DOI: 10.5061/dryad.m0cfxpp0z
title: Along-shelf connectivity and circumpolar gene flow in Antarctic silverfish (Pleuragramma antarctica)

description: The Antarctic silverfish (Pleuragramma antarctica) is a critically
 important forage species with a circumpolar distribution and is unique
 among other notothenioid species for its wholly pelagic life cycle.
 Previous studies have provided mixed evidence of population structure over
 regional and circumpolar scales. The aim of the present study was to test
 the recent population hypothesis for Antarctic silverfish, which
 emphasizes the interplay between life history and hydrography in shaping
 connectivity. A total of 1067 individuals were collected over 25 years
 from different locations on a circumpolar scale. Samples were genotyped at
 fifteen microsatellites to assess population differentiation and genetic
 structuring using clustering methods, F-statistics, and hierarchical
 analysis of variance. A lack of differentiation was found between
 locations connected by the Antarctic Slope Front Current (ASF), indicative
 of high levels of gene flow. However, gene flow was significantly reduced
 at the South Orkney Islands and the western Antarctic Peninsula where the
 ASF is absent. This pattern of gene flow emphasized the relevance of
 large-scale circulation as a mechanism for circumpolar connectivity.
 Chaotic genetic patchiness characterized population structure over time,
 with varying patterns of differentiation observed between years,
 accompanied by heterogeneous standard length distributions. The present
 study supports a more nuanced version of the genetic panmixia hypothesis
 that reflects physical-biological interactions over the life history.
description: A total of 1067 <em>Pleuragramma antarctica
 </em>individuals from 19 sampling locations collected by multiple
 institutions between 1989 and 2014 were included in the analysis. Of
 these, 249 individuals collected between 1989 and 1997 from the Ross Sea
 (RS96, RS97), Halley Bay (HB89, HB91), and South Shetland Islands (SSI96)
 were included from a previous study on population structure using
 mitochondrial DNA (Zane et al. 2006). A further 562 individuals collected
 between 2001 and 2012 from Larsen Bay (LB07), Charcot Island (CI10),
 Marguerite Bay (MB01, MB02, MB10, MB11), and Joinville Island (JI07, JI10,
 JI12) were included from a previous connectivity study focused on the
 Antarctic Peninsula (Agostini et al. 2015). The remaining 256 individuals
 collected in 2011 and 2014 from the South Orkney Islands (SOI11), Larsen
 Bay (LB11), Filchner Trough (FT14), Atka Bay (AB14), and Halley Bay (HB14)
 have not previously been examined. All samples, including those obtained
 in 2011 and 2014 that had not been previously examined, were collected
 during authorized scientific cruises carried out by nations legally
 committed to the Convention for the Conservation of Antarctic Marine
 Living Resources (CCAMLR 1980). No manipulations were carried out on live
 samples. Muscle tissue or fin clips were preserved in
 95-100% ethanol at the time of sampling. Genomic DNA was extracted from
 specimens using a standard salting-out procedure (Patwary et al. 1994).
 Genomic DNA was extracted from all previously unexamined samples, as well
 as from tissue samples remaining from individuals on which the
 mitochondrial DNA analysis was performed in a previous study (Zane et al.
 2006). Concentration and quality of the extracted DNA (260/280 nm and
 260/230 nm) was checked using a NanoDrop UV–Vis spectrophotometer (Thermo
 Scientific) prior to PCR amplification. All extracted DNA was of high
 enough quality to use in subsequent PCR reactions.
 Individuals were genotyped using 16 published EST-linked
 microsatellites developed in<em> Chionodroco
 </em><em>hamatus
 </em><em>(Molecular Ecology Resources
 Primer Development Consortium et al. 2011, Agostini et al.
 2013)</em>, that had been previously shown to
 cross-amplify successfully in <em>P. antarctica
 </em><em>(Agostini et al.
 2015)</em>. The 16 loci were amplified in two multiplex
 PCR reactions. Multiplex PCR reaction volume was 10 µL, containing 1x
 QIAGEN Multiplex PCR Master mix (HotStartTaq DNA Polymerase, Multiplex PCR
 Buffer, dNTP Mix; QIAGEN, Hilden, Germany), 0.2 µM primer mix and 100 ng
 of genomic DNA. The PCR amplification profile for all loci consisted of:
 (1) an initial activation step of 15 min at 95 ˚C; (2) 30 cycles of
 denaturation at 94 ˚C for 30 s, annealing at 57 ˚C for 90 s, and extension
 at 72 ˚C for 60 s; and (3) a final extension of 30 min at 60 ˚C.
 PCR products were prepared for microsatellite genotyping and sent
 to an external service (BMR Genomics, http://www.bmr-genomics.com/), where
 they were sequenced using an ABI 3730xl automated sequencer (LIZ 500 as
 internal size ladder, Applied Biosystems, Waltham, MA, USA).
 Microsatellites were analyzed using GeneMarker ver. 2.6 (SoftGenetics).
 Genotypes of individuals included in a previous microsatellite-based
 analysis of <em>P. antarctica</em> (Agostini et al. 2015) were
 integrated into this analysis. To ensure that the datasets were
 comparable, two individual samples from this previous study (Agostini et
 al. 2015) were processed alongside samples from the present study as
 positive controls during each amplification and sequencing run. This
 allowed the comparison of the raw microsatellite data between the previous
 and current study and confirmed that there was no change in microsatellite
 sizing. Binning was automated with FlexiBin 2 (Amos et al. 2007) and
 refined by eye to assure accuracy with the corresponding binnings
 established in the previous study (Agostini et al. 2015).
description: <strong>Pleuragramma2018_Genotypes_15loci_GENEPOP.txt</strong> <strong>Text file containing the genotypes of all analyzed individuals at the 15 microsatellite loci considered in the final analysis. Data are divided by the 19 population divisions based on the 19 sampling provenances of samples. File prepared using CREATE ver 1.37 </strong><strong>(Coombs et al. 2008)</strong><strong> for compatibility with </strong>GENEPOP online (Rousset 2008), a common format for downstream bioinformatics analyses. Coombs JA, Letcher BH, Nislow KH (2008) create: a software to create input files from diploid genotypic data for 52 genetic software programs. Molecular ecology resources 8:578-580 Rousset F (2008) GENEPOP'007: A complete re-implementation of the GENEPOP software for Windows and Linux. Molecular ecology resources 8:103-106 <strong>Pleuragramma2018_Genotypes_metadata.xlsx</strong> Excel file containing all metadata associated with the genotypes produced in this study. Metadata (columns A – U) headers are: <strong>Study #</strong>: the ID number of the samples used in the present study. Samples without Study # (na) represent samples from a previous study (Agostini <em>et al.</em> 2015) - only the genotypes of these samples were used in the present study; <strong>Extract-DNA box ID</strong>: the DNA extraction ID combined with the ID of the storage box where the genomic DNA was stored (na values refer to samples from Agostini <em>et al</em>. 2015); <strong>BMR CODE</strong>: the ID given to sequences for samples produced by BMR (na values refer to samples from Agostini <em>et al</em>. 2015); <strong>Age</strong>: life stage: adult, juvenile, larva (na values refer to samples from Agostini <em>et al</em>. 2015); <strong>Tissue type</strong>: the type of tissue from which DNA was extracted: muscle, fin clip, fin/m (fin and muscle), whole (for larvae and juveniles) (na values refer to samples from Agostini <em>et al</em>. 2015); <strong>Year</strong>: sampling year; <strong>Geographic Origin</strong>: sampling area; <strong>Comment</strong>: additional information related to the sample, where relevant; <strong>Dataset</strong>: Cecilia (Agostini <em>et al.</em> 2015) or Jilda (present study); <strong>Population</strong>: population labels corresponding to sampling areas, representing the 19 population divisions; <strong>Pop specific</strong>: population labels corresponding to sampling location, further dividing the 19 population divisions into population subdivisions by sampling station within the 19 sampling areas; <strong>study#_pop ID</strong>: individual sample IDs based on Study # and population ID for samples from the present study, and Agostini <em>et al.</em> 2015 sample ID and population ID for samples from Agostini <em>et al.</em> 2015; <b>study#_pop specific ID</b>: individual sample IDs based on Study # and pop specific ID for samples from the present study, and Agostini <em>et al.</em> 2015 sample ID and pop specific ID for samples from Agostini <em>et al.</em> 2015; <b>Pop 6</b>: population labels corresponding to the 6 population divisions based on sampling region; <b>station</b>: sampling station information where available; <b>depth</b>: sampling depth in m; <b>SL</b>: standard length of sampled fish in cm where available; <b>Cruise</b>: name of research cruise during which sample was collected; <b>n</b>: total number of individuals in Pop specific group divisions; <b>Lat</b>: latitude of sampling location, where available; <b>Long</b>: longitude of sampling location, where available. Genotype data (columns V – BA) correspond to the genotype information for samples at the 16 total microsatellite loci sequenced in this study. Only 15 loci were ultimately used in the analysis, as allele Ch11230, columns AP and AQ, was problematic due to stuttering and interference from other fluorophores used during sequencing. Genotype information is expressed as whole numbers based on the output derived from Flexibin 2 (Amos et al. 2007), which takes microsatellite size information in base pairs and groups these into discrete allele categories based on the repeat length of the microsatellite. In this spreadsheet, each locus has two columns of information, the first providing the genotype of the first allele for that locus for a given individual, the second providing the genotype for the second allele for that locus for a given individual. Amos W, Hoffman JI, Frodsham A, Zhang L, Best S, Hill AVS (2007) Automated binning of microsatellite alleles: Problems and solutions. Molecular Ecology Notes 7:10-14