;Lombal, Wenner, Lavers, & Austin, 2018;Rayner et al., 2011). Sixth, species limits are controversial, mostly due to high morphological stasis (Austin, Bretagnolle, & Pasquet, 2004;Austin, 1996); indeed, only a few phenotypic traits, such as vocalisation characteristics, slight plumage colour differences and in particular, body size, may differ between closely related species. ...
... During the breeding season and prior to molting, seabirds act as central place foragers, attempting to maximize energy intake by foraging as close as possible to their colony, which drives high intraspecific competition and niche partitioning (Orians & Pearson, 1979). The resulting degree of foraging overlap has been found to correlate with population connectivity, that is, species with population-specific foraging grounds show higher genetic structure (Burg & Croxall, 2001;Calderón, Quintana, Cabanne, Lougheed, & Tubaro, 2014), even when breeding in nearby colonies (Lombal et al., 2017). In these cases, the opportunity for gene flow is much lower than in those that have a common nonbreeding area. ...
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... Spatial segregation during the non-breeding period has also been proposed to increase genetic divergence (Friesen, 2015), with birds from colonies exploiting different foraging areas less likely to mix at sea and return to different colonies. However, individual studies provide conflicting evidence (Burg et al., 2003;Gangloff et al., 2013;Clucas et al., 2014;Quillfeldt et al., 2017;Lombal et al., 2018). ...
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An exhaustive summary of all that is known of the birds of the Australian, New Zealand and Antarctic region. Volume 1 comprises two parts: Part A: Ratites to Petrels Part B: Australian Pelican to Ducks.
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