DOI: 10.5061/dryad.stqjq2cb3
title: Sequencing data for seabird eDNA in long-nosed fur seal diets from southeastern Australia

description: Wildlife conflicts require robust quantitative data on incidence and
 impacts, particularly among species of conservation and cultural concern.
 We apply a multi-assay framework to quantify predation in a southeastern
 Australian scenario where complex management implications and calls for
 predator culling have grown despite a paucity of data on seabird predation
 by recovering populations of long-nosed fur seals (Arctocephalus
 forsteri). We apply two ecological surveillance techniques to analyse this
 predator’s diet – traditional morphometric (prey hard-part) and
 environmental DNA metabarcoding (genetic) analyses using an avian specific
 primer for the 12S ribosomal RNA (rRNA) gene – to provide managers with
 estimated predation incidence, number of seabird species impacted and
 inter-prey species relative importance to the predator. DNA metabarcoding
 identified additional seabird taxa and provided relative quantitative
 information where multiple prey species occur within a sample; while
 parallel use of both genetic and hard-part analyses revealed a greater
 diversity of taxa than either method alone. Using data from both assays,
 the estimated frequency of occurrence of predation on seabirds by
 long-nosed fur seals ranged from 9.1–29.3% of samples and included up to 6
 detected prey species. The most common seabird prey was the culturally
 valued little penguin (Eudyptula minor) that occurred in 6.1–25.3% of
 samples, higher than previously reported from traditional morphological
 assays alone. We then explored DNA haplotype diversity for little penguin
 genetic data, as a species of conservation concern, to provide a
 preliminary estimate of the number of individuals consumed. Polymorphism
 analysis of consumed little penguin DNA identified five distinct
 mitochondrial haplotypes – representing a minimum of 16 individual
 penguins consumed across 10 fur seal scat samples from 99 sampled across
 southeastern Australia. We recommend rapid uptake and development of
 cost-effective genetic techniques and broader spatiotemporal sampling of
 fur seal diets to further quantify predation and hotspots of concern for
 wildlife conflict management.
description: <strong>Methods (adapted from the
 manuscript)</strong>  
 Note that the dataset contained herein represents seabird DNA
 from the 12s ribosomal RNA gene, extracted and collected from 99
 long-nosed fur seal faecal (scat) samples from multiple time points and
 locations across southern New South Wales and Victoria described below,
 and in greater detail in the associated publication. Here we provide
 sequenced, demultiplexed and paired-end data in .fastq files, that have
 been minimally filtered and we highlight the steps taken with these shared
 data below, and provide further information on how the authors chose to
 further filter sequences. More processed versions of these data are
 available upon request.  
 <strong>Site background
 information</strong>  
 Long-nosed fur seal pups have been recorded (to the best of our
 knowledge) at Deen Maar Island since 2002, Cape Bridgewater since 2008,
 Gabo Island since 2016, and Barunguba since 2000 (Arnould et al., 2003;
 McIntosh et al. unpub. data; Shaughnessy et al., 2001). Pup numbers, as an
 index of population size in 2013, were ~100 at Cape Bridgewater, ~ 24 at
 Deen Maar Island (and in 2017), 8 at Gabo Island and ~42 at Barunguba
 (McIntosh et al., 2014; McIntosh et al. unpublished data) (Illustrated in
 Figure 1 in Hardy et al. [2024]). Additionally, Phillip Island, in
 north-central Bass Strait, is home to the largest little penguin colony,
 with an estimated 31,000 breeding pairs of penguins in 2010 (Sutherland
 & Dann, 2014). Seabird morphological remains are conspicuous
 across long-nosed fur seal colonies in southeastern Australia (Illustrated
 in Figure 2 in Hardy et al. [2024]).  
 <strong>Sample collection</strong>
   Individual predator scat samples (n =
 99) were collected across multiple time points from four long-nosed fur
 seal breeding colonies in Victorian Bass Strait and New South Wales (NSW),
 southeastern Australia (Fig. 1 & 2). Pup abundances are
 illustrated as a conventional proxy for relative seal population abundance
 (Fig. 1; Appendix S1.1). Most samples were collected from the two larger
 colonies, Barunguba and Cape Bridgewater, in the Austral spring
 (September) 2016 and summer (January) 2017, with additional samples
 included from spring 2015 and summer 2016 at Cape Bridgewater. Samples
 from Gabo Island were collected from summer 2017. One sample was
 opportunistically collected from a lactating female at Deen Maar Island
 and included in assays. Sample sizes resulted from balancing adequate
 replication per site with availability of fresh samples.
   Whole and moist scats were sampled to
 minimise bias from differential DNA degradation or partial loss of
 material (similar to Deagle et al., 2009). Whole scats were thoroughly
 mixed with individual disposable spatulas at point of collection and a 2
 mL subsample was taken from each field-homogenised scat for genetic
 analyses of prey tissues (Hardy et al. 2017). The remaining whole scats
 were collected for analyses of morphological prey remains, using
 individual, zip-lock bags. Samples were stored within hours of collection
 between -10˚ and -20˚C in portable freezers (WAECO) for up to 7 d in the
 field and transferred to -20˚C freezer facilities. 
   <strong>Identification of
 seabird genetic remains</strong>
   For the genetic assay, prey DNA
 extractions used 250 mg of faecal subsamples and MoBio PowerSoil® DNA
 Isolation Kits (now QIAGEN: www.qiagen.com)  effective for DNA extraction
 from highly inhibited and mixed samples of faecal origin (similar to
 Carroll et al. 2019), with modifications to the manufacturer’s
 instructions made to optimise DNA extraction. These included an overnight
 digestion phase in cell lysis buffer (C2 solution) at 4˚C, and the
 incubation step in inhibitor removal solution was extended from 5 to 60
 minutes at 4˚C. No host inhibitor step was required because the assays
 used do not detect mammals. Target DNA was then eluted in 100µL of 10 mM
 Tris buffer, MoBio PowerSoil® C6 solution, (www.qiagen.com) and stored at
 -20˚C. DNA extract concentrations were measured and verified using a
 NanoDrop™ Spectrophotometer (www.thermofisher.com/). For use in positive
 controls and to test primer specificity, nuclear DNA was extracted from
 the centre of the muscle tissue matrix (25 mg) of a domestic chicken
 (Gallus gallus domesticus) and a little penguin carcass obtained by
 Phillip Island Nature Parks, using Bioline Isolate II Genomic DNA Kits
 (www.bioline.com/us/) as per manufacturer instructions. A dedicated
 controlled eDNA laboratory was used at RMIT University, Bundoora,
 Victoria, with separate spaces and rooms designated for the physical
 separation of eDNA extraction, pre-PCR preparations and post-PCR
 procedures. Positive and negative controls (extraction and PCR) were used
 to identify potential contamination at each laboratory procedural step
 from DNA extraction to diagnostic PCR steps.
   The 99 faecal DNA sample extracts were
 screened in duplicate and at two DNA concentrations (neat and 1:10
 dilutions), alongside extraction blanks (n = 5), PCR blanks (n = 4), and
 positive controls (n = 2) by diagnostic endpoint PCR (dPCR) using the
 Bird12sa/h assay (forward 5’ CTGGGATTAGATACCCCACTAT to 3’, reverse 5’
 CCTTGACCTGTCTTGTTAGC to 3’), a conservative primer ‘Bird12sa/h’ targeting
 a ~230 base pair (bp) fragment of the avian 12S ribosomal RNA (rRNA) gene
 (Cooper, 1994) (Table 1). PCRs were run on Bio Rad C1000 Touch thermal
 cycler using cycling steps outlined in Table S2, and using the AmpliTaq
 Gold® 360 Master Mix using reagents and concentrations provided by the
 manufacturers. All duplicate dPCR products were run on 1.5% agarose gels
 to determine the presence/absence of amplified target bird DNA. We
 obtained optimal amplification and low inhibition from neat DNA
 concentrations.   Using the
 avian specific Bird12sa/h assay, a total of 32 samples (of 99) showed
 target amplicons in both or a single duplicate at neat DNA concentration,
 all extraction and PCR controls were negative. DNA extracts of the 32
 samples that tested positive for birds, and two extraction blanks and one
 positive control (n = 35 samples for sequencing) were therefore sent for
 quantitative PCR (qPCR), cleanup, sample-based rarefaction and
 extrapolation sampling curves, appropriate sequencing depth (&lt;
 10,000 reads per sample) and next generation sequencing performed on
 Illumina Miseq by Ramaciotti Centre for Genomics (RCG), University of New
 South Wales. There, a single-step fusion tagging PCR procedure was used to
 attach and assign unique MID (Multiplex IDentifier) tag combinations, next
 generation sequencing (NGS) adaptors and the Bird12sa/h assay. Amplicons
 were purified and blended at RCG in equimolar concentrations to form a
 library, which was sequenced with a 150 bp paired-end sequencing kit
 (Illumina Miseq v2 Nano 150 bp). We used the single-step fusion PCR
 procedure over the two-step PCR approach to reduce the risk of ‘tag
 jumping’ during the second amplification step where MID tags are assigned
 (Taberlet et al., 2018, Schnell et al., 2015). This type of error is
 difficult to detect and risks cross-contamination of amplified DNA among
 samples between initial PCR products and terminal PCR products.
 Single-step fusion PCR procedures therefore provide us with the least risk
 of sample cross-contamination over other procedures. After sequencing,
 samples were ‘demultiplexed’ and assigned to the correct original sample
 by their individual MID tags.  
 We used Geneious R8.1.5 (Kearse et al., 2012) to merge the
 paired-end forward and reverse sequences (2x ~150 bp fragments, with
 overlap of 70 bp) and retain only those with exact flanking MID tags,
 primers, and adapter sequences. Once paired, the MID tags, NGS adaptor
 sequences and the Bird12sa/h forward and reverse primers, were
 subsequently trimmed, leaving the complete target sequences for each
 sample. Sequences were discarded if they did not contain exact matches to
 both the forward and reverse PCR primers, tags, and adaptor sequences,
 failed to pair, or were &gt; 10% shorter than the primer product
 length (expected 220 bp, discarded below 200 bp) (as in Berry et al.,
 2017, and Hardy et al., 2017). The data contained within the .fastq files
 shared herein have been processed up until this point. Below we include
 information on the next steps that we took with these data, but we chose
 to share the rawer pre-filtered version of these data in order to allow
 future users to filter and quality control to their preferred methods and
 standards.  
 <strong>Further sequence quality
 filtering</strong>  
 These sequences were quality filtered and clustered into
 molecular operational taxonomic units (OTUs) using the UPARSE algorithm in
 USEARCH (Edgar, 2010; Edgar & Flyvbjerg, 2015) and using a 97%
 similarity criterion (as in Berry et al., 2017). Illumina’s Miseq has been
 found to have an error rate of about 0.1% (Fox et al. 2014), we chose a
 conservative 1% cut off of aggregated 100% identical, unique nucleotide
 sequences (hereafter ‘unique sequences’) to further minimises the risk of
 erroneous sequences and false positives. We then mapped total filtered
 sequences (hereafter retained reads or sequences) for these identified
 OTUs back to individual samples. This can result in some samples
 containing very low to trace amounts of retained target sequences of high
 quality that belong to a more abundant cluster from the pool across
 samples.    Consensus,
 representative sequences for each out were queried against the National
 Center for Biotechnology Information’s (NCBI) GenBank nucleotide database
 using the algorithm BLASTn (Basic Local Alignment Search Tool; Benson et
 al., 2005). The resulting queried sequences were assigned to taxa,
 following criteria and taxonomic reference databases outlined in Hardy et
 al. (2017) and Appendix S1.4 (Table S3). These criteria maximised
 confidence in making a taxonomic identification by remaining conservative
 in our assignments (i.e., selecting an identification at genus level)
 where multiple species were found to be closely related on the Bird12s
 gene, or where other likely and related prey lacked genetic reference
 material, which could lead to false assignment to a genetically related
 taxon with representative genetic reference material. All the identified
 seabirds occurred within the geographic ranges of the LNFS and are
 considered viable prey species for LNFS.
   <strong>Statement of Animal
 Ethics Approval & Permits for Sampling</strong>
   Samples from NSW were collected for
 another project and sub-samples were submitted to Phillip Island Nature
 Parks for screening of bird DNA. Samples from NSW were collected under
 University of Sydney ethics permit (L04/9-2013/4/6056); Australian
 Government permits to conduct research under the EPBC Act
 (AU-COM2013-224), and from the Office of Environment and Heritage NSW
 Scientific License (SL101244). Victorian research was performed under
 Phillip Island Nature Parks Ethics Permit (2.2016) and Department of
 Environment, Land, Water and Planning Research Permit (10007974). We
 acknowledge the following Nations and Traditional Owners on whose unceded
 lands we conducted this research: Yuin (Barunguba), Bunurong (Millowl,
 Phillip Island), Gunditjmara (Cape Bridgewater and Deen Maar Island),
 Eastern Maar (Deen Maar Island), Kulin (RMIT Bundoora) and Eora
 (USYD).   
 <strong>Literature Citations From
 Methods</strong>  
 Arnould, J. P. Y., Boyd, I. L., & Warneke, R. M.
 (2003). Historical dynamics of the Australian fur seal population:
 Evidence of regulation by man? Canadian Journal of Zoology.
 doi.org/10.1139/z03-134  
 Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J.,
 & Wheeler, D.L. (2005). GenBank. Nucleic Acids Research 33:
 suppl_1, D34–D38. doi.org/10.1093/nar/gki063
   Berry, T E., Osterrieder, S.K.,
 Murray, D.C., Coghlan, M.L., Richardson, A.J., Grealy, A.K., Stat, M.,
 Bejder, L., & Bunce, M. (2017). DNA metabarcoding for diet
 analysis and biodiversity: A case study using the endangered Australian
 sea lion (Neophoca cinerea). Ecology and Evolution 7:14, 5435–5453.
 doi.org/10.1002/ece3.3123   
 Carroll, E. L., Gallego, R., Sewell, M. A., Zeldis, J.,
 Ranjard, L., Ross, H. A., Tooman, L. K., O’Rorke, R., Newcomb, R. D.
 & Constantine, R. (2019). Multi-locus DNA metabarcoding of
 zooplankton communities and scat reveal trophic interactions of a
 generalist predator. Scientific Reports, 9(1), 1-14.
   Cooper, A. (1994). DNA from Museum
 Specimens. In B. Herrmann & S. Hummel (Eds.), Ancient DNA:
 Recovery and Analysis of Genetic Material from Paleontological,
 Archaeological, Museum, Medical, and Forensic Specimens (pp. 149–165).
 Springer. doi.org/10.1007/978-1-4612-4318-2_10 
   Deagle, B. E., Kirkwood, R., &
 Jarman, S. N. (2009). Analysis of Australian fur seal diet by
 pyrosequencing prey DNA in faeces. Molecular ecology, 18(9),
 2022-2038.   Edgar, R.C.
 (2010). Search and clustering orders of magnitude faster than BLAST.
 Bioinformatics, 26:19, 2460–2461.
 doi.org/10.1093/bioinformatics/btq461 
   Edgar, R.C., & Flyvbjerg, H.
 (2015). Error filtering, pair assembly and error correction for
 next-generation sequencing reads. Bioinformatics 31:21, 3476–3482.
 doi.org/10.1093/bioinformatics/btv401  
 Fox, E. J., Reid-Bayliss, K. S., Emond, M. J. & Loeb,
 L. A. (2014). Accuracy of Next Generation Sequencing Platforms. Next
 Generation Sequencing & Applications, 1.
   Hardy, N., Berry, T., Kelaher, B. P.,
 Goldsworthy, S. D., Bunce, M., Coleman, M. A., Gillanders, B. M., Connell,
 S. D., Blewitt, M., & Figueira, W. (2017). Assessing the trophic
 ecology of top predators across a recolonisation frontier using DNA
 metabarcoding of diets. Marine Ecology Progress Series, 573, 237–254.
 doi.org/10.3354/meps12165  
 Kearse, M., Moir, R., Wilson, A., Stones-Havas, S., Cheung, M.,
 Sturrock, S., Buxton, S., Cooper, A., Markowitz, S., Duran, C., Thierer,
 T., Ashton, B., Meintjes, P., & Drummond, A. (2012). Geneious
 Basic: An integrated and extendable desktop software platform for the
 organization and analysis of sequence data. Bioinformatics 28:12,
 1647–1649. doi.org/10.1093/bioinformatics/bts199
   McIntosh, R. R., Sutherland, D. R.,
 Dann, P., Kirkwood, R., Thalman, S., Alderman, R., Arnould, J. P.,
 Mitchell, T., Kirkman, S., Salton, M., & Slip, D. (2014). Pup
 estimates for Australian and New Zealand fur seals in Victoria, Tasmania
 and New South Wales between 2007 and 2013 (Final Report to The Australian
 Marine Mammal Centre, Department of Environment, Australian Government,
 95pp.)   Schnell IA, Bohmahh
 K, Gilbert TP. (2015) Tag jumps illuminaated – reducing sequence-to-sample
 misidentifications in metabarcoding studies. Molecular Ecology Resources.
 15, 1289-1303.  
 Shaughnessy, P. D., Briggs, S. V., & Constable, R.
 (2001). Observations on Seals at Montague Island, New South Wales.
 Australian Mammalogy, 23(1), 1–7. doi.org/10.1071/am01001
   Sutherland, D. R., & Dann, P.
 (2014). Population trends in a substantial colony of Little Penguins:
 Three independent measures over three decades. Biodiversity and
 Conservation, 23(1), 241–250.
 doi.org/10.1007/s10531-013-0597-y  
 Taberlet, P., Brown, A., Zinger, L., and Coissac, E. (2018).
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description: # Sequence data for seabird eDNA in long-nosed fur seal diets from
 southeastern Australia
 [https://doi.org/10.5061/dryad.stqjq2cb3](https://doi.org/10.5061/dryad.stqjq2cb3) The dataset contained herein represents seabird DNA from the 12s ribosomal RNA gene, extracted and collected from 99 long-nosed fur seal faecal (scat) samples from multiple time points and locations across southern New South Wales and Victoria described below, and in greater detail in the associated publication. Here we provide sequenced, demultiplexed and paired-end data in .fastq files, that have been minimally filtered and we highlight the steps taken with these shared data below, and provide further information on how the authors chose to further filter sequences. More processed versions of these data are available upon request. Data contained in .fastq files includes: * All seabird sequences found within submitted samples sequenced using the Bird12sa/h primer (Cooper, 1994). Methods for sample screen, diagnostic PCR and sequencing are described by Hardy et al. (2024). However, we provide a summary of key steps herein. * Using the avian specific Bird12sa/h assay, a total of 32 samples (of 99) showed target amplicons in both or a single duplicate at neat DNA concentration, all extraction and PCR controls were negative. DNA extracts of the 32 samples that tested positive for birds, and two extraction blanks and one positive control (n = 35 samples for sequencing) were therefore sent for quantitative PCR (qPCR), cleanup, sample-based rarefaction and extrapolation sampling curves, appropriate sequencing depth (&lt; 10,000 reads per sample) and next generation sequencing performed on Illumina Miseq by Ramaciotti Centre for Genomics (RCG), University of New South Wales. * There, a single-step fusion tagging PCR procedure was used to attach and assign unique MID (Multiplex IDentifier) tag combinations, next generation sequencing (NGS) adaptors and the Bird12sa/h assay. * Amplicons were purified and blended at RCG in equimolar concentrations to form a library, which was sequenced with a 150 bp paired-end sequencing kit (Illumina Miseq v2 Nano 150 bp). * After sequencing, samples were ‘demultiplexed’ and assigned to the correct original sample by their individual MID tags by RCG. * For each sample submitted for genetic sequencing on Illumina Miseq platform, RCG generated two paired-end forward and reverse sequences that were merged based on 70 bp overlap, and then trimmed of their forward and reverse primers, sample indexes (MID tags) and adapters off the ends of each sequence. The trimmed sequences are described in the metadata spreadsheet and below in description of the data and file structure. * Finally, additional sequences were discarded if they did not contain exact matches to both the forward and reverse PCR primers, tags, and adaptor sequences, failed to pair, or were &gt; 10% shorter than the primer product length (expected 220 bp, discarded below 200 bp). Thus, the data contained in shared .fastq files made available here were minimally filtered, but are not fully filtered to remove sequences that could have resulted from sequencing error or that occur below various thresholds of sequence quality. * We share this partially processed and minimally filtered dataset, because users differ greatly in the level of conservatism they wish to apply to sequencing data and we thus allow users to reprocess these data using their preferred thresholds for sequence quality filtering and in a format compatible with known pipelines for eDNA data processing. ## Description of the data and file structure Detailed description and metadata for file naming convention, location and dates of samples, as well as additional suppporting information regarding all samples that were screened both for hard-parts and for genetic remains within samples are contained in the metadata file titled "PINP_SEABIRD_META_AND_DATA.xlsx" uploaded alongside .fastq files. In this file, the following tabs describe and include the following information, and clarify how data were processed to arrive at the genetic data made available in the .fastq files share here: * README: This is a brief directory of the tabs included in this spreadsheet, this includes a brief summary of colours used when describing samples in the BIRD_DATA and BIRD_OTUs tabs. * FILES_NAMES: Describes the file naming structure of the .fastq files * Sequencing_samples_submission: contains metadata that informs how the data were processed prior to inclusion in this data publication and includes a detailed list of samples that tested positive in diagnostic and that were submitted for sequencing by Illumnia Miseq. This sheet includes information on the adapters, sample indexes (MID) tags and primers used, and that we also trimmed. Note that the sequencing platform originally generated two sets of .fastq files for each sample that included paired-end forward and reverse sequences (2x ~150 bp fragments, with overlap of 70 bp). To produce a single .fastq file for each sample made available here, the authors merged paired-end forward and reverse sequences based on 70 bp overlap, and then trimmed forward and reverse primers, sampled indexes (MID tags) and adapters off the ends of each sequence. * BIRD_DATA: includes fully processed data presence / absence data of hard-parts and genetic remains identified in each sample, this is an additional supporting information to accompany these data. * BIRD_OTUs: includes the outputs of our custom genetic data quality filtering steps to accompany the data files shared herein. * ESM_Tables_Figs: includes additional supporting metainformation that are published online with the manscript, but made available here alongside the data for ease of access of information. ## Sharing/Access information These data are released under the terms of the [Creative Commons Attribution License (CC BY)](http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in linked Frontiers in Marine Science journal (below) is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. ## Code/Software Processing of these data are described in the Methods section adapted from the manuscript linked below, code and processed data are also available in the public repository for project analyses on Github: [https://github.com/NatashaAHardy/pinp_stats/tree/main](https://github.com/NatashaAHardy/pinp_stats/tree/main). We chose to provide demultiplexed and minimally filtered data, as described in Methods, in order to allow future users to filter and process our sequencing data with the level of scrutiny and stringency they prefer. In the manuscript cited below, Hardy et al. (2024), we suggest software and provide links to code used to process the data from .fastq files through to analyses described in the manuscript. ## Associated Primary Research Article Hardy, N. A., Berry, T. E., Bunce, M., Bott, N., Figueira, W. F., & McIntosh, R. R. (2024) Quantifying wildlife conflicts with metabarcoding and traditional dietary analyses: applied to seabird predation by long-nosed fur seals. *Frontiers in Marine Science*, *11*, 1288769: [10.3389/fmars.2024.1288769](https://doi.org/10.3389/fmars.2024.1288769) ## Additional Literature Cited Cooper A. (1994). “DNA from Museum Specimens,” in *Ancient DNA: Recovery and Analysis of Genetic Material from Paleontological, Archaeological, Museum, Medical, and Forensic Specimens*. Eds. Herrmann B., Hummel S. (New York, NY, USA: Springer), 149–165. doi: 10.1007/978-1-4612-4318-2_10