SC/S11/RW26
Effective population size and bottleneck signals in the Atlantic population of the southern
right whale
Larissa Rosa de Oliveira1, 2,*, Paulo Henrique Ott 2,3, Felipe Gobbi Grazziotin4, Bradley White5 and
Sandro Bonatto4.
1
Laboratório de Ecologia de Mamíferos, Universidade do Vale do Rio dos Sinos (UNISINOS), Avenida
Unisinos, 950, São Leopoldo - RS, 93022-000, Brazil
2
Grupo de Estudos de Mamíferos Aquáticos do Rio Grande do Sul (GEMARS), Avenida Tramandai, 976,
Imbé, RS, 95625-000, Brazil
3
Universidade Estadual do Rio Grande do Sul (UERGS), Porto Alegre, RS, Brasil.
4
Laboratório de Biologia Genômica e Molecular da Pontifícia, Universidade Católica do Rio Grande do Sul
(PUCRS), Faculdade de Biociências, Av. Ipiranga, 6681 - Prédio 12A , Porto Alegre-RS- 90619-900-Brazil.
5
Natural Resources DNA Profiling and Forensic Centre, Trent University, Peterborough,
Ontario K9J 7B8, Canada (BNW)
*Correspondence author
e-mail: [email protected]
Key-words: Eubalaena australis, molecular markers, demography, genetic bottleneck
Abstract
The southern right whale (Eubalaena australis) was one of the most intensively hunted whales
th
th
between the 17 and 20 centuries in the Southern Hemisphere. Recent estimates indicate that today
there are around 7,000 whales, representing 5 to 10% of its original population. However, for
evolutionary matters, the effective population size (Ne), not the census number (N) and identification of
genetic bottlenecks, are prime concerns, because they are demographic factors responsible for maintaining the
genetic diversity of the species and its evolutionary potential. In this sense, we present an estimate of effective
population size and its oscillations over time for the population of E. australis that occurs along the Atlantic
coast of South America, based on the analyses of mtDNA and microsatellite loci for the southwestern Atlantic
population. We analyzed 69 sequences of E. australis from mtDNA control region (495bp) and 10
microsatellite loci, collected from specimens of two breeding areas: Southern Brazilian coast n=48, (~27o30´S
- 30o00´S) and Península Valdés, Argentina n=21 (42o30´S; 63o30´W). Both breeding sites represent a
genetically single population in the southwestern Atlantic (FST=0,009 e ST=0,016). The female effective
population size (Nef) for this whole population was estimated using the formula: Nef = θ/2 µg, where µ =
1.75E-8 mutations per site per year, g=18.1years as generation time and θ = genetic diversity (theta),
calculated by a coalescent model by Lamarc and also by Waterson’s method using DNAsp. In order to
calculate population size fluctuation over time using mtDNA data we used the Bayesian Skyline Plot method
implemented in the program Beast v1.3 1.2. We tested the occurrence of a genetic bottleneck in 10
microsatellite loci for three different mutation models (SMM, IAM and TPM) using the program Bottleneck.
The overall effective population sizes (2Nef) estimated by the coalescent model and Waterson’s method were
142,066 (CI: 101,026-211,522) (θLamarc=0.045) and 56,194 individuals (θWaterson=0.0178), respectively. There
was a drastic decline in the effective population size of southern right whales during the Pleistocene around
17,000 years ago based on the analysis of mtDNA. Significant deviations from neutrality-equilibrium were
obtained for the majority of the 10 studied loci in all the evolutionary models tested (IAM = 9, SMM=7 and
TPM=7) suggesting the occurrence of a recent bottleneck event. Our data suggest that the southwestern
Atlantic population had already experienced a loss of genetic diversity due to a significant population decline
before the commercial hunting began in the 17th century.
1
1. Introduction
The southern right whale was one of the most intensively hunted whales between the 17th and
20 centuries in the Southern Hemisphere. Whale population dynamics models and historical
reconstruction estimate that there are now about 7,000 individuals, which may represent 5 to 10%
of its original population size (pre-exploitation abundance) (IWC, 2001). Nevertheless, this method
usually is based on incomplete information about historical catches registered in logbooks
(Tormosov et al., 1998).
The estimation of long-term effective population size with genetic markers is being
evaluated as an alternative to demographic estimation by counting (e.g. Roman & Palumbi, 2003;
Baker & Clapham, 2004). Neutral genetic diversity reflects population size and changes across vast
ecological time because this diversity is directly related to population size for a given mutation rate.
Large, stable populations harbor greater diversity than do small or fluctuating populations and, for a
given population size, genetic markers with high rates of mutational substitution are more variable
than are those with low rates (Avise, 2000).
Roman & Palumbi (2003) estimated the effective population size of three baleen whales
based on the genetic diversity (θ) from mtDNA control region sequences. Although they admitted
the uncertainty of the genetic estimates (nearly 1,000,000 for the three species), the authors
suggested that previous historical reconstructions are biased downward by incomplete catch
records, intentional underreporting of catches and high struck-but-lost rates.
Baker & Clapham (2004) believe that reconciling demographic and genetic approaches to
the reconstruction of whale population dynamics could serve as a useful case study with broad
application to modeling both historical and contemporary population dynamics.
For evolutionary matters, the effective population size (Ne), not the census number (N) and
the identification of genetic bottlenecks (significant declines in the effective population size), are
prime concerns because they are demographic factors responsible for maintaining the genetic
diversity and evolutionary potential of a species (Frankel & Soulé, 1981).
Therefore, we present an estimate of effective population size and its oscillations over time
for the population of E. australis that occurs along the Atlantic coast of South America, based on
genetic diversity.
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2. Materials and methods
Population sampling and molecular methods
We analyzed 69 sequences of E. australis from mtDNA control region (495bp) and 10
microsatellite loci (Table 1) (Ott, 2002), collected from specimens of two breeding areas: Southern
Brazilian coast n=48, (~27o30´S - 30o00´S) and Península Valdés, Argentina n=21 (42o30´S;
63o30´W). According to Ott (2002), both breeding sites represent a genetically single population in
the southwestern Atlantic (FST=0,009 e ST=0,016).
In this sense, based on mtDNA we estimated the female effective population size (Nef) for
this whole population using the formula: Nef = θ/2 µg, where µ = mutational substitution rate per
generation and θ = genetic diversity (theta), see below (Avise et al., 1988). Generation time (g)
estimated for E. australis and used for calculation was 18.1years (Taylor et al., 2007) with a
mutation rate of 1,75E-8 (Roman & Palumbi, 2003).
Population size fluctuation and demographic parameters such as θ were tested using the
MCMC method implemented in the package Lamarc 2.0.2 (Kuhner 2006). The θ parameter was
calculated by a coalescent model by Lamarc and also by Waterson’s method using DNAsp.
2
Estimates of Ne(f) were then converted to population census size (NC) using adjustments assuming
equal sex ratio based in breeding adults (2Nef).
We used the Bayesian Skyline Plot method implemented in the program Beast v1.3 1.2 in
order to calculate population size oscillations over time using mtDNA data (Drummond & Rambaut
2003).
To test the hypothesis that the southwestern Atlantic population of southern right whale had
recently experienced genetic bottlenecks we compared the sample heterozygosity (He) at each of the
10 microsatellite loci to the values expected for a sample of equal size and number of alleles, taken
from a population under neutrality and in-equilibrium conditions (Heq). As shown by Cornuet &
Luikart (1996), samples from populations that recently experienced bottlenecks have He > Heq. In
order to generate the expected heterozygosities under neutrality-equilibrium we used the program
Bottleneck (Piry et al., 1999), which constructs a likelihood distribution of the parameter theta (θ =
4 Ne µ) (where Ne is the effective population size and µ is the mutation rate), conditional on the
observed number of alleles (k) and sample size (n). We tested the bottleneck hypothesis for three
different microsatellite mutation models: the strict one-step stepwise mutation model (SMM, Otha
& Kimura, 1973), the infinite allele model (IAM, Kimura & Crow, 1964) and a two-phase model
(TPM, Di Rienzo et al., 1994).
3. Results
The female effective population size (Nef = θ/2 µg) estimated by the coalescent model and
Waterson’s method were 71,033 (CI: 50.513 - 105761) (θLamarc=0.045) and 28,097 individuals
(θWaterson=0.0178), respectively. In this sense the overall effective population sizes (2Nef) (taking into
account the contribution of males and females) estimated by the coalescent model and Waterson’s
method were 142,066 (CI: 101,026-211,522) (θLamarc=0.045) and 56,194 individuals
(θWaterson=0.0178), respectively.
A significant population size fluctuation was detected by the Bayesian Skyline Plot method
based on the analysis of mtDNA. The scenario presented by the skyline plot showed a drastic
decline in the effective population size of southern right whales sample during the end of
Pleistocene and beginning of Holocene around 22 and 6 thousand years ago, with the start of the
inflection of the median curve at 17,000 years ago (Figure 1).
The results of the test for evidence of genetic bottlenecks are summarized in Table 1.
Significant deviations from neutrality-equilibrium were obtained for the majority of the 10 studied
loci in all the evolutionary models tested (IAM = 9, SMM=7 and TPM=7), suggesting the
occurrence of a recent bottleneck event.
3
Figure 1. Bayesian skyline plot showing the effective population size fluctuation throughout time of
Eubalaena australis from the Atlantic coast of South America (Southern Brazilian coast n=48 and Península
Valdés, Argentina n=21). Solid line: median estimations; grey lines: confidence interval; grey area indicates
the population decline.
Table 1. Results of the genetic diversity (A) and heterozygosity expected under Hardy-Weinberg equilibrium
(Heq) at 10 polymorphic microsatellite loci for the southwestern Atlantic population of southern right whale,
Eubalaena australis. A = number of alleles; He= the sample heterozygosity; Heq = heterozygosity under
neutrality and in-equilibrium conditions; SMM = one-step stepwise mutation model; IAM = the infinite allele
model and TPM= two-phase model.
Locus
G271
GT023
RW26
EV37
TR2G5
RW417
TV20
RW410
TV17
TR3F2
Observed genetic
diversity
n
A
He
138
2
0.184
138
8
0.817
131
11
0.890
138
11
0.864
138
2
0.491
138
14
0.769
136
5
0.555
124
11
0.884
130
10
0.865
134
3
0.532
Under the
IAM
Heq
0.175*
0.643*
0.744*
0.737*
0.182*
0.798
0.490*
0.746*
0.710*
0.312*
Under the
TPM
Heq
0.190
0.726*
0.807*
0.802*
0.198*
0.852
0.573
0.813*
0.785*
0.372*
Under the
SMM
Heq
0.218
0.795*
0.856*
0.855*
0.219*
0.889
0.663
0.857*
0.842*
0.444*
* He > Heq
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4. Discussion
According to the θ method, the estimated effective population size using genetic data for the
southwestern Atlantic population of southern right whales ranged from 56,194 to 142,066
individuals. The coalescent estimation is very close to the probable pre-exploitation estimated
abundance for this species (~110,000 southern right whales) throughout the southern hemisphere
during the 19th century (IWC, 2001), based on the catches reported in the logbooks.
On contrary to our findings, overestimating results were observed by Roman & Palumbi
(2003) for fin and humpback whales, which presented “genetic population estimates” ten- to 20-fold
larger than current demographic estimates based on whaling records (Smith & Reeves, 2003).
Baker & Clapham (2004) emphasized that the overestimated population size could be the
result of the mutation rate used in the equation. According to the Nef formula, lower substitution
rates would result in proportionately higher genetic estimates of effective population size for whales
(Roman & Palumbi, 2003).
Baker & Clapham (2004) strongly recommended the reconciliation between the
demographic and genetic approaches in the reconstruction of whale population dynamics. Authors
suggested a search of a common analytical framework to link demographic and genetic models and
to evaluate uncertainty in key parameters, such as rates of maximum increase, struck-but-loss rates,
mutation rates and gene flow. Moreover, it will be very important to know the exact values of sex
ratio, generation time and the proportion of breeding adults in order to make adjustments while
generating a more realistic population census size (NC) (Nunney, 1993).
The drastic population decline detected in the
mtDNA sequences during the Pleistocene around 17,000 years ago could be related to
events pre-dating the onset of whaling. Similarly, the North Atlantic right whale, Eubalaena
glacialis has low genetic variability that seems to predate the most severe population decline of the
species in the 18th century (Rosenbaum et al., 2000; Waldick et al., 2002). The authors suggested
that the loss of coastal habitat during the most recent glaciation event (18,000 years ago) may have
reduced the population size or, alternatively, a genome-wide selective sweep could have occurred at
some time in the past, thereby reducing variability throughout the nuclear genome (see Amos &
Harwood 1998). However, Waldick et al. (2002) found no evidence of genetic loss in the diversity
of microsatellite loci, like we found for E. australis. These results suggest that the southern right
whale has undergone different demographic events in the southwestern Atlantic. One possible
interpretation for our evidence of deviation from equilibrium in this case is a population bottleneck.
Such a bottleneck could be an indirect result of the synergic effect between the glaciation event and
the impact from whaling in the past. Future work should aim to incorporate additional populations
and increase the number of markers screened to approximately 20 loci (as suggested by Cornuet &
Luikart, 1996), which would substantially increase our power to detect genetic signatures of past
population processes in E. australis.
The identification of recently genetic bottlenecked populations is very important for
threatened species such as the southern right whale because these events can increase demographic
stochasticity, rate of inbreeding, loss of genetic variation, and fixation of deleterious alleles, thereby
reducing the adaptative potential and increasing the probability of population extinction (e.g.
Frankel & Soulé, 1981; Goodnight, 1987; Hedrick & Miller, 1992; Frankham, 1995).
The implications of these findings for the conservation or management plans of southern
right whales are important. Even though some populations have shown strong signs of recent
recovery (e.g. IWC, 2001, Groch et al., 2005), our data suggest that the southwestern Atlantic
population had already experienced a loss of genetic diversity due to a significant population
decline before the commercial hunting began in the 17th century.
5
Acknowledgements
We thank Paulo André Flores, José T. Palazzo and Karina Groch from the Projeto Baleia
Franca, as well as Ney Cantarutti from Instituto Chico Mendes para a Conservação da
Biodiviersidade (Torres) for the logistic support during sampling activities. We also thank
Thales R. Freitas (UFRGS, Brazil) for all support during the molecular studies of the Brazilian
southern right whale population. Financial support during sampling and laboratory activities was
made possible by grants from several funding agencies throughout the years, including:
International Wildlife Coalition, World Wildlife Fund (WWF-Brazil), Yaqu Pacha Foundation,
CAPES (Ministry of Education, Brazil) and CNPq (Ministry of Science and Technology,
Brazil). L.R. Oliveira was supported by CNPq (Proc. 151307/2005-9) and P.H. Ott was
supported by WWF-Brazil (Proc. CSR 140-00) and CNPq (Proc. 144064/1998-7, 200465/20015 and 477611/2004-4). This is GEMARS contribution no. 35.
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