Cross-species screening of microsatellite markers for ... · Cross-species screening of...

Cross-species screening of microsatellitemarkers for individual identification ofsnow petrel Pagodroma nivea andWilsons storm petrel Oceanites oceanicusin AntarcticaAnant Pande1,*, Nidhi Rawat2,*, Kuppusamy Sivakumar1,Sambandam Sathyakumar1, Vinod B. Mathur3 and Samrat Mondol2

1 Endangered Species Management, Wildlife Institute of India, Dehradun, Uttarakhand, India2Animal Ecology and Conservation Biology, Wildlife Institute of India, Dehradun, Uttarakhand,India

3 Wildlife Institute of India, Dehradun, Uttarakhand, India* These authors contributed equally to this work.

ABSTRACTSeabirds are important indicators of marine ecosystem health. Species within theorder Procellariiformes are the most abundant seabird species group distributed fromwarm tropical to cold temperate regions including Antarctica. There is a paucityof information on basic biology of the pelagic seabird species nesting on the Antarcticcontinents, and long-term studies are required to gather data on their populationdemography, genetics and other ecological parameters. Under the Biology andEnvironmental Sciences component of the Indian Antarctic programme, long-termmonitoring of Antarctic biodiversity is being conducted. In this paper, we describeresults of cross-species screening of a panel of 12 and 10 microsatellite markers in tworelatively little studied seabird species in Antarctica, the snow petrel Pagodromanivea and the Wilsons storm petrel Oceanites oceanicus, respectively. These locishowed high amplification success and moderate levels of polymorphism in snowpetrel (mean no. of alleles 7.08 3.01 and mean observed heterozygosity 0.35 0.23),but low polymorphism inWilsons storm petrel (mean no. of alleles 3.9 1.3 andmeanobserved heterozygosity 0.28 0.18). The results demonstrate that these panels canunambiguously identify individuals of both species (cumulative PIDsibs for snow petrelis 3.7 10-03 and Wilsons storm petrel is 1.9 10-02) from field-collected samples.This work forms a baseline for undertaking long-term genetic research of theseAntarctic seabird species and provides critical insights into their population genetics.

Subjects Conservation Biology, GeneticsKeywords Antarctic seabirds, Genetic monitoring, Procellariiformes, Genetic diversity

INTRODUCTIONAs top predators, seabirds maintain the structure of marine food webs, regulate islandand marine ecosystem processes and act as indicators of marine ecosystem health(Lascelles et al., 2012; Paleczny et al., 2015). Their ability to fly over large distances,

How to cite this article Pande et al. (2018), Cross-species screening of microsatellite markers for individual identification of snow petrelPagodroma nivea and Wilsons storm petrel Oceanites oceanicus in Antarctica. PeerJ 6:e5243; DOI 10.7717/peerj.5243

Submitted 8 January 2018Accepted 26 June 2018Published 20 July 2018

Corresponding authorSamrat Mondol, [email protected]

Academic editorAntonio Amorim

Additional Information andDeclarations can be found onpage 8

DOI 10.7717/peerj.5243

Copyright2018 Pande et al.

Distributed underCreative Commons CC-BY 4.0

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their extreme life history strategies (monogamy, slow reproduction, late sexual maturity),natal philopatry, high visibility and dependence on land for breeding make it essential toconduct long-term population level studies (Piatt, Sydeman & Wiese, 2007) for betterunderstanding of their biology. Recent studies focusing on seabird population monitoringhave highlighted the threatened status of seabirds across the globe (Croxall et al., 2012),especially in the Southern Ocean where seabird populations have declined substantiallyover last few decades (Paleczny et al., 2015). This has led to interdisciplinary approaches tounderstand seabird population dynamics in order to aid their conservation andmanagement across their distribution range (Croxall et al., 2012; Taylor & Friesen, 2012).

Seabirds within the order Procellariiformes comprising petrels, shearwaters, albatrosses,storm petrels, and diving petrels represent one of the most widely distributed andabundant avifauna (Warham, 1996). Despite their broad distribution and large populationsizes, long-term ecological and genetic data exists for few of these species across theglobe. In addition to several ecological studies on Procellariiformes (Croxall et al., 2012),some recent studies have used genetic data to address important biological parameterssuch as relatedness, population structure, past population demography (e.g. see Gmez-Daz, Gonzlez-Sols & Peinado, 2009 for Corys shearwater; Welch et al., 2012 forHawaiian petrel) for species distributed in tropical and Arctic marine ecosystems.Research on the biology of Procellariiformes is relatively limited in the SouthernOcean ecosystem, especially in Antarctica because of its remoteness and associatedlogistical difficulties. Despite site-specific monitoring of some Procellariiformes on sub-Antarctic islands (e.g. Brown et al., 2015 for giant petrels; Quillfeldt et al., 2017 forAntarctic prion, thin-billed prion and blue petrel) and the Antarctic coast (e.g. Barbraud& Weimerskirch, 2001 for snow petrel; Barbraud & Weimerskirch, 2006 for multiplespecies; Techow et al., 2010 for giant petrels), long-term ecological as well as geneticresearch is sparse. Nunn & Stanley (1998) reported the phylogenetic relationshipsamong Procellariiformes using a neighbour-joining approach, but within each family,detailed population genetic information is lacking. Prior studies have used RestrictionFragment Length Polymorphisms and allozymes to investigate genetic variation andextra-pair paternity in snow petrel as well as some other Procellariiformes (Jouventin &Viot, 1985; Viot, Jouventin & Bried, 1993; Lorensten et al., 2000, Quillfeldt et al., 2001)in Antarctica.

As part of the Biology and Environmental Sciences component of the Indian AntarcticProgram, we conducted comprehensive ecological surveys between 2009 and 2016 tounderstand seabird and marine mammal ecology around the Indian Antarctic researchstations (Pande et al., 2017). Currently, this programme is focused on generating baselinegenetic data of breeding seabird species found around Indian area of operations inAntarctica, especially on snow petrel Pagodroma nivea and Wilsons storm petrelOceanites oceanicus. The snow petrel is endemic to Antarctica and Southern Oceanwith breeding distribution along Antarctic coast including some inland mountains andfew sub-Antarctic islands (Croxall et al., 1995). On the other hand, the Wilsons stormpetrel has a much wider breeding distribution from Cape Horn to the Kerguelen Islandsand coastal Antarctica and migrates to the mid-latitudes of the north Atlantic, north

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Indian and Pacific Oceans during non-breeding period (BirdLife International, 2017).Effective monitoring of these species in the Indian Antarctic sector will require systematicinformation on their distribution, current population status and genetic parameters. In thispaper, we report results from cross-species screening of microsatellite markers forindividual identification of snow petrel and Wilsons storm petrel in Antarctica. Thesetested microsatellite panels will provide valuable tools for estimating levels of geneticvariation, relatedness, and genetic signals of population demography, in both speciesacross their ranges.

METHODSStudy areaWe carried out sampling at Larsemann Hills, Prydz Bay and Schirmacher Oasis, CentralDronning Maud Land (Fig. 1); close to permanent Indian research stations in AntarcticaBharati (Larsemann Hills) and Maitri (Schirmacher Oasis). Distance between these twostudy areas is about 2,500 km. Larsemann Hills (6920S6930S; 7555E7630E), are agroup of islands in Prydz Bay located on the Ingrid Christensen Coast, Princess ElizabethLand of east Antarctica. This island group comprises of variously sized islands andpeninsulas, located halfway between the eastern extremity of the Amery Ice Shelf andthe southern boundary of the Vestfold Hills. Schirmacher Oasis, Central Dronning MaudLand (70447046S and 11221154E) is situated on the Princess Astrid coast about120 km from the Fimbul ice shelf. Four species of seabirds (Adelie penguin Pygoscelisadeliae, southpolar skua Stercorarius maccormickii, snow petrel andWilsons storm petrel)breed in the ice-free areas of Larsemann Hills, whereas only the south polar skua breedsat Schirmacher Oasis (Pande et al., 2017).

Field samplingWe conducted sampling for this study as part of the Antarctic Wildlife MonitoringProgramme under the Indian Scientific Expedition to Antarctica (Expedition nos. 33,34, and 35) during the austral summers (NovemberMarch) of 201314, 201415, and201516. We adopted an opportunistic genetic sampling approach under the seabirdnest monitoring protocol (see Pande et al., 2017) for snow petrel sample collection.First, we selected previously marked nest sites with breeding snow petrel individuals forgenetic sampling. Subsequently, we conducted both non-destructive (buccal swabs andblood smears) and non-invasive (hatched eggshells and abandoned eggs) sampling tocollect biological materials from the monitored nesting sites. During non-destructivesampling of snow petrel individuals, we carefully hand-captured birds at their nest cavitiesand collected buccal swabs or blood samples. We collected blood samples from birdsbrachial vein using 0.1 ml sterilized syringe needles and stored in EDTA vacutainer tubes.We collected buccal epithelial tissue by gently rotating a sterilized cotton-tipped swabagainst the inner cheek of the bird (Handel et al., 2006). All individuals were releasedwithin 60 s of capture. Wherever available, we also collected hatched eggshells, shed adultfeathers and abandoned eggs from the nests. In addition, we also conducted opportunisticmuscle tissue sampling of snow petrel carcasses from wherever they were found. These



carcasses were mostly from birds predated by south polar skua or found naturally deaddue to other unidentified causes. We did not find any nesting sites of snow petrels atSchirmacher Oasis during our field surveys, and thus opportunistic sampling of carcassesfor muscle tissue was conducted.

Similarly, we collected Wilsons storm petrel samples from monitored nesting sites atLarsemann Hills. All genetic samples of Wilsons storm petrel were collectedopportunistically through carcass muscle tissue collection as capturing them was notpossible due to their narrow nest cavities. No Wilsons storm petrel samples were collectedfrom Schirmacher Oasis. We stored the samples collected at field sites at -20 C atrespective Indian Antarctic research stations before being brought to Wildlife Institute ofIndia, Dehradun for further laboratory analysis. The details of sampling locations areprovided in File S1.

Primer selectionAs there are no species-specific microsatellite markers published for snow petrel, wescreened a panel of cross-species markers for individual identification of snow petrels.We selected 15 microsatellite markers developed for Hawaiian petrel Pterodromasandwichensis (Nine markers, Welch & Fleischer, 2011) and white-chinned petrelProcellaria aequinoctialis (Six markers, Techow & ORyan, 2004). These markers wereselected based on their polymorphic information content (number of alleles as well as

Figure 1 Seabird sampling locations in Antarctica. (A) Schirmacher oasis, site of Maitri station(B) Larsemann hills, site of Bharati station (Photo Credit: Anant Pande).

Full-size DOI: 10.7717/peerj.5243/fig-1


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expected heterozygosity) in the aforementioned species. We also tested this panel of15 microsatellite loci for individual identification of Wilsons Storm Petrel samples.

DNA extraction and primer standardizationWe used muscle tissue samples of snow petrel and Wilsons storm petrel for initialstandardization and validation of microsatellite panel. Genomic DNA was extracted induplicate from all tissue samples using commercially available DNeasy Tissue kit (QiagenInc., Valencia, CA, USA) using a modified approach. In brief, all samples were maceratedwith sterile blades independently, followed by overnight complete tissue digestion with25 mL proteinase-K. Post-digestion, extraction was performed using Qiagen animal tissuespin column protocol. DNA was eluted twice with 100 mL of 1 TE and stored in -20 Cuntil further processing. Each set of 11 extractions was accompanied with one extractioncontrol to monitor possible contamination.

We conducted all initial PCR standardizations using muscle tissue DNA samples.We carried out amplifications for each primer in 10 mL reaction volumes containingfour mL Qiagen Multiplex PCR buffer mix (Qiagen Inc., Valencia, CA, USA), 0.2 mMlabeled forward primer, 0.2 mM reverse primer, four mM BSA, and two mL of 1:10 dilutedDNA extract. The temperature regime included an initial denaturation (94 C for 15 min);35 cycles of denaturation (94 C for 30 s), annealing (53 or 57 C for 45 s) and extension(72 C for 45 s); followed by a final extension (72 C for 30 min). Post-temperaturestandardization, primers with identical annealing temperatures was optimized formultiplex reactions with the same samples of both species (see Table 1). Subsequently,all test samples were amplified with standardized parameters. During all amplifications,both extraction controls and PCR negative controls (one PCR negative every set ofamplifications) were included to monitor any possible contamination. PCR productswere visualized in 2% agarose gels, and genotyped using LIZ500 size standard in anautomated ABI3500XL genetic analyser. Microsatellite alleles were scored using programGENEMARKER (Softgenetics Inc., State College, PA, USA) and allele bins for each locuswere created from the data generated. We randomly re-genotyped 15% of each locus fromdifferent samples to check for reliable genotypes and estimated genotyping error rates.

Data analysisWe calculated average amplification success as the percent positive PCR for each locus,as described by Broquet, Mnard & Petit (2007). We quantified allelic dropout and falseallele rates manually as the number of dropouts or false alleles over the total number ofamplifications, respectively (Broquet, Mnard & Petit, 2007). We also calculated theProbability of Identity for siblings (PIDsibs), the probability of two individuals drawnfrom a population sharing the same genotype at multiple loci and the theoreticalProbability of Identity or PIDunbiased (Waits, Luikart & Taberlet, 2001) using programGIMLET (Valire, 2002). We tested the frequency of null alleles in our data set usingFREENA (Chapuis & Estoup, 2007) whereas summary statistics, tests for deviations fromHardyWeinberg equilibrium and pairwise linkage disequilibrium were calculated for eachlocus using program ARLEQUIN v.3.1 (Excoffier, Laval & Schneider, 2005).



Table

1Characterizationof

microsatellite

loci

genotyped

insnow

petrel

andWilsonssstorm

petrelindividu

alsfrom

Antarctica.

Sp.

Locus

Primer

sequ

ences53

Repeat

nature

Repeat

motif

Dye

PSR

(bp)

Ta

CNa

Ho

He

Allelic

range

PID

unbiased

cumulative

PID

sibs

cumulative

AS

(%)

ADO

(%)

PCR

set

a)Sn

owPetrel

(n=55)

Ptero08

aF:

GCACCTGCTGGTGATGAGTC

R:A

GGGAAAAGGAACCATCCAG

Tetra

(AAGG) 8

VIC

181221

5311

0.49

0.73

528.03

10

-02

4.1

10-0

196.4

0Set2

Paequ

03b

F:TGTGGGTGCAGTAGAGCA

R:C

AATAAGAAGATCAGCAGAACAGAC

Di

(GA) 19

VIC

219243

5312

0.68

0.72

247.63

10

-03

1.71

10

-01

98.2

0Set1

Ptero07

aF:

TTAAAAATCGGTCCAATAGTCG

R:G

CACAGAGTTGACCGTGTTG

Tetra

(AAAG) 8

FAM

177217

538

0.53

0.66

481.12

10

-03

7.89

10

-02

98.2

3.6

Set1

Ptero04

aF:

TGCATTGTTTCTGTCCAAACTC

R:G

GCTGGAATGCATAGTACCAAC

Di

(CA) 13

FAM

117147

5311

0.67

0.63

321.81

10

-04

3.78

10

-02

100

0Set2

Paequ

10b

F:GAAGCTGCACTGGAACTG

R:C

ATGTGGTAAGAATCCAGATG

Di

(CA) 8

NED

159183

537

0.20

0.56

123.79

10

-05

1.99

10

-02

98.2

0Set2

Paequ

13b

F:GACCTGCAGCAATAGCACGAC

R:T

GCCTTCATCAGAATCCTCCTG

Di

(GT) 9

PET

144150

574

0.07

0.44

61.27

10

-05

1.23

10

-02

100

0Set3

Paequ

07b

F:TGCAGACCTGACTTTCACAGCTC

R:C

CTCCAAACATCCAGCCATC

Di

(GT) 12

FAM

314320

573

0.30

0.40

64.83

10

-06

7.92

10

-03

100

0Set3

Paequ

02b

F:GCCTACTCCATCTTAATTGTG

R:G

GTTCATACAGTTTCCTAGGTC

Di

(CA) 2

TT(CA) 10

PET

180200

537

0.03

0.30

302.32

10

-06

5.74

10

-03

98.2

1.8

Set2

Ptero03

bF:

TGTGTACAGCATGTGCTTGAG

R:G

CTGAATGGCAGTTTCTTCC

Di

(CA) 9

FAM

165177

534

0.10

0.23

221.36

10

-06

4.50

10

-03

100

0Set1

Paequ

08b

F:TATTCTGAGACTTGCGTTATCC

R:G

TGATCCATTAGTTGATGTCTACTG

Di

(CA) 11

PET

215223

534

0.16

0.18

88.89

10

-07

3.71

10

-03

100

0Set1

* Ptero09

aF:

GCAAATACCAGTCTTCCAAAGG

R:T

TTAAGATAAAGATGTTTGAGAACCAC

Tetra

(AAGG) 8

FAM

161189

579

0.67

0.72

28

100

0Set3

* Ptero01

aF:

GAAAACAACTCCCCCACAAC

R:T

CCGTCAGACCTGCTGTATG

Di

(CA) 7

PET

82104

535

0.33

0.32

24

98.2

0Set1

Mean(SD)

7.08

(3.01)

0.35

(0.23)

0.49

(0.19)

24.5

(14.5)

b)Wilsons

Storm

Petrel

(n=24)

Ptero07

aF:

TTAAAAATCGGTCCAATAGTCG

R:G

CACAGAGTTGACCGTGTTG

Tetra

(AAAG) 8

FAM

177217

536

0.42

0.76

407.67

10

-02

3.95

10

-01

100

0Set1

Paequ

10b

F:GAAGCTGCACTGGAACTG

R:C

ATGTGGTAAGAATCCAGATG

Di

(CA) 8

NED

181191

534

0.38

0.64

101.39

10

-02

1.91

10

-01

100

0Set1

Paequ

13b

F:GACCTGCAGCAATAGCACGAC

R:T

GCCTTCATCAGAATCCTCCTG

Di

(GT) 9

PET

146148

572

0.08

0.5

24.59

10

-03

1.13

10

-02

100

8.3

Set2

Paequ

08b

F:TATTCTGAGACTTGCGTTATCC

R:G

TGATCCATTAGTTGATGTCTACTG

Di

(CA) 11

PET

219227

513

0.21

0.47

81.58

10

-03

6.91

10

-02

100

0Set3

Ptero01

aF:

GAAAACAACTCCCCCACAAC

R:T

CCGTCAGACCTGCTGTATG

Di

(CA) 7

PET

165177

534

0.17

0.44

125.56

10

-04

4.28

10

-02

100

0Set1

Paequ

03b

F:TGTGGGTGCAGTAGAGCA

R:C

AATAAGAAGATCAGCAGAACAGAC

Di

(GA) 19

VIC

219235

535

0.21

0.39

162.04

10

-04

2.79

10

-02

100

0Set1

Ptero03

aF:

TGTGTACAGCATGTGCTTGAG

R:G

CTGAATGGCAGTTTCTTCC

Di

(CA) 9

FAM

88104

572

0.17

0.35

169.25

10

-05

1.94

10

-02

91.7

0Set2

* Paequ

07b

F:TGCAGACCTGACTTTCACAGCTC

R:C

CTCCAAACATCCAGCCATC

Di

(GT) 12

FAM

312318

513

0.08

0.16

6

100

4.2

Set3

* Ptero09

aF:

GCAAATACCAGTCTTCCAAAGG

R:T

TTAAGATAAAGATGTTTGAGAACCAC

Tetra

(AAGG) 8

FAM

173185

616

0.67

0.55

16

91.7

0Set3

* Ptero04

aF:

TGCATTGTTTCTGTCCAAACTC

R:G

GCTGGAATGCATAGTACCAAC

Di

(CA) 13

FAM

127139

574

0.38

0.52

12

100

0Set2

Mean(SD)

3.9

(1.3)

0.28

(0.18)

0.48

(0.15)

13.8

(9.7)

Notes:

Sp,species;P

SR,produ

ctsize

range;Ta,annealingtemperature;N

a,nu

mberof

alleles;Ho,ob

served

heterozygosity;H

e,expected

heterozygosity;P

ID,probabilityof

identification

;AS,am

plification

success;ADO,allelic

drop

out.

*Lo

cusdeviatingfrom

HardyW

einb

ergequilib

rium

.aWelch

&Fleischer(2011).

bTechow&

ORyan(2004).



RESULTS AND DISCUSSIONWe genotyped a total of 55 snow petrel and 24 Wilsons storm petrel samples to test andstandardize the selected microsatellite markers. Snow petrel samples were selected from blood(n = 1), buccal swab (n = 2), carcass muscle tissue (n = 24), and hatched eggshells (n = 28) totest amplification success from different types of biological samples. Wilsons stormpetrel samples were all from muscle tissue of individual carcasses collected in the field.

Of the 15 loci tested during the initial standardization, 12 loci showed amplification forsnow petrel (loci Ptero2, Ptero6, and Ptero10 did not amplify), whereas only 10 locisuccessfully amplified for Wilsons storm petrel (loci Paequ2, Ptero2, Ptero6, Ptero8, andPtero10 did not amplify) (Table 1). Subsequently, these panels of 12 and 10 loci were testedwith all snow petrel and Wilsons storm petrel samples, respectively. Overall, theamplification success ranged between 96.4% and 100% for snow petrel and 91.7%100% forWilsons storm petrel; and allelic dropout rates were 03.6% and 08.3% for snow petreland Wilsons storm petrel respectively (see Table 1 for more details). For snow petrel,the loci varied from highly polymorphic (Paequ03-12 alleles, Ho-0.68) to less polymorphic(Paequ13-4 alleles, Ho-0.07), whereas for Wilsons storm petrel the loci were moderatelypolymorphic (Ptero07-6 alleles, Ho-0.76) to less polymorphic (Paequ13-2 alleles,Ho-0.08) (Table 1). Two loci in snow petrel (Ptero01 and Ptero09) and three loci inWilsons storm petrel (Ptero04, Ptero09, and Paequ07) were found to deviate from the HardyWeinberg Equilibrium, and thus were removed from any further analysis. There was noevidence for a strong linkage disequilibrium between any pair of loci (details in Files S2and S3). PIDsibs and PIDunbiased values were found to be 3.71 10-03 and 8.89 10-07 forsnow petrel, and 1.94 10-02 and 9.25 10-05 for Wilsons storm petrel, respectively.Locus-wise and average values for observed and expected heterozygosity, number of allelesand allelic size ranges are presented in Table 1. The frequency of null alleles across the lociwas observed to be low in both the study species (snow petrel0.11 0.09 and Wilsonsstorm petrel0.15 0.07, respectively; see File S4).

This paper is the first attempt to use nuclear microsatellite markers to individuallyidentify both snow petrel and Wilsons storm petrel in Antarctica, and the final paneldescribed here provide unambiguous individual identification from both species in ourstudy area. Testing the markers on various types of biological materials (tissue, blood,buccal swab, and hatched eggshells) showed high amplification success, but further testswith non-invasive samples (for example shed feathers) is required for long-term studies.Both PIDunbiased and PIDsibs values are also sufficient enough for population geneticstudies considering low population sizes of snow petrel (8001,000 individuals) andWilsons storm petrel (

storm petrel (Moodley et al., 2015) to create a comprehensive panel for studying largepopulations of the species.

CONCLUSIONMolecular genetic analysis has become crucial in understanding levels of geneticdifferentiation, hybridisation and extinction risk in seabird populations (Taylor & Friesen,2012). In critical ecosystems such as Antarctica, individual-level genetic data can be avaluable tool to study evolution, adaptation, past events of diversifications and extinctionsfor wide-ranging seabirds. Moreover, genetic datasets on species of pelagic seabirds such assnow petrel and Wilsons storm petrel are generally lacking in comparison withcharismatic species such as penguins. In this study, we could establish the efficacy of cross-species markers in individual identification of these two common Antarctic seabirdspecies. In the future, we aim to build upon the long-term genetic research under theAntarctica Wildlife Monitoring Programme by increasing spatio-temporal samplingefforts to understand the population structure, relatedness and other aspects and provideinsights to seabird behaviour (monogamy, extra-pair paternity etc.) and evolution. Thisdetailed genetic research will also aid in long-term ecological monitoring and conservationmanagement of breeding seabird populations of Antarctica.

Permits and ethical clearancesAll samples were collected under the Biology and Environmental Sciences component(Letter no: NCAOR/ANT/ASPA/2014-15/01) of the Indian Scientific Expeditions toAntarctica with appropriate approvals from the Environment Officer, Committee forEnvironmental Protection (Antarctic Treaty Secretariat), National Centre for Antarcticand Ocean Research, Earth System Science Organisation, Ministry of Earth Sciences,Government of India, Goa, India.

ACKNOWLEDGEMENTSWe thank the National Centre for Antarctic and Ocean Research, Ministry of EarthSciences for providing all logistic support during the Indian Scientific Expeditions toAntarctica. We are grateful to respective expedition leaders and team member volunteersof 33rd, 34th, and 35th Indian Scientific Expeditions to Antarctica for their assistanceduring field work. We thank A. Madhanraj, and MEERCAT lab members for their help inlaboratory and Srinivas for his inputs on data analysis. We sincerely thank WildlifeForensics and Conservation Genetics Cell, CAMPA Cell, Research Coordinator and Dean,Wildlife Institute of India for their support. Our sincere thanks to all the reviewers of theearlier version of the manuscript.

ADDITIONAL INFORMATION AND DECLARATIONS

FundingThe Wildlife Institute of India and DST-Inspire Faculty Award to Samrat Mondol(Grant no: IFA12-LSBM-47) provided financial support for this study. The funders had



no role in study design, data collection and analysis, decision to publish, or preparation ofthe manuscript.

Grant DisclosuresThe following grant information was disclosed by the authors:Wildlife Institute of India and DST-Inspire Faculty Award to Samrat Mondol: IFA12-LSBM-47.

Competing InterestsThe authors declare that they have no competing interests.

Author Contributions Anant Pande conceived and designed the experiments, performed the experiments,analysed the data, prepared figures and/or tables, authored or reviewed drafts of thepaper, approved the final draft.

Nidhi Rawat performed the experiments, analysed the data, authored or reviewed draftsof the paper.

Kuppusamy Sivakumar conceived and designed the experiments, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the finaldraft.

Sambandam Sathyakumar contributed reagents/materials/analysis tools, authored orreviewed drafts of the paper.

Vinod B. Mathur contributed reagents/materials/analysis tools, authored or revieweddrafts of the paper, approved the final draft.

Samrat Mondol conceived and designed the experiments, performed the experiments,analysed the data, contributed reagents/materials/analysis tools, prepared figures and/ortables, authored or reviewed drafts of the paper, approved the final draft.

Animal EthicsThe following information was supplied relating to ethical approvals (i.e., approving bodyand any reference numbers):

All samples were collected under the Biology and Environmental Sciences component(Letter no: NCAOR/ANT/ASPA/2014-15/01) of the Indian Scientific Expeditions toAntarctica with appropriate approvals from the Environment Officer, Committee forEnvironmental Protection (Antarctic Treaty Secretariat), National Centre for Antarcticand Ocean Research, Earth System Science Organization, Ministry of Earth Sciences,Government of India, Goa, India.

Data AvailabilityThe following information was supplied regarding data availability:

Dryad: DOI:10.5061/dryad.57027hc.

Supplemental InformationSupplemental information for this article can be found online at http://dx.doi.org/10.7717/peerj.5243#supplemental-information.


http://dx.doi.org/10.5061/dryad.57027hchttp://dx.doi.org/10.7717/peerj.5243#supplemental-informationhttp://dx.doi.org/10.7717/peerj.5243#supplemental-informationhttp://dx.doi.org/10.7717/peerj.5243https://peerj.com/

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http://dx.doi.org/10.1093/oxfordjournals.molbev.a025864http://dx.doi.org/10.1371/journal.pone.0129342http://dx.doi.org/10.16943/ptinsa/2017/48958http://dx.doi.org/10.1186/s12862-017-1008-xhttp://dx.doi.org/10.1642/0004-8038(2001)118[0242:gmiwss]2.0.co;2http://dx.doi.org/10.3354/meps09694http://dx.doi.org/10.1046/j.1471-8286.2003.00560.xhttp://dx.doi.org/10.1016/j.ympev.2009.09.005http://dx.doi.org/10.1046/j.1471-8286.2002.00228.x-i2http://dx.doi.org/10.1046/j.1365-294x.2001.01185.xhttp://dx.doi.org/10.1007/s12686-011-9409-4http://dx.doi.org/10.1038/hdy.2012.7http://dx.doi.org/10.7717/peerj.5243https://peerj.com/

Cross-species screening of microsatellite markers for individual identification of snow petrel Pagodroma nivea and Wilsons storm petrel Oceanites oceanicus in Antarctica ...IntroductionMethodsResults and DiscussionConclusionflink5References

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