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Sex identification in Gentoo (Pygoscelis papua) and Chinstrap (Pygoscelis
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antarctica) penguins: can flow cytometry be used as a reliable
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identification method?
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João Loureiroa, Daniela Tavaresa, Sónia Ferreirab, José Secoc, Tiago Valentec, Phil
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Trathand, Andrés Barbosae José C. Xavierc,d
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a CFE,
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Coimbra, 3001-401 Coimbra, Portugal
Centre for Functional Ecology, Department of Life Sciences, University of
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b Department
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Mamarrosa, Portugal
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c Institute
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3001-401 Coimbra, Portugal
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d British
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e
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28006 Madrid, Spain
of Health and Education, Institute of Education and Citizenship 3770-033
of Marine Research, Department of Life Sciences, University of Coimbra,
Antarctic Survey, NERC, High Cross, Madingley Road, CB3 0ET, Cambridge, UK
Departamento de Ecología Evolutiva. Museo Nacional de Ciencias Naturales, CSIC,
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Corresponding author:
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João Loureiro
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E-mail: jloureiro@bot.uc.pt
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Telephone no.: +351239855242
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Full postal address: CFE, Centre for Functional Ecology, Department of Life Sciences,
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University of Coimbra, P.O. Box 3046, 3001-401 Coimbra, Portugal
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Abstract
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An important scientific question in ecology is how to differentiate males from females
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that have similar morphology. In penguins, due to their monomorphic plumage, it is
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difficult to determine gender. So far, most approaches to address this problem have
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focused on using sex based discriminant functions combined with DNA based tools as a
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validation method. As discriminant functions can be species and locality specific, in this
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study we explored the feasibility of using flow cytometry to determine the sex of two
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species of penguins, Pygoscelis antarctica and P. papua. Our results for sex assessment
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determined by flow cytometry were compared with those obtained using
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morphological characters (bill length and depth), and DNA based methods (using the
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PL/PR primer pair) were used to validate both approaches. For both species,
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statistically significant differences were observed between male and females, with
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males presenting on average 2.1% more nuclear DNA than females. Flow cytometry
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enabled similar or better rates of correct sex assignment (86.4% and 80.0%) than sex
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discriminant functions (31.8% and 90.0%) for Pygoscelis antarctica and P. papua,
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respectively, and thus may be considered as a promising alternative to the use of
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morphological data for sex identification. Nevertheless, some individuals with
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intermediate genome size values were observed, which increased the difficulty of
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categorically assigning their sex. Therefore, flow cytometry alone cannot be considered
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in all cases, but if used together with DNA-based methods in targeted samples, it can
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be used to efficiently estimate the gender of, at least, these two penguin species, with
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the potential to be used in other species.
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Keywords: flow cytometry; nuclear DNA content; penguins; PL/PR primers; sex
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discriminant functions; sexing
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1. Introduction
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An important question in ecological research is how to differentiate males from
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females that have similar morphology. Within vertebrates, birds are generally difficult
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to sex (Catry et al., 2005; Griffiths et al., 1998). Seabirds are particularly interesting as
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there are numerous species that exhibit very similar morphologies, such as
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albatrosses, petrels and penguins (Knox, 2007; Tickell, 2000). Currently, there are six
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different genera of penguins (Family Spheniscidae) living in the world. Penguins of the
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genus Pygoscelis comprise three species, found mostly in the higher latitudes of the
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sub-Antarctic and the Antarctica (Davis and Renner, 2003). Gentoo Penguins
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(Pygoscelis papua) have the most northerly distribution, occurring on most of the sub-
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Antarctic islands but extending to the Antarctic Peninsula, while Chinstrap Penguins
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(Pygoscelis antarctica) are found almost exclusively at the Antarctic Peninsula, further
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in the south and across the Scotia Arc. At the South Shetland Islands the distribution of
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both species overlap and both species often breed in close proximity (Davis and
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Renner, 2003). Knowledge about the ecology and population dynamics of penguins in
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the Antarctic has grown considerably in the last two decades (e.g. Carravieri et al.,
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2013; Croxall et al., 2002; Dann et al., 1995; Fretwell et al., 2012).
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Sex determination in penguins through visual clues is considered to be difficult
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due to their monomorphic plumage (Polito et al., 2012; Valenzuela-Guerra et al.,
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2013). Still, penguins exhibit a slight dimorphism of size between sexes, with males
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usually having larger body, bill and flipper sizes (Davis and Renner, 2003). This has led
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to the development of discriminant methods based on single or multiple
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morphological characters (e.g. Amat et al., 1993; Renner et al., 1998; Setiawan et al.,
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2004). In the case of the Pygoscelis penguins, discriminant functions have been
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calculated previously, and further validated using DNA-based molecular methods for
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sex determinations (Polito et al., 2012; Valenzuela-Guerra et al., 2013). However, as
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observed for other penguins, a geographic morphological variation is found in
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Pygoscelis penguins, with decreases in size being observed toward southern latitudes.
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Therefore, discriminant sexing functions are only valid for specific species/sub-species
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and localities. This is evident from the study of Valenzuela-Guerra et al. (2013), who
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developed morphological discriminant functions for Gentoo penguins from three
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localities in the South Shetland Islands and the Antarctic Peninsula, and observed
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percentages of correct identification ranging from 83.95% to 93.87% after validation
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with molecular sex identification, as well as, morphological divergence between
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localities.
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DNA-based molecular methods, mostly using universal primers as the P2/P8 pair,
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have been used in the past as a complementary method to determine the sex of
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penguins, and, as referred above, as a validation method of the morphological
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discriminant functions (Griffiths et al., 1998; Polito et al., 2012; Valenzuela-Guerra et
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al., 2013). The P2/P8 primers have been widely used in avian molecular sexing and
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amplify regions of the CHD1 gene found in sex chromosomes (Griffiths et al., 1998).
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More recently, Zhang et al. (2013) developed primers specific for determining the sex
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of penguin species, the PL/PR primer pair. In penguin species this primer combination
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can be used to unequivocally determine the gender of individuals from all species
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tested, whereas using P2/P8 it is not possible to determine the sex in one of the
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species, due to the production of faint PCR bands of similar size of the CHD1Z and
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CHD1W genes.
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In the beginning of the 1990s, an alternative sexing methodology was proposed by
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Nakamura et al. (1990), i.e., the use of flow cytometry to estimate the nuclear DNA
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content of erythrocytes from a small blood sample. This approach is based on the sex-
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chromosome heteromorphism present in birds, the ZW sex-determination system.
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Contrarily to the XY sex-determination system found in humans and most other
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mammals, males are the homogametic sex (ZZ), while females are the heterogametic
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sex (ZW). As the Z chromosome is larger than the W chromosome, males have a larger
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DNA content than females, which considering the precision of flow cytometry in
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distinguishing minute differences in DNA content (e.g. De Vita et al., 1994; Nakamura
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et al., 1990), would make it theoretically possible to distinguish males and females
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based on their genome sizes, only. Indeed, flow cytometry was used with success to
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distinguish the sex of dozens of bird species from a variety of orders (e.g. De Vita et al.,
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1994; Nakamura et al., 1990; Tiersch and Mumme, 1993). The difference in DNA
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content between sexes may vary among bird species (e.g. 0.6% in Tyto alba to 5.8% in
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Neophron pernocterus subsp. ginginianus, De Vita et al., 1994); this difference is the
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main determinant of the success of this technique for sexing purposes. In comparison
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with other methods used to determine the sex of birds, flow cytometry is considered
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to be a rapid, noninvasive and inexpensive approach (considering that a flow
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cytometer is readily available) that only requires microliter volumes obtainable from a
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variety of bird tissues, as long as it is possible to extract any type of nucleated cells.
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Despite its potential, after publication of a number of studies, only a few
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laboratories continued to exploit this technique for determining the gender of bird
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species. Therefore, the main objective of this study was to evaluate the feasibility and
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accuracy of using flow cytometry to determine the sex of penguins, in particular of
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Gentoo and Chinstrap penguins. For that, the results of sex assessment were
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compared with those obtained through the discriminant function of Polito et al. (2012)
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based on bill measurements. Also, DNA based molecular methods were used in a
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subset of samples to validate the flow cytometric results. To our knowledge, this is the
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first study exploring the use of flow cytometry for such purposes in penguins, being of
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particular importance considering the difficulty of determining gender in this family.
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2. Material and Methods
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2.1.
Field sampling
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The Gentoo and Chinstrap penguin blood samples for sex determination were
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collected in December 2011 and January 2012, at Livingston Island (Antarctic
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Peninsula) at Hannah Point (60 37´ W 62 39´ S) and at Hurd Peninsula (60 25´ W 62 43´
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S), respectively. To reduce disturbance at the breeding colony adult penguins were
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selected randomly and captured while traveling between the colony and the sea.
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While all the individuals sampled had adult plumage, this method did not allow us to
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verify their actual age or breeding status. Blood samples were collected with a 25 g
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needle and 1 ml syringe from the brachial vein on the underside of the flipper; this vein
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is located in the brachial groove that can be felt running along the length of the flipper,
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approx. 1.5 cm in from the leading edge. Blood samples were maintained in a -20 °C
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freezer for later analyses (see below). Penguin capture, sample collection and
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subsequent release lasted generally no longer than 10 minutes. Our methods adhered
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to all recommendations advised by the Scientific Committee for Antarctic Research
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(SCAR).
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2.2.
Morphological sexing
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Morphometric measurement is the simplest method available. Bill (culmen) length
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and bill depth (taken through the center of the nostrils) were measured to an accuracy
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of 0.1 mm using vernier callipers. All measurements were conducted by the same
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scientist. Data was used to calculate the sex discrimination functions and the posterior
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probability for a male individual, following the methods of Polito et al. (2012).
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2.3.
Flow cytometric sexing
The genome size of 29 individuals of Pygoscelis antarctica and 28 individuals of
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Pygoscelis papua was estimated using flow cytometry following the method of Tiersch
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and Mumme (1993), with modifications. Male chicken erythrocytes, with known
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nuclear DNA content (2C = 2.33 pg DNA; Galbraith et al., 1983), were used as an
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internal reference standard. This standard was chosen because its genome size was
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close to, but not overlapping with that of the sample species.
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For each measurement, approximately 1-10 µL of blood of the sample species
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were mixed with 2 µL of the internal reference standard and added to 1 mL of WPB
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buffer (0.2 M Tris.HCl, 4 mM MgCl2.6H2O, 1% Triton X-100, 2 mM EDTA Na2.2H2O, 86
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mM NaCl, 10 mM metabisulfite, 1% PVP-10, pH adjusted to 7.5 and stored at 4 oC;
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Loureiro et al., 2007). The suspension was then filtered through a 30 µm nylon filter
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and nuclei were stained with the intercalating DNA dye, propidium iodide (PI) at 50
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μg.mL-1. As PI also intercalates into double-stranded RNA (Doležel et al., 2007), RNase
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at 50 μg.mL-1 was also added to eliminate it.
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Samples were kept at room temperature to incubate for 5 min and were
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subsequently analysed in a Partec CyFlow Space flow cytometer (Partec GmbH, Görlitz,
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Germany) equipped with a 532 nm green solid-state laser, operating at 30 mW. Each
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day, during the analysis of the first sample, the amplifier system was set to a constant
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voltage and gain, with the internal reference standard G1 peak being positioned on
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channel 200. The following samples were measured using the same instrument
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settings.
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Data from each sample was acquired using FloMax software v2.5 (Partec GmbH,
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Münster, Germany) in the form of four graphs: relative fluorescence intensity of PI-
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stained nuclei (FL) histograms (1), FL vs. time cytograms (2), FL vs. side light scatter in
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logarithmic scale cytograms (3) and FL height vs. FL area cytograms (4). In the last
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types of cytograms, regions of interest, comprising mostly the isolated nuclei, were
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created to eliminate undesirable low-channel signals, doublets and other debris.
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Whenever possible, at least 5,000 nuclei in both the sample and standard G1 peaks
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were analysed per sample.
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The quality of the analysis was assessed using the same software, through the
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careful evaluation of peaks symmetry and peaks coefficient of variation (CV, %). Only
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when peaks were symmetrical and CV values were below 5% was the sample
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considered for genome size estimations.
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The genome size in pg (2C) of each individual (GSs) was estimated using the
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following formula: GSs= G1s / G1r × GSr, where G1s and G1r are the mean G1 fluorescence
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of sample and reference nuclei, respectively and GSr refers to the genome size of
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reference nuclei.
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In order to assess the reproducibility of the obtained results, when the quantity of
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blood sample was sufficient, three replicates were performed per individual. Also,
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these measurements were done in three different days to compensate for any
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influence regarding instrumental drifts, as advised by Doležel et al. (2007).
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2.4.
Molecular sexing
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Molecular sexing was performed on a sub-set of individual adult Gentoo (n = 10)
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and Chinstrap (n = 22) penguins. DNA extraction was performed using the Wizard
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Genomic DNA Purification Kit (Promega, USA), according the manufacturer
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instructions. Polymerase Chain Reaction was used to determine the gender of the
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individuals, using a pair of primers PL (5'-CCC AAG GAT GAT AAA TTG TGC-3') / PR (5'-
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CAC TTC CAT TAA AGC TGA TCT GG-3') to amplify CHD1 gene as described by Zhang et
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al. (2013). PCR reactions were performed on a MJMini Thermal Cycler (BioRad, USA)
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with the final volume of 50 µl containing 1x PCR Buffer, 0.5 µM of each primer, 2.5mM
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MgCl2, 0.2 mM dNTPs, 0.25U Taq DNA polymerase and 10-100 ng genomic DNA. All
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PCR reactions were initiated with an initial denaturation (95 °C / 15 min.), followed by
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36 cycles of denaturation (94 °C / 30 sec.), annealing (55 °C/ 90 sec.) and extension (72
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°C / 60 sec.), and ended with a final extension (72 °C / 10 min.). Male and female
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chicken blood were used as positive control.
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2.5.
Statistical analyses
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Differences in genome size between species and sexes were evaluated with a two-
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way ANOVA. When treatments were significantly different, a Tukey multiple
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comparison test was used for pairwise comparison (SigmaPlot for Windows Version
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12.0, Germany).
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3. Results
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3.1.
Morphological sexing
The results of morphometric analyses, in particular the measurements of bill
length and bill depth, are given in Table S1.
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Using the discrimination functions of Polito et al. (2012), for Chinstrap penguins, a
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total of 75.9% (22 out of 29) of the individuals were assigned as females, and 24.1% as
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males (7 out of 29). In Gentoo penguins, a total of 57.1% were assigned as females (16
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out of 28), whereas 42.9% were assigned as females (12 out of 28).
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3.2.
Flow cytometry sexing
With flow cytometry, it was possible to obtain PI fluorescence histograms of good
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to excellent quality (Fig. 1), with two distinct G1 peaks of sample and standard nuclei.
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Indeed, the mean CV values of sample and standard G1 peaks were of 3.08% (ranging
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from 1.71% to 4.89%) and 2.73% (ranging from 1.57% to 4.28%), respectively. Also,
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regardless of the species, the measurements were reproducible, with the CV of
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genome size measurements, being always below 2.5% (0.88% on average), which
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sustains the good reproducibility of the flow cytometric method.
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No significant differences in genome size were observed between both species (F
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= 0.0885, P = 0.767; Table 1). Genome size values ranged from 2.85 to 3.12 pg/2C, and
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after comparison with the results of molecular sexing, two genome size classes were
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established, 2.85 to 2.93 pg/2C assumed as female individuals, and 2.96 to 3.12 pg/2C
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considered to correspond to male individuals. There were some cases where
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intermediate values of 2.94 and 2.95 pg/2C were observed. Once again, using the
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results of molecular sexing it was decided to consider 2.94 pg/2C has the highest
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genome size value for female individuals, and 2.95 pg/2C has the lowest genome size
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value for male individuals. Following this gender classification strategy, in Pygoscelis
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papua, males (2.98 ± 0.04 pg/2C) had on average 2.3% more nuclear DNA than females
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(2.91 ± 0.03 pg/2C), while this difference was slightly higher in Pygoscelis antarctica,
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reaching on average 2.6% (males: 2.99 ± 0.04 pg/2C; females: 2.91 ± 0.03 pg/2C; Table
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1). These genome size differences were statistically significant at P < 0.001 regardless
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of the species (F = 49.78, P < 0.001). Also, no statistically significant interaction
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between sex and species (F = 0.168, P = 0.684) was observed.
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If we consider only the subset of samples where both DNA-based methods and
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flow cytometry were used, the genome size difference between males and females
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was lower in both species (1.0% for P. antarctica and 1.4% for P. papua).
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3.3.
Molecular sexing
The results of molecular sexing are given in Table 2. Using the PL/PR primers, it
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was possible to unequivocally determine the sex of the subset of samples selected for
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molecular sexing. Males due to their homogametic karyotype (ZZ), were characterized
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by a single band on the agarose gel corresponding to a single amplified fragment with
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approximately 276 bp, the CHD1Z gene that is amplified by the PL and PR primer pair.
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In the case of females, the heterogametic karyotype (ZW) led to the presence of two
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bands on the gel at approximately 276 bp and 294 bp, corresponding to the CHD1Z and
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CHD1W genes, respectively.
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In Chinstrap penguins, the majority of the 22 individuals tested were males
(81.8%), with only 18.2% of the individuals being identified as females. In Gentoo
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penguins, 60% of the 10 individuals tested were identified as males, whereas the
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remainder were females (40%).
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3.4.
Comparison between the sexing methods
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When compared with the results of molecular sexing, only in 50.0% of all
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individuals (both species) did the sex discriminant functions of Polito et al. (2012)
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enable the correct assignment of sex (Table 2). Even so, these published discriminant
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functions did a much better job of correctly assigning sex in P. papua (90.0% correct)
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relative to P. antarctica (31.8% correct) based on their bill measurements. The poor
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success of discriminant functions in P. antarctica was due to the high number of males
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incorrectly classified as females (Table 2). In contrast, the determination of the sex by
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means of flow cytometry was equivalent to that of molecular sexing in 84.4% of the
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cases (P. antarctica = 86.4%; P. papua = 80.0%; Table 2). When flow cytometric sexing
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was compared with morphological sexing across all samples, similar sex assignment
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was observed in 52.6% of the cases (P. antarctica = 44.8 %; P. papua = 60.7%). The
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agreement between these two methods was slightly improved (53.1%) when
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examining only those individuals that were sub-sampled for molecular sexing (P.
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antarctica = 45.5 %; P. papua = 70.0%).
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Following morphological sexing, males presented a larger bill length (6.3%
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larger in P. papua and 6.9% in P. antarctica) and bill depth (10.2% larger in P. papua
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and 5.7% in P. antarctica) than females (Table 3). Despite the fact that the sex of only
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half (approximately) of the individuals was correctly assigned, a similar same tendency
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was observed after molecular and flow cytometric sexing, with males presenting 7 to
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9% larger bill lengths and 6 to 7% larger bill depths, irrespective of the technique. The
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only exceptions were observed for P. antarctica after molecular sexing, as males
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presented similar values of bill length and smaller bill depth than females. Also, in P.
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papua, after flow cytometric sexing, males were only 1.6% larger than females for bill
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length and 0.6% larger for bill depth.
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4. Discussion
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For many decades, there has been a strong scientific interest in developing
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effective new tools for sex identification in bird species, especially when sexing
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individuals in populations of monomorphic species is important in behavioural and
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ecological studies (e.g. Catry et al., 2005; Cucco et al., 1999; Phillips et al., 2004; Xavier
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et al., 2003). In the beginning of the 1990s, flow cytometry started to be explored in
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other applications besides its use in clinical studies and immunology (Shapiro, 2007).
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One of the applications that was envisaged was the use of flow cytometry to
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determine the gender of birds (Nakamura et al., 1990). The development of a rapid,
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cheap and effective method to determine the sex of bird species with monomorphic
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plumage was particularly important, opening the possibility to estimate the sex in the
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early stages of bird development, when it is in many cases difficult to determine the
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sex.
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Indeed, flow cytometry was used for gender estimation of numerous bird species
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(Cavallo et al., 1997; Cucco et al., 1999; Nakamura et al., 1990), its success being
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dependent on how different was the genome size between males and females (0.4-7%
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difference in genome size in birds), as a result of the differences in the size between
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the W and Z chromosomes. Considering the advantages of flow cytometry it was
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surprising that only a few papers have subsequently been published until now (e.g.
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Cucco et al., 1999; De Vita et al., 1994; Nakamura et al., 1990; Tiersch et al., 1991;
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Underwood et al., 2002). Despite the successful use of flow cytometry for bird sexing,
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a possible explanation for the lack of more studies may have been the development of
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DNA-based methods for the determination of the sex of an individual (reviewed in
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Ellegren and Sheldon 1997a). In this review, the authors were highly critic of the use of
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flow cytometry for such purposes, and highlighted the fact that DNA-based methods
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related with the CHD genes were the best solution (Ellegren and Sheldon, 1997a). Such
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criticism led to few future applications of flow cytometry in bird sexing (Ellegren and
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Sheldon, 1997b; Redelman et al., 1997). Indeed, to our knowledge, the last known
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paper where flow cytometry was used for estimating the sex of a bird species is the
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work of Underwood et al. (2002).
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Our study is the first to use this technique applied to sex identification in penguins.
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Previously, some studies have attempted to develop reliable sex discriminant functions
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based on morphological data (Amat et al., 1993; Polito et al., 2012; Valenzuela-Guerra
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et al., 2013). Indeed, DNA-based molecular techniques are considered to be more
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reliable than morphological data (Hart et al., 2009), yet the discriminant function
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approach is considered a quick, minimally invasive and cost-effective method of sex
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classification (Dechaume-Moncharmont et al., 2011). Polito et al. (2012) developed
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discriminant functions based on bill length and bill depth for three penguin species
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collected at the Admiralty Bay, King George Island, Antarctica, and obtained a success
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rate ranging from 83.2% in Gentoo penguins to 96.7% in Chinstrap penguins. We found
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that applying these same discriminant functions to our dataset resulted in differing
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levels of success between species. Gentoo penguins had a success rate of 90.0% while
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successful sex assignment in chinstrap penguins was only 31.8%. The lower level of
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success for discriminant functions in chinstrap penguins may be due in part to
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morphological variation between King George Island and Livingston Island as has been
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found in previous studies of Pygoscelis penguins (Polito et al., 2012; Valenzuela-Guerra
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et al., 2013). If this was the case, we may have achieved better success for both species
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if we were able to calculate discriminant function equations based on data obtained
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from penguins of Livingston Island. Unfortunately, the generally low sample size of
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individuals sub-sampled for molecular sexing and especially the lack of many female
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chinstrap penguins prohibited our ability to develop robust, location-specific
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discriminant function. Another possible explanation for the poor success of
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discriminant functions in chinstrap penguins is variable age structure in our random
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sample of adults. Mínguez et al. (2001) found that first-time breeding adult Chinstrap
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penguins have smaller bills than older, more experienced breeders. We could not
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confirm the breeding status of the adults sampled in our study and thus the inclusion
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of young, non or first-time breeding males with smaller bills may have led to their
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misclassification as females using the discriminant function of Polito et al. (2012).
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In order to explore the possibility of using flow cytometric methods for sexing
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penguins, the genome size of all individuals was estimated and the results were
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compared with sexing obtained using DNA-based methods. Our results show that in
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approximately 85% of the cases, the determination of the sex by means of flow
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cytometry was equivalent to that of molecular sexing. Such a comparison enabled us
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to observe two clearly distinct genome size classes, with males presenting the highest
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values (2.99 pg/2C on average) and females the lowest ones (2.91 pg/2C).
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Furthermore, our results were highly reproducible and showed very low coefficients of
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variation, and no difficulties were observed when individuals were at either end of the
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genome size scale. Still, in several individuals, due to the low genome size difference
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between male and female individuals (only 2%, approximately), intermediate values
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(2.94-2.95 pg/2C) were observed, which hindered a total certainty of sex assignment in
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these cases. The occurrence of a range of DNA values for each gender has been
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detected before and may be attributed to chromosomal polymorphisms and repeated
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DNA sequences (Tiersch et al., 1991) or to subtle differences in the procedures and
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tests of individuals (Cucco et al., 1999).
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When compared with DNA-based methods, both techniques require only a few
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microliters of whole blood, but flow cytometry is undoubtedly a less laborious and
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time-consuming technique (the analysis of each sample can be completed in 5
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minutes) than the PCR-base procedures. Therefore, in case a flow cytometer is readily
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available, flow cytometry can be regarded as a much cheaper approach, considering
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the low volumes of nuclear isolation buffers (that can be prepared by hand) and
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fluorochrome that are needed to prepare each sample. The only theoretical limitation
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of flow cytometry is related to those cases where the genome size difference between
375
males and females is not high enough to be resolved with certainty (e.g. De Vita et al.,
376
1994), or in situations where there is a continuous range of DNA values (e.g.
377
Underwood et al., 2002), as was the case of the two penguin species studied here. In
378
both these situations, the accuracy of the genome size estimations is much more
379
dependent on the quality of the samples and of the analyses. As PCR is not constrained
380
by these limitations so can be used preferentially to flow cytometry in those cases,
19
381
and, as stated above, has been the preferred method used by ornithologists (e.g.
382
Griffiths et al., 1998; Martín et al., 2000; Zhang et al., 2013).
383
Due to these drawbacks, flow cytometry cannot be considered in all cases as a
384
substitute for other sex identification tools, especially for sex-specific DNA probes. Still,
385
in the particular case of Gentoo and Chinstrap penguins, our study shows that flow
386
cytometry can be considered an effective and very rapid alternative to morphological
387
sexing, being independent of the geographical origin of the sampled individuals or of
388
age structure, as long as DNA-based methods are used to confirm the sex of those
389
samples that present intermediate values of genome size. Therefore, with the current
390
study it is shown that flow cytometry together with DNA-based methods in some
391
targeted samples can be used effectively in the estimation of the gender of two
392
penguin species, with the potential to be explored further in other penguin species.
393
394
395
Acknowledgements
This research was supported by the Ministry of Science and Higher Education,
396
Portugal (Fundação para a Ciência e a Tecnologia), the British Antarctic Survey and is
397
under the research programs CEPH, SCAR AnT-ERA, and ICED. M. Polito provided
398
helpful comments and revisions to an earlier version of this manuscript. Procedures
399
used in this study comply with the current laws for working in Antarctica, with the
400
permission to work in the study area and for penguin handling was granted by the
401
Spanish Polar Committee. This paper represents a contribution to the British Antarctic
402
Survey Ecosystems Programme and to the Portuguese Polar Programme PROPOLAR.
403
20
404
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485
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486
24
487
Figure captions
488
Figure 1: Histograms of relative nuclear DNA content of domestic chicken red blood cells, as an
489
internal reference standard (peak 1) and Pygoscelis antarctica erythrocytes (peak 2) from: A)
490
female individual (ID 6); B) male individual (ID 17). In the inset table, information about the
491
mean fluorescence of both sample and standard G1 peaks is given, as well as, the DNA index
492
(DI), genome size (pg/2C) and CV value (%).
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
25
509
510
26
511
Table 1: Genome size estimations of male and female individuals of Pygoscelis antarctica and
512
P. papua. The results are presented as mean and standard deviation of the mean (SD). Also, for
513
each species, the genome size difference between male and female estimates are provided. In
514
brackets is given the mean genome size of each sex, if only the samples analysed using
515
molecular sexing are considered. Different letters reveal statistically significant differences at P
516
< 0.05 after Tukey test.
Genome size (pg/2C)
Genome size (pg/2C) of
Genome size
Species
517
518
of males (mean ± SD) females (mean ± SD)
difference (%)
Pygoscelis antarctica
2.99 ± 0.04a (2.98)
2.91 ± 0.03b (2.95)
2.6
Pygoscelis papua
2.98 ± 0.04a (2.97)
2.91 ± 0.03b (2.93)
2.3
27
519
Table 2: Genome size estimations of Pygoscelis antarctica and P. papua individuals, and
520
comparison between the three methods used to discriminate the sex of each individual. The
521
results are given as mean, standard deviation of the mean (SD), coefficient of variation of the
522
mean (CV, %) and number of replicates (n).
Genome size (pg/2C)
FCM
Morphological
Molecular
Species/Individual
Mean
SD
CV (%)
n
sexing
sexing
sexing
P. antarctica 1
2.98
0.03
1.03
3
M
F
M
P. antarctica 2
2.96
0.01
0.34
2
M
F
-
P. antarctica 3
2.87
0.02
0.79
2
F
F
M
P. antarctica 4
2.91
0.06
2.16
2
F
F
-
P. antarctica 5
2.91
0.04
1.28
3
F
F
-
P. antarctica 6
2.94
0.02
0.80
3
F
F
-
P. antarctica 7
3.02
0.03
0.85
3
M
F
-
P. antarctica 8
2.96
0.02
0.69
3
M
F
M
P. antarctica 9
3.06
0.07
2.13
3
M
F
-
P. antarctica 10
2.96
0.01
0.43
3
M
F
M
P. antarctica 11
2.96
0.03
1.02
3
M
F
M
P. antarctica 12
2.96
0.02
0.79
3
M
M
M
P. antarctica 13
3.02
0.00
0.14
2
M
F
M
P. antarctica 14
2.99
0.06
2.03
3
M
F
M
P. antarctica 15
3.02
0.07
2.41
2
M
M
M
P. antarctica 16
2.97
0.05
1.53
3
M
M
M
P. antarctica 17
2.98
0.01
0.28
3
M
F
M
P. antarctica 18
2.91
0.01
0.44
3
F
F
F
P. antarctica 19
3.00
0.03
0.88
3
M
F
-
28
P. antarctica 20
3.00
0.02
0.82
3
M
M
M
P. antarctica 21
2.99
0.03
0.98
3
M
M
M
P. antarctica 22
3.12
0.00
0.15
2
M
F
M
P. antarctica 23
3.02
0.02
0.58
3
M
F
M
P. antarctica 24
2.95
-
-
1
M
F
M
P. antarctica 25
2.94
0.00
0.09
3
F
F
F
P. antarctica 26
2.98
0.02
0.53
3
M
M
F
P. antarctica 27
2.97
0.01
0.27
3
M
M
F
P. antarctica 28
3.01
0.01
0.37
3
M
F
M
P. antarctica 29
2.95
-
-
1
M
F
M
P. papua 1
2.97
0.04
1.25
3
M
M
M
P. papua 2
3.07
0.01
0.35
2
M
M
-
P. papua 3
2.91
0.04
1.29
3
F
F
-
P. papua 4
2.96
0.05
1.79
3
M
M
M
P. papua 5
3.04
0.05
1.76
3
M
M
-
P. papua 6
2.95
0.00
0.12
3
M
F
M
P. papua 7
2.97
0.02
0.70
3
M
M
M
P. papua 8
2.95
0.01
0.49
3
M
F
-
P. papua 9
3.01
-
-
1
M
M
M
P. papua 10
2.93
0.03
1.08
3
F
F
F
P. papua 11
2.95
-
-
1
M
M
-
P. papua 12
2.91
0.00
0.14
3
F
F
F
P. papua 13
2.94
0.01
0.48
3
F
M
M
P. papua 14
2.94
0.02
0.64
3
F
F
-
P. papua 15
2.91
0.03
1.04
2
F
M
-
P. papua 16
3.03
-
-
1
M
F
F
29
523
524
525
P. papua 17
2.97
0.01
0.37
2
M
F
-
P. papua 18
2.93
0.06
2.09
2
F
F
-
P. papua 19
2.96
-
-
1
M
F
-
P. papua 20
3.01
-
-
1
M
F
-
P. papua 21
3.01
0.01
0.45
2
M
F
-
P. papua 22
2.92
0.01
0.33
3
F
F
-
P. papua 23
2.93
0.02
0.82
2
F
F
-
P. papua 24
2.85
0.04
1.27
2
F
F
F
P. papua 25
2.89
0.02
0.60
2
F
M
-
P. papua 26
2.97
0.05
1.60
2
M
M
-
P. papua 27
2.96
0.02
0.71
3
M
F
-
P. papua 28
2.98
0.02
0.72
2
M
M
-
30
526
Table 3: Measurements of bill length and bill depth of Pygoscelis antarctica and P. papua
527
according with the sex determination following each method. Values are given as mean and
528
standard deviation of the mean.
Species
Sex
529
530
Morphological sexing
531
Pygoscelis antarctica
Bill length
Bill depth
Mean
SD
Mean
SD
F
47.1
2.6
17.1
1.4
M
50.3
2.1
18.1
1.2
F
45.7
1.6
15.2
0.8
M
48.6
1.4
16.7
0.5
F
45.4
0.8
16.5
1.2
M
48.5
2.8
17.6
1.4
F
46.5
1.8
15.8
0.7
M
47.3
2.2
15.9
1.3
F
48.1
3.3
18.2
0.6
M
48.2
2.8
17.6
1.5
F
44.8
1.2
15.3
1.2
M
48.7
1.0
16.4
0.6
Pygoscelis papua
Flow cytometric sexing
Pygoscelis antarctica
Pygoscelis papua
Molecular sexing
Pygoscelis antarctica
Pygoscelis papua
31
532
533
32
534
Table S1: Morphological sexing of Pygoscelis antarctica and P. papua individuals based on
535
Polito et al. (2012). Measurements of bill (culmen) length and depth were used for calculating
536
the discriminant factor, which was further used to calculated the posterior probability for a
537
male individual (> 50%)
Posterior
Discriminant
Species/Individual
Bill length
Bill depth
Morphological
probability
factor
sexing
for male (%)
P. antarctica 1
43.5
15.5
18.281
0.0
F
P. antarctica 2
48.2
16.0
12.091
0.0
F
P. antarctica 3
45.3
15.3
17.520
0.0
F
P. antarctica 4
44.5
16.8
12.059
0.0
F
P. antarctica 5
46.9
16.3
12.002
0.0
F
P. antarctica 6
45.0
15.0
19.017
0.0
F
P. antarctica 7
43.0
15.4
19.132
0.0
F
P. antarctica 8
47.5
16.5
10.652
0.0
F
P. antarctica 9
49.3
17.0
7.013
0.1
F
P. antarctica 10
47.1
17.4
7.305
0.1
F
P. antarctica 11
49.8
17.9
2.875
5.3
F
P. antarctica 12
47.7
19.6
-2.264
90.6
M
P. antarctica 13
48.0
15.7
13.500
0.0
F
P. antarctica 14
45.8
16.7
11.326
0.0
F
P. antarctica 15
50.2
19.2
-2.820
94.4
M
P. antarctica 16
52.2
18.1
-0.059
51.5
M
P. antarctica 17
49.1
17.9
3.491
3.0
F
P. antarctica 18
45.8
17.6
7.627
0.0
F
33
P. antarctica 19
52.0
18.0
0.528
37.1
F
P. antarctica 20
49.4
19.6
-3.760
97.7
M
P. antarctica 21
54.0
19.5
-7.396
99.9
M
P. antarctica 22
49.0
18.6
0.702
33.1
F
P. antarctica 23
48.7
16.4
10.007
0.0
F
P. antarctica 24
45.9
18.3
4.662
0.9
F
P. antarctica 25
44.9
18.1
6.364
0.2
F
P. antarctica 26
51.8
18.2
-0.118
53.0
M
P. antarctica 27
49.8
19.0
-1.646
83.8
M
P. antarctica 28
50.3
17.9
2.435
8.1
F
P. antarctica 29
43.4
15.9
16.725
0.0
F
P. papua 1
49.1
16.8
-1.967
87.7
M
P. papua 2
48.1
17.0
-1.870
86.7
M
P. papua 3
48.1
14.7
2.483
7.7
F
P. papua 4
49.7
16.2
-1.117
75.4
M
P. papua 5
48.3
17.5
-2.912
94.8
M
P. papua 6
47.5
15.5
1.254
22.2
F
P. papua 7
47.8
16.4
-0.592
64.4
M
P. papua 8
44.2
15.6
2.635
6.7
F
P. papua 9
49.9
17.2
-3.105
95.7
M
P. papua 10
46.6
16.3
0.168
45.8
F
P. papua 11
46.5
17.3
-1.677
84.3
M
P. papua 12
44.2
16.0
1.878
13.3
F
P. papua 13
48.0
16.1
-0.119
53.0
M
P. papua 14
43.6
16.0
2.163
10.3
F
P. papua 15
48.3
16.5
-1.019
73.5
M
34
538
P. papua 16
44.3
13.7
6.184
0.2
F
P. papua 17
45.7
14.2
4.571
1.0
F
P. papua 18
47.3
14.5
3.242
3.8
F
P. papua 19
44.1
15.5
2.872
5.4
F
P. papua 20
44.3
13.8
5.995
0.2
F
P. papua 21
47.2
14.5
3.290
3.6
F
P. papua 22
46.7
16.1
0.499
37.8
F
P. papua 23
47.5
15.9
0.497
37.8
F
P. papua 24
43.9
15.3
3.346
3.4
F
P. papua 25
47.7
16.2
-0.166
54.1
M
P. papua 26
51.8
16.9
-3.441
96.9
M
P. papua 27
46.7
15.3
2.014
11.8
F
P. papua 28
48.3
16.6
-1.208
77.0
M