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RESEARCH ARTICLE
Remarkably low genetic diversity and strong population structure
in common bottlenose dolphins (Tursiops truncatus) from coastal
waters of the Southwestern Atlantic Ocean
Pedro F. Fruet •Eduardo R. Secchi •Fa
´bio Daura-Jorge •Els Vermeulen •
Paulo A. C. Flores •Paulo Ce
´sar Simo
˜es-Lopes •Rodrigo Ce
´zar Genoves •
Paula Laporta •Juliana C. Di Tullio •Thales Renato O. Freitas •Luciano Dalla Rosa •
Victor Hugo Valiati •Luciano B. Beheregaray •Luciana M. Mo
¨ller
Received: 13 September 2013 / Accepted: 24 February 2014
ÓSpringer Science+Business Media Dordrecht 2014
Abstract Knowledge about the ecology of bottlenose
dolphins in the Southwestern Atlantic Ocean is scarce.
Increased by-catch rates over the last decade in coastal
waters of southern Brazil have raised concerns about the
decline in abundance of local dolphin communities. Lack of
relevant data, including information on population structure
and connectivity, have hampered an assessment of the
conservation status of bottlenose dolphin communities in
this region. Here we combined analyses of 16 microsatellite
loci and mitochondrial DNA (mtDNA) control region
sequences to investigate genetic diversity, structure and
connectivity in 124 biopsy samples collected over six
communities of photographically identified coastal bottle-
nose dolphins in southern Brazil, Uruguay and central
Argentina. Levels of nuclear genetic diversity were
remarkably low (mean values of allelic diversity and het-
erozygosity across all loci were 3.6 and 0.21, respectively), a
result that possibly reflects the small size of local dolphin
communities. On a broad geographical scale, strong and
significant genetic differentiation was found between bot-
tlenose dolphins from southern Brazil–Uruguay (SB–U) and
Bahı
´a San Antonio (BSA), Argentina (AMOVA mtDNA
U
ST
=0.43; nuclear F
ST
=0.46), with negligible contem-
porary gene flow detected based on Bayesian estimates. On a
finer scale, moderate but significant differentiation (AM-
OVA mtDNA U
ST
=0.29; nuclear F
ST
=0.13) and
Electronic supplementary material The online version of this
article (doi:10.1007/s10592-014-0586-z) contains supplementary
material, which is available to authorized users.
P. F. Fruet (&)
Programa de Po
´s-Graduac¸a
˜o em Oceanografia Biolo
´gica, FURG,
Rio Grande, Brazil
e-mail: pfruet@gmail.com
P. F. Fruet L. B. Beheregaray L. M. Mo
¨ller
Molecular Ecology Laboratory, School of Biological Sciences,
Flinders University, Bedford Park, SA, Australia
P. F. Fruet E. R. Secchi R. C. Genoves
J. C. Di Tullio L. D. Rosa
Museu Oceanogra
´fico ‘‘Prof. Elie
´zer C. Rios’’, FURG,
Rio Grande, Brazil
P. F. Fruet E. R. Secchi R. C. Genoves
J. C. Di Tullio L. D. Rosa
Laborato
´rio de Ecologia e Conservac¸a
˜o da Megafauna Marinha,
Instituto de Oceanografia, FURG, Rio Grande, Brazil
P. F. Fruet L. M. Mo
¨ller
Cetacean Ecology, Behaviour and Evolution Lab, School of
Biological Sciences, Flinders University, Bedford Park, SA,
Australia
F. Daura-Jorge P. C. Simo
˜es-Lopes
Laborato
´rio de Mamı
´feros Aqua
´ticos (LAMAQ), UFSC,
Floriano
´polis, Brazil
E. Vermeulen
Laboratory of Oceanology - MARE Research Centre, University
of Liege, Lie
`ge, Belgium
P. A. C. Flores
Centro Nacional de Pesquisa e Conservac¸a
˜o de Mamı
´feros
Aqua
´ticos - CMA, ICMBio, MMA, Floriano
´polis, Brazil
P. Laporta
Yaqu-pacha Uruguay, Punta del Diablo, Rocha, Uruguay
T. R. O. Freitas
Departamento de Gene
´tica, Universidade Federal do Rio Grande
do Sul, Porto Alegre, Brazil
V. H. Valiati
Laborato
´rio de Biologia Molecular, Unisinos, Sa
˜o Leopoldo,
Brazil
123
Conserv Genet
DOI 10.1007/s10592-014-0586-z
asymmetric gene flow was detected between five neigh-
bouring communities in SB–U. Based on the results we
propose that BSA and SB–U represent two distinct evolu-
tionarily significant units, and that communities from SB–U
comprise five distinct Management Units (MUs). Under this
scenario, conservation efforts should prioritize the areas in
southern Brazil where dolphins from three MUs overlap in
their home ranges and where by-catch rates are reportedly
higher.
Keywords Cetacean Conservation Connectivity
Population genetics Microsatellite Mitochondrial DNA
Introduction
Bottlenose dolphins (Tursiops spp.) are cetaceans able to
explore, occupy and adapt to different marine environ-
ments, with the exception of polar regions. Many genetic
studies of bottlenose dolphins around the globe have
reported moderate genetic differentiation among regional
populations, despite some reproductive exchange (Sellas
et al. 2005; Rosel et al. 2009; Tezanos-Pinto et al. 2009;
Urian et al. 2009; Mirimin et al. 2011). Over large spatial
scales, genetic discontinuities appear to coincide with
ecological and topographic breaks, such as distinct water
masses, currents and depth contours (Hoelzel et al. 1998a;
Natoli et al. 2004; Bilgmann et al. 2007). On the other
hand, habitat selection (e.g. open coast vs. estuarine eco-
systems) and local adaptation to prey resources are
believed to shape population structure over small spatial
scales (Mo
¨ller et al. 2007; Wiszniewski et al. 2010).
Therefore, a combination of environmental, geomorpho-
logical and evolutionary factors appears to influence the
genetic structure of bottlenose dolphin populations,
although some may represent cryptic species-level differ-
ences (e.g. Natoli et al. 2004; Rosel et al. 2009).
Despite being extensively studied in many regions of the
world, limited information is available for bottlenose dol-
phins of the Southwestern Atlantic Ocean (SWA); partic-
ularly scarce are details of their genetic diversity and
population structure. Understanding population sub-divi-
sions and connectivity provides information critical to the
identification of relevant biological units to be conserved.
These include evolutionary significant units (ESUs)—a
group of historically isolated populations with unique
genealogical and adaptive legacy—and Management Units
(MUs)—demographically distinct populations that should
be managed separately to ensure the viability of the larger
metapopulation (see Funk et al. 2012 for definitions and a
recent perspective on ESUs and MUs). This is especially
important in cases where populations are restricted in dis-
tribution, have small population sizes and are subject to
human induced mortality, which is the case for bottlenose
dolphins of the SWA. It has been reported that in the SWA
coastal bottlenose dolphins are mainly found between
Santa Catarina State, in southern Brazil, and Central
Argentina—and particularly along a narrow coastal corri-
dor between southern Brazil and Uruguay (SB–U) (Laporta
et al. in press). In this region, bottlenose dolphins occur in
bays and estuaries, and between the surf zone and 2 km
from the coastline when in the open-coast, with occasional
records between 2 and 4 km (Laporta 2009; Di Tullio
2009). The distribution of coastal and offshore bottlenose
dolphins apparently does not overlap and their feeding
ecology is distinct, at least in part of the SWA (e.g. Botta
et al. 2012). Concerns about the conservation of coastal
bottlenose dolphins in SWA has recently emerged due to
their relatively small population sizes (Laporta 2009; Fruet
et al. 2011; Daura-Jorge et al. 2013), vulnerability to by-
catch (Fruet et al. 2012) and substantial coastal develop-
ment, particularly in southern Brazil (Tagliani et al. 2007).
A long-term study of dolphin strandings has revealed high
levels of mortality along Brazil’s southernmost coastline,
mainly in areas adjacent to the Patos Lagoon estuary where
by-catch seems to be the main cause of death (Fruet et al.
2012).
Systematic photo-identification studies have shown that
coastal bottlenose dolphins of the SWA consist of small
communities with high site fidelity to estuaries and river
mouths (and each community not exceeding 90 individuals,
Fruet et al. in press a). These are often bordered by other
small bottlenose dolphin communities that show more
extensive movements along the coast, in contrast to estu-
arine communities (Laporta et al. in press). Photo-identi-
fication efforts in the two main estuaries of southern Brazil
suggest that bottlenose dolphins exhibit long-term resi-
dency in these areas (Fruet et al. 2011; Daura-Jorge et al.
2013). Although there is distribution overlap of dolphins
from these estuarine-associated and the adjacent coastal
communities, no information is available on the levels of
genetic connectivity among them. For example, social
network analyses has revealed the existence of at least
three distinct communities, which partially overlap in
range near the Patos Lagoon estuary, in southern Brazil
(Genoves 2013). This includes the year-round resident
community of the Patos Lagoon estuary and two coastal
communities: one that regularly moves from Uruguay to
southern Brazil during winter and spring (Laporta 2009)
and another which appears to inhabit the adjacent coastal
waters of the Patos Lagoon estuary year-round. Such range
overlap suggests potential for interbreeding among indi-
viduals of these communities, which would have implica-
tions for MUs classification and conservation management
efforts. Given the assumption of demographic indepen-
dence between different MUs, their delineation requires a
Conserv Genet
123
direct or indirect estimate of current dispersal rates (Pals-
bøll et al. 2007). However, dispersal rates can be difficult
to estimate, particularly in the marine environment, which
lacks marked physical barriers and where many organisms
are not easily accessible for long-term field studies of
identifiable or tagged individuals. In these cases, genetic
methods generally offer a suitable alternative to assess
dispersal rates and other indicators of demographic inde-
pendence, as well as for estimating genetic diversity.
In this study we investigate the genetic diversity and
population structure of bottlenose dolphins along the SWA
coast using data from nuclear microsatellite markers and
mtDNA control region sequences. We use this information
to assess the strength and directionality of genetic con-
nectivity over a range of spatial scales. Our sampling
design allows comparisons among neighbouring coastal
communities in southern Brazil-Uruguay (SB–U), and
between these and a community inhabiting Bahı
´a San
Antonio (BSA) in the Patagonian coast—the most southern
resident bottlenose dolphin community known for the
SWA and located in a different marine biogeographical
region to southern Brazil-Uruguay. We hypothesize that
specialization for, or association with particular habitat
types such as estuaries and open coasts may promote
genetic differentiation on small spatial scales, while the
biogeographical disjunction may influence differentiation
at broad scale. The adjacent dolphin communities sampled
in SB–U include two estuarine and three open coast com-
munities. If habitat type specialization or, association with,
drives genetic structure, we might expect to find lower
genetic differentiation between communities inhabiting the
contiguous open coast habitat than those living in sheltered
estuarine environments, irrespective of geographical dis-
tances. We also expect that greater differentiation would
characterize communities from different biogeographical
regions. By delineating conservation units for coastal bot-
tlenose dolphins in the SWA we expect to provide scien-
tific support to guide strategies for population monitoring
efforts, conservation status assessment and short-term
management goals.
Methods
Sampling scheme
The study area covers approximately 2,112 km of linear
distance along the coast. It extends from Floriano
´polis, in
southern Brazil, to Bahı
´a San Antonio, in the Patagonian
Argentina. Along this region we surveyed six locations
between 2004 and 2012 and collected 135 samples (Fig. 1).
Samples consisted primarily of skin tissueobtained from free-
ranging coastal bottlenose dolphins (common bottlenose
dolphins, Tursiops truncatus—see Wang et al. (1999)for
southern Brazil bottlenose dolphins molecular taxonomic
identification) belonging to communities inhabiting a variety
of habitat types: Floriano
´polis (FLN, coastal, n=9), Laguna
(LGN, estuarine, n=11), north of Patos Lagoon (NPL,
coastal, n=21), Patos Lagoon estuary (PLE, estuarine,
n=71), south of Patos Lagoon/Uruguay (SPL/URU, coastal,
n=14) and Bahı
´a San Antonio, Argentina (BSA, coastal
bay, n=12) (Table 1). Samples were collected using a
crossbow with 150 lb (68 kg) draw weight and darts and tips
especially designed for sampling small cetaceans (Ceta-Dart,
Copenhagen, Denmark). We attempted to individually iden-
tify sampled dolphins through simultaneous photo-identifi-
cation (see Fruet et al. in press b for details). Samples were
grouped according to the sampled location. For those col-
lected in the adjacent coastal areas of Patos Lagoon estuary,
where three distinct communities live in close proximity and
overlap in their range, identified individuals were grouped
according to the social unit to which they were previously
assigned based on social network analysis (Genoves 2013).
Our dataset also included four samples from freshly stranded
carcasses, two collected in La Coronilla, Uruguay, and two in
southern Brazil from animals known to belong to the NPL
community as photo-identified based on their natural marks
prior to their death. Samples were preserved in 20 % dimethyl
sulphoxide (DMSO) saturated with sodium chloride (Amos
and Hoelzel 1991) or 98 % ethanol.
Genetic methods
Genomic DNA was extracted from all samples following a
salting-out protocol (Sunnucks and Hales 1996). Sex of each
biopsy sample was determined by the amplification of
fragments of the SRY and ZFX genes through the polymerase
chain reaction (PCR) (Gilson et al. 1998), with PCR condi-
tions described in Mo
¨ller et al. (2001). Samples were geno-
typed at 16 microsatellite loci (Online Resource 1) and a
fragment of approximately 550 bp of the control region was
sequenced using primers Dlp-1.5 and Dlp-5 (Baker et al.
1993) on an ABI 3730 (Applied Biosystems) with GenScan
500 LIZ 3130 internal size standard. Procedures for micro-
satellite PCR and genotyping are found in Mo
¨ller and Be-
heregaray (2004), and for mtDNA PCR and sequencing in
Mo
¨ller and Beheregaray (2001). For microsatellites, bins for
each locus were determined and genotypes scored in GENE-
MAPPER 4.0 (Applied Biosystems). Rare alleles (i.e. fre-
quency \0.05) or alleles that fell in between two bins were
re-genotyped. Micro-Checker 2.2.3 (Van Oosterhout et al.
2004) was used to check for potential scoring errors, the
presence of null alleles, stuttering and large allelic drop out.
Genotyping error rates were estimated by re-genotyping 30
randomly selected samples, representing 22 % of the total
sample size used in this study. We used GENALEX6.5
Conserv Genet
123
(Peakall and Smouse 2012) to find potential matches
between genotypes and to estimate the probability of identity
as an indicator of the power of the 16 markers to distinguish
between two sampled individuals. Samples matching at all
genotypes or those mismatching at only a few alleles (1–2)
were double-checked for potential scoring errors. Sequences
of the mtDNA were edited using SEQUENCHER 3.0 (Gene
Codes Corporation, Ann Arbor, MI) and aligned using the
ClustalW algorithm in MEGA 5.05 (Tamura et al. 2011).
Haplotypes were defined using DNASP 5.0 (Librado and Rozas
2009). After careful examination, samples sharing identical
genotypes at all loci, same mtDNA haplotype and sex were
considered as re-sampled individuals and one of each pair
was removed. Re-sampled individuals identified by photo-
identification (n=7) were also confirmed through genetic
methods.
Data analysis
Population structure
We used 10,000 permutations in SPAGEDI to test for the rel-
ative importance of a stepwise mutation model as a
contributor to genetic diversity and structure (Hardy and
Vekemans 2002). This provides a way to assess whether F
ST
or R
ST
potentially provides a more appropriate statistic to
estimate genetic structure since R
ST
accounts for divergence
times between microsatellite alleles and is thus expected to
better reflect older divergences (Hardy et al. 2003). Allele
size permutation test in SPAGEDI were non significant for all
loci. This suggests that F
ST
is likely the most appropriate
estimator, and only F
ST
values are therefore reported here-
after. ARLEQUIN 3.5.1.2 was used for an analysis of molecular
variance (AMOVA) to evaluate differentiation between SB–
U and BSA dolphins, and among SB–U communities, for
both nuclear and mtDNA datasets. Degree of genetic dif-
ferentiation among locations was also assessed using
ARLEQUIN to calculate F
ST
(Weir and Cockerham 1984) for
microsatellites, and both F
ST
and U
ST
measures for mtDNA.
For each of these measures we used the Tamura and Nei
(1993) model with a gamma correction of 0.5. Significance
was tested based on 10,000 permutations. We also estimated
the statistical power to detect nuclear differentiation using
POWSIM (Ryman and Palm 2006) by simulating six popula-
tions with samples sizes of each sampled community (8, 10,
19, 63, 12, 12) with F
ST
of 0.05 (combining generation, time
Fig. 1 Study area in the Southwestern Atlantic Ocean showing the
proposed evolutionary significant units (ESUs) and management units
(MUs) (color counter lines) for coastal common bottlenose dolphins
(Tursiops truncatus), and the respective frequencies of mitochondrial
control region haplotypes (pie charts). Arrows indicate the main
sampling locations for each dolphin community. Approximate
geographic boundaries of management units were built combining
the results of this study with current knowledge on residency, social
structure and movement patterns of bottlenose dolphins along this
region. Specifically for NPL, the genetic assignment of some
individuals regularly sighted approximately 400 km north of Patos
Lagoon estuary (represented by stars) to NPL community were used
as a proxy to define the northern limit of the community range. The
dashed rectangle highlights the area of heightened conservation
concern proposed by this study (see ‘‘Conservation implications’’
section for details). FLN Floriano
´polis, LGN Laguna, NPL north of
Patos Lagoon, PLE Patos Lagoon estuary, SPL/URU south of Patos
Lagoon/Uruguay, BSA Bahı
´a San Antonio. (Color figure online)
Conserv Genet
123
t=25 with effective population size, N
e
=500), which
approximates the lowest empirical fixation index found
based on 15 loci (see ‘‘Results’’ section). The a(Type I)
error was assessed running the same simulated scenario, but
sampling directly from the base population (i.e. setting drift
time t=0). A thousand replicates were run and the signif-
icance of the tests was assessed with Fisher’s exact tests and
Chi square tests.
The Bayesian clustering method implemented in
STRUCTURE 2.3.3 (Pritchard et al. 2000) was also used for
inferring population structure based on the microsatellite
data. We assumed correlated allele frequencies and an
admixture model using sampling location as prior infor-
mation (LOCPRIOR function) (Hubisz et al. 2009). Sim-
ulations were performed using a 200,000 step burn-in
period and 10
6
repetitions of the Markov Chain Monte
Carlo (MCMC) search, assuming number of clusters
(K) varying between 1 and 6. We performed 20 indepen-
dent runs to limit the influence of stochasticity, to increase
the precision of the parameter estimates, and to provide an
estimate of experimental reproducibility (Gilbert et al.
2012). The most likely K was explicitly determined by
examining DK (Evanno et al. 2005)inS
TRUCTURE HAR-
VESTER (Earl and vonHoldt 2012). Following the recom-
mendations of Evanno et al. (2005), we ran an iterative
process where, for each most likely K detected by STRUC-
TURE, we independently re-analyzed the data to test for
further sub-division. This process was repeated until the
most likely K was 1.
Isolation by distance (IBD) was assessed by conducting
Mantel tests (Mantel 1967) between matrices of F
ST
genetic distances and geographical distances measured as
the shortest marine coastal distance between two locations.
Given the large geographical distance between the south-
ernmost sampling site (BSA) and others, we excluded BSA
from the IBD analysis. We also used partial Mantel tests to
test for an association between habitat type (estuarine
versus coastal) and genetic distance, while controlling for
the effect of geographical distance. Both tests were run
with 1,000 random permutations in GENODIVE 2.0.
Gene flow
Magnitude and direction of contemporary gene flow among
the six sampled communities was estimated using BAYE-
SASS 3.0 (Wilson and Rannala 2003). The software uses a
MCMC algorithm to estimate the posterior probability
distribution of the proportion of migrants from one popu-
lation to another. This was conducted with ten independent
MCMC runs of 10
7
steps, with the first 10
6
repetitions
discarded as burn-in. To reach the recommended accep-
tance rates of total iterations between 20 and 40 % we
adjusted the values of continuous parameters such as
Table 1 Ecological information and summary of genetic diversity for the six communities and the two proposed evolutionary significant units (ESUs) of coastal common bottlenose dolphins
(Tursiops truncatus) based on mtDNA control region sequences and 15 microsatellite loci
N(f:m) Pop. size
(95 % CI)
Habitat
type
mtDNA Microsatellites
hpPA NA AR H
E
H
O
F
IS
PI
U
PI
SIBS
Southern Brazil–
Uruguay ESU
FLN 8 (6:2) Unknown Coastal 0.7500 (0.0965) 0.0045 (0.0032) 0 1.6 1.6 0.19 0.23 -0.22 1.5 910
-3
4.3 910
-2
LGN 10 (2:8) 59 (49–72)
a
Estuarine 0.0000 (0.0000) 0.0000 (0.0000) 0 1.6 1.5 0.21 0.15 0.28* 1.3 910
-3
3.6 910
-2
NPL 19 (8:11) Unknown Coastal 0.5425 (0.1231) 0.0067 (0.0041) 2 2.3 1.9 0.20 0.19 0.06 7.5 910
-4
3.5 910
-2
PLE 63 (38:25) 86 (78–95)
b
Estuarine 0.4808 (0.0621) 0.0072 (0.0042) 9 3.0 2.0 0.26 0.26 -0.01 4.6 910
-5
9.7 910
-3
SPL/
URU
12 (5:7) Unknown Coastal 0.6484 (0.1163) 0.0067 (0.0041) 5 2.1 1.9 0.20 0.23 -0.02 3.5 910
-4
2.4 910
-2
Total 112 (59:53) – – 0.6457 (0.0404) 0.0096 (0.0053) 16 3.7 2.2 0.22 0.22 0.02 – –
Bahı
´a San Antonio ESU BSA 12 (2:10) 76 (70–97)
c
Coastal Bays 0.0000 (0.0000) 0.0000 (0.0000) 1 1.76 1.76 0.19 0.18 0.08 2.6 910
-3
5.4 910
-2
Total 124 (61:63) – – 0.7022 (0.0352) 0.0195 (0.0100) – 3.6 – 0.28 0.23 0.194* – –
Ntotal number of individuals (separated by sex); PA number of private alleles; NA mean number of alleles per locus; AR mean allelic richness; H
E
mean expected heterozygosity; H
O
mean
observed heterozygosity; F
IS
inbreeding coefficient; PI
U
,PI
SIBS
probabilities of identity for unbiased samples and samples of full-sibs, respectively
* Significant multi-locus Pvalue (P\0.001)
a
Daura-Jorge et al. (2013),
b
Fruet et al. (2011),
c
Vermeulen and Cammareri (2009)
Conserv Genet
123
migration rates (D
M
), allele frequencies (D
A
) and inbreed-
ing coefficient (D
F
) to 0.9, 0.6 and 0.8, respectively.
Samples were collected every 200 iterations to infer the
posterior probability distributions of parameters. Trace files
were monitored for convergence and runs with potential
problems were discarded. Additionally, convergence was
checked by comparing the migration rate profile between
the runs according to their average total likelihood and
associated credible confidence interval (CI).
Genetic diversity
For microsatellites, genetic diversity, expressed as number
of alleles (NA), expected (H
E
) and observed (H
O
) hetero-
zygosity, as well as the inbreeding coefficient (F
IS
) were
estimated for each community in GENODIVE 2.0 (Meirmans
and Van Tienderen 2004). Departures from Hardy–Wein-
berg equilibrium and linkage disequilibrium were tested
using the Fisher’s exact test and a Markov chain method
with 1,000 iterations in GENEPOP 4.2 (Rousset 2008). Allelic
richness (AR) was estimated in FSTAT 2.9.3.2 (Goudet
1995). All statistical tests followed sequential Bonferroni
correction to address type I errors associated with multiple
comparisons (Rice 1989). For the mtDNA sequences, we
used ARLEQUIN 3.5.1.2 (Excoffier and Lischer 2010)to
estimate haplotypic and nucleotide diversities. A median-
joining network from the mtDNA haplotypes was con-
structed using NETWORK 4.6.1.1 (Bandelt et al. 1999).
Results
Summary statistics
A total of 134 biopsy samples and four samples from
stranded carcasses were used. All samples were success-
fully amplified at 16 microsatellite loci and sequenced for
approximately 550 bp of the mtDNA control region. Only
eight out of 450 repeated genotypes (1.7 %) did not match
but were resolved by re-genotyping. The probability of two
unrelated individuals or siblings sharing the same geno-
types was very low for all communities (Table 1). Multiple
lines of evidence (identical genotype, same mtDNA
sequence and sex) suggested that 14 biopsied individuals
were sampled twice, including seven individuals that were
suspected re-samples based on photo-identification. All re-
sampled animals were biopsied in the same location: eight
in PLE, two in SPL/URU, two in NPL, one in LGN, and
one in FLN. After removal of duplicates, 124 samples were
included in the final dataset analyzed. From these, 61
samples were males and 63 were females (Table 1).
The microsatellite locus Tur91 was monomorphic and
therefore excluded from further analysis. We found no
evidence for effects of large allelic dropout in any locus.
Null alleles were detected for two loci but these were not
consistent among sampled locations (locus TUR80 in PLE
and Ttr04 in BSA), and therefore the loci were kept for all
analyses. One locus pair (TUR105 and EV37) showed
evidence of linkage disequilibrium. However, because
similar results were obtained when analyses were run both
with and without TUR105 this locus was kept in the
dataset. Laguna was the only sample location that showed
significant deviation from Hardy–Weinberg equilibrium
when averaged across all loci, likely due to inbreeding
(F
IS
=0.28) in this small community. Inbreeding coeffi-
cient was low and non-significant for all other communities
(Table 1).
Genetic structure
The AMOVA results showed strong differentiation
between SB–U and BSA for both microsatellites
(F
ST
=0.46, P\0.001) and mtDNA (U
ST
=0.43,
P\0.0001). On a smaller spatial scale, the AMOVA
indicated moderate differentiation among SB–U commu-
nities, for both microsatellites (F
ST
=0.13, P\0.0001)
and mtDNA (U
ST
=0.29, P\0.0001). Accordingly, sig-
nificant differentiation was observed for all pairwise
comparisons using microsatellites (Table 2), but over a
wide range of F
ST
values (0.066–0.617). Excluding BSA,
which was by far the most differentiated (average F
ST
of
0.51 for all comparisons with other communities), moder-
ate but significant differentiation was found between all
other pairwise comparisons, with the two geographically
closest communities (PLE and NPL) having the lowest
value of F
ST
(F
ST
=0.06; P\0.001). POWSIM simulations
for 15 microsatellite loci and the sample sizes used in this
study suggested a 100 % probability of detecting differ-
entiation above the lowest empirical F
ST
level of differ-
entiation, indicating satisfactory statistical power for our
analyses. The estimated type I error varied from 0.041 with
Fisher’s exact tests to 0.083 with v
2
tests, which approxi-
mates the conventional 5 % limit for significance testing.
Results of pairwise comparisons using mtDNA were
generally congruent with results from the microsatellite
analyses, albeit with higher levels of differentiation
between communities. The exceptions were NPL and PLE
(for both F
ST
and U
ST
), and NPL and FLN (for U
ST
only),
which showed no significant differentiation (Table 3). All
three of these communities are dominated by the most
common mtDNA haplotype (H08). Pairwise significant F
ST
values ranged between 0.097 (NPL–FLN) to 1 (LGN–
BSA), with BSA the most differentiated community across
all comparisons.
Mantel tests revealed a positive and significant corre-
lation between microsatellites and mtDNA fixation indices
Conserv Genet
123
and geographical distances, suggesting a pattern of IBD
(Fig. 2). For the mtDNA data, the correlation was not as
strong (r
2
=0.428) as for the microsatellites (r
2
=0.934),
but still significant. Results of partial Mantel tests (details
not shown) suggested that differentiation was more likely
influenced by distance than by habitat type (estuarine
versus coastal). When controlling for geographical dis-
tances, non-significant relationships between locations and
clusters (cluster 1 and 2: estuarine and coastal communi-
ties, respectively) were found for both microsatellites
(r
2
=-0.437; P=0.51) and mtDNA (r
2
=-0.525;
P=0.52).
Bayesian posterior probabilities indicated that the data-
set is best explained by the clustering of samples into two
genetic populations (K =2), with all individuals from
BSA placed in one cluster and remaining individuals
sampled in SB–U placed in a second cluster (Fig. 3a).
Negligible admixture appears to exist between these two
clusters, with assignment estimates of all individuals to
their respective clusters above 0.99 and 0.98, respectively.
Testing for further sub-division by running STRUCTURE
for the set of northern communities led to the identification
of additional partitioning within SB–U most consistent
with five populations (Fig. 3b–d). No sub-division was
detected within BSA (data not shown).
Gene flow
Estimates of contemporary gene flow inferred in BAYESASS
suggested very low gene flow from BSA to SB–U com-
munities (2.2 %) and negligible gene flow in the opposite
direction (0.3 %). Within the SB–U region, BAYESASS
revealed moderate and complex asymmetrical migration
rates (Table 4; Fig. 4) consistent with the inferred pattern
of IBD. Generally, higher migration occurred between
neighbouring communities than between those separated
by greater geographic distances, with the exception of
LGN, which seems to exchange more migrants with more
distant communities than with its closest neighbouring
community (FLN). Migration estimates between sampling
locations at the extremities of the sampling distribution was
low. Estimated migration rates from FLN to NPL and from
SPL/URU to PLE were at least twice the rates between all
other community pairs (Fig. 4). For the estuarine commu-
nities, PLE seems to act as a sink with a considerable rate
of migrants coming from LGN, NPL and SPL/URU, and
negligible migration in the opposite direction. In contrast,
LGN seems to be more closed to immigration while con-
tributing genetic migrants to PLE and NPL.
Genetic diversity
Levels of genetic variation were remarkably low for all
samples as measured by both allelic richness (AR) and
expected heterozygosity (H
E
) (Table 1; Appendix).
Observed heterozygosity (H
O
) ranged from 0.15 to 0.26,
with a mean across all loci of 0.21. AR ranged from 1.5 to
Table 2 Estimates of microsatellite differentiation among six coastal
communities of common bottlenose dolphins (Tursiops truncatus)
sampled along the Southwestern Atlantic Ocean
FLN LGN NPL PLE SPL/
URU
BSA
FLN –
LGN 0.131** –
NPL 0.147** 0.169** –
PLE 0.144** 0.101** 0.066** –
SPL/
URU
0.289** 0.250** 0.156** 0.101** –
BSA 0.617** 0.502** 0.538** 0.423** 0.477** –
Differentiation is expressed as F
ST
based on 15 microsatellites loci
FLN Floriano
´polis, LGN Laguna, NPL north of Patos Lagoon, PLE
Patos Lagoon estuary, SPL/URU south of Patos Lagoon/Uruguay,
BSA Bahı
´a San Antonio
*P\0.05; ** P\0.01
Table 3 Estimates of mitochondrial differentiation among six coastal communities of common bottlenose dolphins (Tursiops truncatus)
sampled along the Southwestern Atlantic Ocean
FLN LGN NPL PLE SPL/URU BSA
FLN – 0.659** 0.100* 0.209** 0.249** 0.687**
LGN 0.893** – 0.622** 0.572** 0.666** 1.000**
NPL 0.040 0.744** – 0.009 0.297** 0.679**
PLE 0.198* 0.489** 0.06 – 0.329** 0.638**
SPL/URU 0.531** 0.466** 0.392** 0.230** – 0.689**
BSA 0.639** 1.000** 0.399** 0.340** 0.609** –
Differentiation is expressed as U
ST
(above diagonal) and F
ST
(below diagonal) based on 457-bp of the mtDNA control region
FLN Floriano
´polis, LGN Laguna, NPL north of Patos Lagoon, PLE Patos Lagoon estuary, SPL/URU south of Patos Lagoon/Uruguay, BSA Bahı
´a
San Antonio
*P\0.05; ** P\0.01
Conserv Genet
123
2.0, being higher in PLE, NPL and SPL/URU, and lower in
LGN and BSA. Number of alleles per locus ranged from
two to seven (Appendix) with a mean across all loci of 3.6,
while the mean number of alleles per community was two.
Out of 17 ‘‘private’’ (unique) alleles identified, nine were
found in PLE, five in SPL/URU, two in NPL and one in
BSA (Table 1). The only private allele in BSA was found
in high frequency in that community, while in all other
communities unique alleles had low frequencies.
After sequence alignment and editing, 457 bp of the
mtDNA control region could be analyzed for the same 124
individuals used for the microsatellite analysis. Thirteen
polymorphic sites (all transitional mutations) revealed nine
distinct haplotypes. The number of haplotypes detected in
each sampled location varied from one to five, and hap-
lotype diversity ranged from 0 to 0.75. Overall, nucleotide
diversity among all individuals was low (p=0.009), and
haplotype diversity moderate (h=0.712), although values
varied among communities. FLN community displayed the
highest level of haplotype diversity, while PLE had the
highest nucleotide diversity (Table 1). The most common
and widely dispersed haplotype (H8) was found in 49.6 %
of the individuals and across all locations, except in LGN
and BSA where all dolphins shared the same haplotypes
(H7 for LGN and H4 for BSA). Private haplotypes were
found in four of the six communities (FLN, n=1; NPL,
n=1; SPL/URU, n=2; BSA, n=1) (Fig. 1).
The median-joining network showed two main groups
of haplotypes separated by a minimum of five mutational
steps (Fig. 5). Individuals from PLE, NPL and SPL/URU
communities were present in both groups while individ-
uals from LGN, BSA and FLN were represented in only
one of the groups. Bahı
´a San Antonio retains a unique
haplotype (H05), which is fixed for this location and
differs from the most common haplotype (H08) by one
mutational step.
Discussion
This study comprises the first comprehensive assessment of
population structure and genetic diversity of coastal
Fig. 2 Isolation by distance
plots using Euclidean distance
(km) and genetic distance (F
ST
)
among five coastal communities
of common bottlenose dolphins
(Tursiops truncatus) inhabiting
southern Brazil–Uruguay based
on amtDNA control region and
b15 microsatellite loci (lower
box)
Conserv Genet
123
bottlenose dolphins (Tursiops truncatus) along the SWA.
On a large spatial scale, we report on two genetic popu-
lations (SB–U and BSA) that are highly differentiated and
show very low level of gene flow. On a smaller spatial
scale, we detected low to moderate levels of asymmetric
gene flow between communities within the SB–U popula-
tion and an influence of geographic distance in shaping
patterns of connectivity, perhaps with the exception of
Laguna. Here we also show that coastal bottlenose dolphins
in the SWA have very low levels of genetic diversity. This
reduced gene flow and genetic diversity, combined with the
small size and probable demographic independence of
communities, limit the likelihood of replenishment if they
undergo a genetic or demographic decline, highlighting the
need to implement local-based monitoring and conserva-
tion plans.
Large-scale population structure in SWA bottlenose
dolphins
On a broad geographical scale, our results indicate that
bottlenose dolphins in coastal Argentinean Patagonia (BSA
A
B
C
D
Fig. 3 STRUCTURE Bayesian assignment probabilities for common
bottlenose dolphins (Tursiops truncatus) based on 15 microsatellite
loci. Each vertical line represents one individual dolphin and vertical
black lines separate the sampled communities. We run an iterative
process where for each most likely K detected by STRUCTURE we
independently re-analyzed the data to test for further sub-division
(Evanno et al. 2005; Pritchard et al. 2007). This process was repeated
iteratively until the highest likelihood values resulted in K =1. When
all samples were analyzed together, STRUCTURE clearly separated
individuals sampled in BSA from all those sampled in southern
Brazil/Uruguay, resulting in K =2(a). The highest DK for the next
run within southern Brazil/Uruguay communities was for K =2,
clustering LGN, PLE and SPL/URU, and FLN and NPL (b). When we
run STRUCTURE independently for the above-mentioned clusters,
the highest DK resulted for K =3(c) and K =2(d), respectively.
FLN Floriano
´polis, LGN Laguna, NPL north of Patos Lagoon, PLE
Patos Lagoon estuary, SPL/URU south of Patos Lagoon/Uruguay,
BSA Bahı
´a San Antonio. (Color figure online)
Conserv Genet
123
community) are highly differentiated from those sampled
along the southern Brazil–Uruguay (SB–U) coast, likely
reflecting a combination of IBD and environmental dif-
ferentiation. Several studies have argued that bottlenose
dolphins are capable of specialization for a variety of
habitats and prey types, and that such specialization could
promote genetic divergence (Hoelzel et al. 1998a; Natoli
et al. 2004;Mo
¨ller et al. 2007; Tezanos-Pinto et al. 2009;
Wiszniewski et al. 2010;Mo
¨ller 2012). Bahı
´a San Antonio
is located in the San Matı
´as Gulf (Fig. 1), which is part of
the Northern Patagonian gulfs of Argentina. Geomorpho-
logical characteristics (bathymetry and coastal complex-
ity), oceanographic processes (upwelling, nutrient input,
sea surface temperature regimes and currents), and bio-
logical community structure biogeographically distin-
guishes the Patagonian region from the rest of the Atlantic
coast (Balech and Ehrlich 2008; Tonini 2010). For exam-
ple, archaeozoological evidence suggests that one of the
main prey species of bottlenose dolphins in SB–U, the
white croaker (Micropogonias furnieri) (Pinedo, 1982;
Mehsen et al. 2005), is currently absent from BSA (Scar-
tascini and Volpedo 2013), which is the northernmost limit
for many prey species confirmed to be part of the diet of
bottlenose dolphins in Patagonia (e.g. pouched lamprey
(Geotria australis), Patagonian octopus (Octopus tehuel-
chus), Argentine Hake (Mercluccius hubbsi) (Crespo et al.
2008), as it is located at the boundary between two bio-
geographic regions (Galva
´n et al. 2009). Regional differ-
ences in prey distribution and abundance are thought to
play a role on the genetic structuring of bottlenose dolphins
elsewhere (e.g. Bilgmann et al. 2007). Therefore, BSA
bottlenose dolphins may have different foraging adapta-
tions compared to SB–U bottlenose dolphins. The high
degree of differentiation at neutral markers and the results
from the Bayesian analysis of migration rates imply neg-
ligible gene flow between bottlenose dolphin communities
of these two regions. Future studies combining morpho-
logical, genetic, environmental, and ecological data are
needed to better clarify the taxonomic status between BSA
and SB–U coastal bottlenose dolphins.
Fine-scale population structure in SWA bottlenose
dolphins
In spite of their high dispersal potential, several empirical
studies have shown that coastal bottlenose dolphins often
form discrete population units, even at very small geo-
graphical scales (e.g. Sellas et al. 2005;Mo
¨ller et al. 2007;
Rosel et al. 2009; Ansmann et al. 2012). Our results from
both fixation indices and the Bayesian clustering analysis
confirmed that the five studied communities within the SB–
U population are genetically distinct, indicating higher
genetic differentiation than expected over small
Table 4 Estimates of recent migration rates among six coastal communities of common bottlenose dolphins (Tursiops truncatus) sampled along the Southwestern Atlantic Ocean
From To
FLN LGN NPL PLE SPL/URU BSA
FLN 0.6915 (0.646–0.736) 0.0232 (0.019–0.066) 0.2152 (0.133–0.296) 0.0237 (0.019–0.067) 0.0232 (0.019–0.065) 0.0232 (0.019–0.063)
LGN 0.0209 (0.017–0.058) 0.6887 (0.648–0.728) 0.1289 (0.016–0.241) 0.1197 (0.007–0.232) 0.0209 (0.017–0.058) 0.0210 (0.017–0.059)
NPL 0.0126 (0.011–0.036) 0.0127 (0.011–0.036) 0.8454 (0.738–0.952) 0.1036 (0.001–0.208) 0.0127 (0.012–0.037) 0.0129 (0.010–0.036)
PLE 0.0050 (0.004–0.015) 0.0054 (0.004–0.015) 0.0455 (0.003–0.094) 0.9343 (0.883–0.985) 0.0049 (0.010–0.019) 0.0049 (0.004–0.014)
SPL/URU 0.0181 (0.015–0.051) 0.0179 (0.016–0.052) 0.0237 (0.029–0.076) 0.2367 (0.141–0.331) 0.6855 (0.621–0.749) 0.0180 (0.015–0.051)
BSA 0.0182 (0.015–0.051) 0.0183 (0.015–0.051) 0.0182 (0.015–0.052) 0.0185 (0.015–0.052) 0.0183 (0.015–0.052) 0.9084 (0.841–0.975)
Bold denotes the proportion of non-migrants in each dolphin community. 95 % CI values are given in brackets
FLN Floriano
´polis, LGN Laguna, NPL north of Patos Lagoon, PLE Patos Lagoon estuary, SPL/URU south of Patos Lagoon/Uruguay; BSA Bahı
´a San Antonio
Conserv Genet
123
geographical scales. Relatively lower degrees of nuclear
genetic differentiation are commonly reported for bottle-
nose dolphins over comparable spatial scales with the
exception of the high differentiation found among the
neighbouring communities of T. truncatus in Irish coastal
waters (Shannon estuary and Connemara–Mayo commu-
nities F
ST
=0.179; Mirimin et al. 2011). For instance,
lower differentiation was found between neighbouring
communities of T. truncatus along the coast of the western
North Atlantic (minimum and maximum reported F
ST
values of 0.002 and 0.015, respectively; Rosel et al. 2009)
and Bahamas (F
ST
=0.048; total distance between two
sampling sites was 116 km; Parsons et al. 2006).
For highly mobile, long-lived animals with low repro-
ductive rates such as cetaceans, it is well accepted that a
combination of mechanisms including habitat selection,
specialized foraging behaviours, social structure and natal
philopatry can drive population differentiation across small
spatial scales (Hoelzel 2009;Mo
¨ller 2012). For a closely
related species, the Indo-Pacific bottlenose dolphins,
restricted gene flow between some coastal and estuarine
communities appears to have occurred after coastal dol-
phins colonized the embayment, as a consequence of high
site fidelity and resource and behavioural specializations
(Mo
¨ller et al. 2007). In our study, however, we actually
found similar levels of genetic differentiation when com-
paring coastal and estuarine communities or among coastal
communities of the common bottlenose dolphin in SWA.
This pattern is contrary to what would be expected if
habitat type was a main driver of bottlenose dolphin pop-
ulation structure in the region. Instead, for most commu-
nities, structure appeared to follow an isolation-by-distance
model, where exchange of individuals seems to more likely
occur between adjacent communities, irrespective of hab-
itat type. The only exception was Laguna, which appeared
as an outlier to the IBD model. In Laguna, a unique for-
aging tactic involving cooperative interactions between
dolphins and beach-casting fishermen has evolved. It has
been suggested that the propagation of such behaviour
through social learning has a matrilineal origin, where the
mother–calf relationship might create conditions suitable
for behavioural information exchange (Daura-Jorge et al.
2012). In such special conditions, the costs to individuals
of leaving a suitable habitat is likely greater than the risk of
searching for more profitable locations. In contrast, some
Fig. 4 Schematic diagram showing the recent asymmetric migration
rates estimated between five coastal communities of common
bottlenose dolphins (Tursiops truncatus) sampled along southern
Brazil and Uruguay. The width of the arrows corresponds to the rates
of gene flow between putative populations
Fig. 5 Median-joining network of mtDNA control region haplotypes
in coastal common bottlenose dolphins (Tursiops truncatus). The size
of the circles is proportional to the total number of individuals bearing
that haplotype. Dashed lines separate the two main groups of
haplotypes. Different colors denote the different sampled communi-
ties: FLN Floriano
´polis, LGN Laguna, NPL north of Patos Lagoon,
PLE Patos Lagoon estuary, SPE/URU south of Patos Lagoon/
Uruguay, BSA Bahı
´a San Antonio. Dashes represent extinct or
unsampled haplotypes. (Color figure online)
Conserv Genet
123
PLE dolphins frequently interact with animals from other
communities in the coastal zone, and there is no evidence
of particular feeding specializations compared to LGN.
Thus, it appears that feeding specializations (LGN) and
sociality (PLE), instead of habitat type per se, may play a
role in shaping genetic structure of bottlenose dolphins in
these regions.
The contemporary asymmetric gene flow found in our
study system suggests moderate levels of connectivity
among communities in SB–U ESU, which are consistent
with a metapopulation. Gene flow is particularly mediated
by coastal communities, especially FLN and SPL/URU,
although estuarine communities exchange genes as well. It
seems that PLE potentially acts as a sink, receiving low to
moderate number of migrants while not contributing sub-
stantially to other communities. In contrast, LGN showed
much lower gene flow with adjacent communities, appar-
ently constituting a more closed genetic unit. This pattern
is also supported by mitochondrial data, which suggested
high connectivity between PLE and the adjacent coastal
community (NPL), but high maternal philopatry and
restricted dispersal of LGN dolphins.
Remarkably low levels of genetic diversity in SWA
bottlenose dolphins
Low genetic variation was detected with both mitochon-
drial and nuclear DNA markers across all communities.
Levels of variation at the mtDNA control region were
similar to those reported for T. truncatus in other parts of
the world. In contrast, nuclear DNA variation for all
communities was much lower than that reported for other
local coastal communities elsewhere (see Online Resource
2 for comparisons with studies of Parsons et al. 2006; Rosel
et al. 2009; Tezanos-Pinto et al. 2009; Mirimin et al. 2011;
Caballero et al. 2012). This is supported by the low num-
bers of alleles, reduced allelic richness and reduced het-
erozygosity. For LGN and BSA communities in particular,
the remarkably low variation at both marker types fall
within the range observed for cetaceans with extremely
small populations sizes (i.e. \100 individuals), such as the
subspecies of Hector’s dolphins, Cephalorhyncus hectori
mauii (Hamner et al. 2012), and the Black Sea subspecies
of the harbour porpoise, Phocoena phocoena relicta (Rosel
et al. 1995). These findings are consistent with the current
abundance estimates of less than 90 individuals for the
BSA, PLE, and LGN communities (Vermeulen and Cam-
mareri 2009; Fruet et al. 2011; Daura-Jorge et al. 2013) and
may also reflect the potential small size of the other
communities (such as FLN, NPL and SPL/URU) for which
estimates of abundance are not currently available. Several
authors have suggested that coastal populations of bottle-
nose dolphin elsewhere might have originated via inde-
pendent founder events from offshore populations,
followed by local adaptation and natal philopatry (Hoelzel
et al. 1998a; Natoli et al. 2004; Sellas et al. 2005;Mo
¨ller
et al. 2007; Tezanos-Pinto et al. 2009), leading to a
reduction in genetic diversity.
Conservation implications
On a large geographical scale our results strongly support
that SB–U and BSA dolphins constitute at least two distinct
ESUs, and these warrant separate conservation and man-
agement strategies. The SB–U ESU comprises a set of
communities (or sub-populations) distributed along a nar-
row strip of the coast between Florianopolis (27°210S) in
southern Brazil, and the southern limit of the Uruguayan
coast (34°550S). The BSA ESU geographical range goes
possibly from the northern border of Rio Negro Province,
at the Rio Negro estuary (41°010S), to southern Golfo
Nuevo (43°050S), as suggested by sightings of bottlenose
dolphins in northern Patagonia (Vermeulen and Cammareri
2009; Coscarella et al. 2012). Our results indicate that these
two ESUs are genetically isolated which has important
implications for future conservation plans. It is funda-
mental that managers design appropriate conservation
strategies for each ESU, taking into account their respec-
tive threats, genetic and ecological processes shaping
structure, and geographical distribution in space and time,
as their responses to future environmental changes may
possibly differ. This is of particular relevance for BSA
dolphins since they apparently constitute the only popula-
tion within that ESU with reduced abundance and signs of
historical decline (Bastida and Rodrı
´guez 2003; Coscarella
et al. 2012).
The most serious and continuous threats for bottlenose
dolphins along the SWA coast are found within the SB–U
ESU, where they have experienced increased rates of
human-related mortalities during the past decade (Fruet
et al. 2012). These animals also face considerable coastal
habitat degradation as a consequence of ongoing industrial
and port development activities (Tagliani et al. 2007).
Based on this study we suggest that these dolphin com-
munities within SB–U are functionally independent, and
therefore should be treated as separate MUs for conserva-
tion purposes. We advocate for managers to adopt the
proposed MUs reported here (see Fig. 1), while
Conserv Genet
123
recognizing that their boundaries may change as more
information on dolphin home ranges and population
genetic structure becomes available. Under this proposed
management scenario, conservation programs should be
directed towards the Patos Lagoon estuary and adjacent
coastal waters where dolphins from distinct communities
(PLE, NPL and SPL/URU) show overlapping home ranges,
and where by-catch rates are reportedly higher (Fig. 1).
Protecting dolphins in this region would reduce the risk of
disrupting connectivity between MUs and increase the
chances of long-term viability. Strategies should reduce the
impact of by-catch and maximize the protection of ‘‘cor-
ridors’’ in coastal areas for maintaining connectivity
between adjacent dolphin communities.
The very low levels of genetic diversity in coastal bot-
tlenose dolphins from SWA could be a source for concern.
The importance of genetic variation relates to multiple
aspects of population resilience and persistence, and is
usually assumed to be critical for long-term fitness and
adaptation (Franklin 1980; Charlesworth and Willis 2009),
although some studies have shown that minimal genetic
variation is not always a reliable predictor of extinction risk
in wild populations (e.g. Schultz et al. 2009). We propose,
however, the adoption of a precautionary approach for
coastal bottlenose dolphins in SWA. Although there is no
evidence of inbreeding depression for bottlenose dolphins
in this region, the possibility of inbreeding in the small
LGN community (Table 1) may, in the long-term, be det-
rimental to its viability since inbreeding can increase vul-
nerability to environmental stressors (O’Brien et al. 1985;
Frankham 1995; Spielman et al. 2004; Hale and Briskie
2007). Bottlenose dolphins from Laguna and their neigh-
bouring community (FLN) are being affected by a chronic
dermal infection, the fungal Lobomycosis, and Lobomy-
cosis-like disease (LLD) (Van Bressen et al. 2007, Daura-
Jorge and Simo
˜es-Lopes 2011), with evidence of an
increase in the number of affected animals in recent years
(Daura-Jorge and Simo
˜es-Lopes 2011). While our results
suggest restricted dispersal of LGN dolphins, which may
limit the spread of the disease, the isolated nature of this
community can potentially accelerate fungal transmission
among resident dolphins.
Conclusions
Common bottlenose dolphins from coastal waters of the
SWA are characterized by unprecedentedly low mito-
chondrial and nuclear DNA diversity. Moderate to strong
levels of population differentiation at both marker types
were also disclosed and are likely associated with a com-
bination of geographical, environmental and social factors.
The pattern of genetic differentiation and the negligible
migration rates detected suggest two distinct lineages, or
evolutionarily significant units, one in Argentina and the
other in southern Brazil-Uruguay. In addition, five distinct
communities, or Management Units, characterized by low
to moderate asymmetrical gene flow were identified in
southern Brazil–Uruguay—a region where human activi-
ties negatively impact upon common bottlenose dolphins.
We propose that policies and practices relevant to conser-
vation management of common bottlenose dolphins in
coastal waters of the SWA should recognize the existence
of two lineages, as well as promote connectivity between
the estuarine and open-coast populations in southern Brazil
and Uruguay to ensure their long-term persistence.
Acknowledgments We thank many people who have helped during
our field surveys along South America and provided logistical sup-
port, including Alejandro Cammarieri, Dan Jacob Pretto, Paulo
Mattos, Paulo Henrique Ott, Mauricio Cantor, Ana Costa, Jonatas
Henrique Prado, Mariana Rosa Fetter, Rafael V. Camargo, Ma
´rcia
Bozzeti, Juliana Wolmann Gonc¸alves, Caio Eichenberger and Ricardo
Castelli. Special thanks to Lauro Barcellos (Director of the Museu
Oceanogra
´fico-FURG) for providing logistical support to this project.
Jonatan Sandovall-Castillo, Chris Brauer, Fabrı
´cius Domingos and
Kerstin Bilgmann provided helpful advice on molecular methods and
analysis. This study was made possible by the financial support of
Yaqu Pacha Foundation (Germany), the Brazilian Long Term Eco-
logical Program (PELD—National Council for Research and Tech-
nological Development/CNPq), Porto do Rio Grande (Brazil), and
grants-in-aid-research provided by the Society for Marine Mammal-
ogy in 2001 (USA). The Coordination for Enhancement of Higher
Education Personnel (CAPES—Brazil) provided a PhD scholarship to
P.F. Fruet (Programa de Po
´s-Graduac¸a
˜o em Oceanografia Biolo
´gica,
Instituto de Oceanografia, Universidade Federal do Rio Grande-
FURG). National Council for Research and Technological Develop-
ment (Brazil) provided a fellowship to E.R.Secchi (PQ 307843/2011-
4) and an international fellowship to P.F Fruet (SWE 201567/2011-3).
Flinders University of South Australia provided a fee waiver as part
of P.F. Fruet’s PhD cotutelle program between this university and
Universidade Federal do Rio Grande. Samples were collected under
regional permits (Brasil: SISBIO 24429-1 issued to PAC Flores,
SISBIO 24407-2 issued to PF Fruet) and transferred to Australia
under CITES permits 11BR007432/DF and 2011-AU-647980. This is
a contribution of the Research Group ‘‘Ecologia e Conservac¸a
˜oda
Megafauna Marinha—EcoMega/CNPq’’ and is also publication #52
from MEGMAR (the Molecular Ecology Group for Marine Research
at Flinders University).
Appendix
See Table 5.
Conserv Genet
123
Table 5 Genetic diversity screened at 16 microsatellite loci in six coastal communities of common bottlenose dolphin sampled along the Southwestern Atlantic
FLN (n=8) LGN (n=10) NPL (n=19) PLE (n=63) SPL/URU (n=12) BSA (n=12)
NA H
O
H
E
PNAH
O
H
E
PNAH
O
H
E
PNAH
O
H
E
PNAH
O
H
E
PNAH
O
H
E
P
Tur4_142
a
1 0.00 0.00 NA 1 0.00 0.00 NA 1 0.00 0.00 NA 2 0.01 0.01 1.00 1 0.00 0.00 NA 1 0.00 0.00 NA
Tur4_91
a
1 0.00 0.00 NA 1 0.00 0.00 NA 1 0.00 0.00 NA 1 0.00 0.00 NA 1 0.00 0.00 NA 1 0.00 0.00 NA
Tur4_141
a
2 0.25 0.23 1.00 1 0.00 0.00 NA 1 0.00 0.00 NA 2 0.06 0.06 1.00 2 0.08 0.08 1.00 2 0.08 0.08 1.00
Tur4_F10
a
1 0.00 0.00 NA 1 0.00 0.00 NA 1 0.00 0.00 NA 3 0.06 0.09 0.05 2 0.08 0.08 1.00 2 0.25 0.23 1.00
Tur4_E12
a
3 0.75 0.66 0.77 3 0.30 0.59 0.02* 3 0.45 0.53 0.15 4 0.68 0.65 0.85 3 0.67 0.68 0.21 2 0.33 0.39 1.00
Tur4_105
a
1 0.00 0.00 NA 1 0.00 0.00 NA 1 0.00 0.00 NA 4 0.04 0.04 1.00 1 0.00 0.00 NA 2 0.25 0.23 1.00
Tur4_80
a
1 0.00 0.00 NA 2 0.10 0.10 1.00 2 0.05 0.05 1.00 5 0.03 0.08 0* 2 0.08 0.23 0.13 1 0.00 0.00 NA
Tur4_87
a
1 0.00 0.00 NA 1 0.00 0.00 NA 1 0.00 0.00 NA 3 0.03 0.03 1.00 1 0.00 0.00 NA 1 0.00 0.00 NA
Mk6
b
1 0.00 0.00 NA 1 0.00 0.00 NA 1 0.00 0.00 NA 1 0.00 0.00 NA 1 0.00 0.00 NA 2 0.58 0.52 1.00
Mk8
b
3 0.62 0.62 0.73 2 0.60 0.53 1.00 5 0.50 0.45 0.13 4 0.43 0.46 0.03* 4 0.75 0.69 0.45 2 0.42 0.43 1.00
Kw2
c
2 0.75 0.50 0.43 2 0.20 0.50 0.08 5 0.60 0.62 0.92 5 0.55 0.67 0.15 3 0.08 0.70 0.55 1 0.00 0.00 NA
Kw12a
c
1 0.00 0.00 NA 2 0.30 0.39 0.48 2 0.15 0.14 1.00 2 0.46 0.39 0.20 1 0.00 0.00 NA 2 0.08 0.08 1.00
Ev37mn
d
2 0.62 0.46 0.48 2 0.20 0.50 0.08 3 0.25 0.23 1.00 3 0.44 0.43 1.00 4 0.17 0.30 0.09 1 0.00 0.00 NA
TexVet5
e
2 0.12 0.12 1.00 1 0.00 0.00 NA 2 0.05 0.05 1.00 1 0.00 0.00 NA 2 0.08 0.08 1.00 2 0.25 0.23 1.00
Ttr63
f
2 0.12 0.12 1.00 1 0.00 0.00 NA 3 0.35 0.50 0.23 3 0.63 0.51 0.06 2 0.33 0.29 1.00 1 0.00 0.00 NA
Ttr04
f
2 0.50 0.40 1.00 3 0.70 0.65 0.37 4 0.65 0.66 0.37 5 0.78 0.75 0.69 4 0.58 0.47 1.00 3 0.42 0.68 0.28
NA number of alleles, H
O
observed heterozygosity, H
E
expected heterozygosity, PP-value of exact test using Markov chain, NA not available
* Significant deviation from Hardy–Weinberg equilibrium (P\0.05)
a
Nater et al. (2009),
b
Kru
¨tzen et al. (2001),
c
Hoelzel et al. (1998b),
d
Valsecchi and Amos (1996),
e
Rooney et al. (1999),
f
Rosel et al. (2005)
Conserv Genet
123
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