DOI: 10.5061/dryad.12jm63z66
title: Genomic insights into the critically endangered King Island scrubtit

description: Small, fragmented or isolated populations are at risk of population
 decline due to fitness costs associated with inbreeding and genetic drift.
 The King Island scrubtit Acanthornis magna greeniana is a critically
 endangered subspecies of the nominate Tasmanian scrubtit A. m. magna, with
 an estimated population of &lt; 100 individuals persisting in three
 patches of swamp forest. The Tasmanian scrubtit is widespread in wet
 forests on mainland Tasmania. We sequenced the scrubtit genome using
 PacBio HiFi and undertook a population genomic study of the King Island
 and Tasmanian scrubtits using a double-digest restriction site-associated
 DNA (ddRAD) dataset of 5,239 SNP loci. The genome was 1.48 Gb long,
 comprising 1,518 contigs with an N50 of 7.715 Mb. King Island scrubtits
 formed one of four overall genetic clusters, but separated into three
 distinct subpopulations when analysed independently of the Tasmanian
 scrubtit. Pairwise FST values were greater among the King Island scrubtit
 subpopulations than among most Tasmanian scrubtit subpopulations. Genetic
 diversity was lower and inbreeding coefficients were higher in the King
 Island scrubtit than all except one of the Tasmanian scrubtit
 subpopulations. We observed crown baldness in 8/15 King Island scrubtits,
 but 0/55 Tasmanian scrubtits. Six loci were significantly associated with
 baldness, including one within the DOCK11 gene which is linked to early
 feather development. Contemporary gene flow between King Island scrubtit
 subpopulations is unlikely, with further field monitoring required to
 quantify the fitness consequences of its small population size, low
 genetic diversity and high inbreeding. Evidence-based conservation actions
 can then be implemented before the taxon goes extinct.
description: <em>2.1 Sample collection</em> To
 obtain indicative genetic diversity metrics across mainland Tasmania, we
 sampled between five and eleven scrubtits from seven
 <em>a-priori</em> subpopulations on mainland Tasmania
 (including Bruny Island) during the non-breeding season (January – March
 2021). Due to small population sizes and licensing restrictions on King
 Island, we sampled five individuals from each of the three locations
 during the same non-breeding season (Table 1, Figure 1). We trapped
 scrubtits using a single 6m mist net and one minute of scrubtit song
 broadcast using portable speakers (ANU animal ethics permit # A2021/33).
 We sampled blood (&lt; 20 μl per individual) using the standard
 brachial venepuncture technique with a 0.7mm needle into 70% ethanol. For
 two individuals from whom we were unable to safely obtain blood, we
 collected feathers shed during handling. One male Tasmanian scrubtit was
 collected under licence (see acknowledgements) for genome sequencing, from
 which organ tissue samples (heart, spleen, kidney, gonads, brain, liver)
 were taken (Table S1). For each individual we took standard morphometric
 measurements and scanned for any unusual physical features such as feather
 abnormalities or skin lesions that may be indicators of poor health. A
 single observer (CY) sampled and measured all birds, and the maximum
 capture time was 35 minutes. No birds showed adverse reactions to sampling
 and all flew off strongly upon release. The fifteen individuals sampled on
 King Island was the maximum permissible sample size under licence
 conditions. <em>2.2 DNA extraction,
 sexing and sequencing</em> High molecular weight
 DNA was extracted from flash frozen heart and kidney using the Nanobind
 Tissue Big DNA Kit v1.0 11/19 (Circulomics). A Qubit fluorometer (Thermo
 Fisher Scientific) was used to quantify DNA concentrations with the Qubit
 dsDNA BR assay kit (Thermo Fisher Scientific). RNA was extracted from
 heart, spleen, kidney, gonads, brain, and liver stored in RNA later using
 the RNeasy Plus mini Kit (Qiagen) with RNAse-free DNAse (Qiagen)
 digestion. RNA quality was assessed via Nanodrop (Thermo Fisher
 Scientific). We extracted DNA for population genomics from blood and
 feather samples using the Monarch® Genomic DNA Purification Kit (New
 England BioLabs, Victoria, Australia). We quantified DNA concentrations
 using a Qubit 3.0 fluorometer (yield range 10.3 – 209 ng
 μl<sup>-1</sup>, Table S1) and standardised the concentration
 of each sample to 10-30 ng µl<sup>-1 </sup>DNA for 20 – 25 μl
 and determined the sex of individuals using a polymerase chain reaction
 (PCR) protocol adapted from Fridolfsson and Ellegren (1999, Supplementary
 file S1). We arranged the samples on a single 96 well plate, containing
 five technical replicates of the samples with the highest DNA
 concentrations, an additional 21 non-technical replicates including all of
 the King Island samples, five extra samples from mainland Tasmania and one
 negative control. Double-digest restriction associated
 DNA (ddRAD) sequencing following Peterson et al. (2012) was undertaken at
 the Australian Genome Research Facility, Melbourne on an Illumina NovaSeq
 6000 platform using 150bp paired-end reads. Samples were first quantified
 using Quantifluor and visualised on 1 % agarose e-gel to ensure all
 samples exceeded the minimum input DNA quantity of 50 ng. Three
 establishment samples with at least 250 ng DNA that were representative of
 the distribution of the samples (2 Tasmanian scrubtits, 1 King Island
 scrubtit) were used to determine the optimal combination of restriction
 enzymes, which were <em>EcoRI</em> and
 <em>HpyCH4IV</em>. Further details on the library preparation
 protocol are provided in Supplementary file S1.
 <em>2.3 Genome sequencing and assembly</em>
 Full methodological details of the genome and transcriptome
 sequencing and assembly are provided in Supplementary file S2. In summary,
 high molecular weight DNA was sent for PacBio HiFi library preparation
 with Pippin Prep and sequencing on one single molecule real-time (SMRT)
 cell of the PacBio Sequel II (Australian Genome Research Facility,
 Brisbane, Australia). Total RNA was sequenced as 100 bp paired-end reads
 using Illumina NovaSeq 6000 with Illumina Stranded mRNA library
 preparation at the Ramaciotti Centre for Genomics (University of New South
 Wales, Sydney, Australia). Genome assembly was conducted on Galaxy
 Australia (The Galaxy Community, 2022) following the genome assembly guide
 (Price & Farquharson, 2022) using HiFiasm v0.16.1 with default
 parameters (Cheng et al., 2021; Cheng et al., 2022). Transcriptome
 assembly was conducted on the University of Sydney High Performance
 Computer, Artemis. Genome annotation was performed using FGENESH++ v7.2.2
 (Softberry; (Solovyev et al., 2006)) on a Pawsey Supercomputing Centre
 Nimbus cloud machine (256 GB RAM, 64 vCPU, 3 TB storage) using the longest
 open reading frame predicted from the global transcriptome, non-mammalian
 settings, and optimised parameters supplied with the <em>Corvus
 brachyrhynchos </em>(American crow) gene-finding matrix. The
 mitochondrial genome was assembled using MitoHifi v3 (Uliano-Silva et al.,
 2023). Benchmarking universal single copy orthologs (BUSCO) was used to
 assess genome, transcriptome and annotation completeness (Manni et al.,
 2021). <em>2.4 Bioinformatics pipeline and SNP
 filtering</em> Raw sequence data were processed
 using Stacks v2.62 (Catchen et al., 2013) and aligned to the genome with
 BWA v0.7.17-r1188 (Li & Durbin, 2009). Full details of the
 bioinformatics pipeline, which produced a variant call format (VCF) file
 containing 45,488 variants for SNP filtering in R v4.0.3 (R Core team
 2020) are provided in Supplementary file S1. We filtered genotyped
 variants using the “SNPfiltR” v1.0.0 package (DeRaad, 2022) based on (i)
 minimum read depth (≥ 5), (ii) genotype quality (≥ 20), (iii) maximum read
 depth (≤ 137), and (iv) allele balance ratio (0.2 – 0.8). Then, using a
 custom R script, we filtered SNPs based on (i) the level of missing data
 (&lt; 5%); (ii) minor allele count (MAC ≥ 3), (iii) observed
 heterozygosity (&lt; 0.6), and (iv) linkage disequilibrium
 (correlation &lt; 0.5 among loci within 500,000 bp).
 To ensure that relationships between individuals could be
 accurately inferred from the data, we used these SNPs and samples to
 construct a hierarchical clustering dendrogram based on genetic distance,
 with visual examination of the dendrogram confirming that all 24
 replicates paired closely together on long branches (Figure S1). The
 percentage difference between called genotypes of technical replicates was
 also used to confirm that genotyping error rates were low after filtering
 (mean 99.91% ± 0.005% SE similarity between replicates). We therefore
 removed one of each replicate pair from all further analyses. We also made
 a higher-level bootstrapped dendrogram by using genetic distances among
 sampling localities instead of individuals (Figure S2).
 We used “tess3r” (Caye et al. 2016, 2018) to perform a genome
 scan for loci under selection, using the Bejamini-Hochberg algorithm
 (Benjamini & Hochberg, 1995), with a false discovery rate of 1 in
 10,000 to correct for multiple testing. Because this method identified
 zero candidate loci under selection, we also used the
 <em>gl.outflank</em> function in “dartR” v2.0.4 to implement
 the OutFLANK method (Whitlock & Letterhos 2015) to infer the
 distribution of F<sub>ST</sub> for loci unlikely to be
 strongly affected by spatially diversifying selection. This method also
 identified zero putatively adaptive loci, leaving a final dataset for
 formal population genetic analysis containing all 70 originally sampled
 individuals, 5,239 biallelic SNPs, and an overall missing data level of
 0.98 %. The number of SNPs and samples removed from the dataset at each
 filtering step is provided in Table S2.  See
 accompanying Supplementary File for further information on library
 preparation, molecular sexing, library preparation, bioinformatics, genome
 sequencing, assembly and annotation. References cited above are provided
 in the main document.
description: # Genomic insights into the critically endangered King Island scrubtit
 [https://doi.org/10.5061/dryad.12jm63z66](https://doi.org/10.5061/dryad.12jm63z66) The dataset comprises: 1\) the raw .vcf file "CAGRF220911987_GA.vcf" 2\) a csv of the final filtered dataset used for genomic analysis within the manuscript:  "scrubtit_genos_140623.csv". Rows contain samples and columns contain SNPs 3\) a csv of sample metadata: "Sampinfo_ross_check.csv". Rows contain samples and columns contain metadata 4\) an annotated R- script for repeat analysis  ## Description of the data and file structure 2\) a csv of the final filtered dataset used for genomic analysis within the manuscript:  "scrubtit_genos_140623.csv". Rows contain samples and columns contain SNPs column 1: sample ID (as per 'sample1') in sample medtadata (column 1).  columns 2-5239: SNP ids Missing data code: NA 3\) a csv of sample metadata: "Sampinfo_ross_check.csv". Rows contain samples and columns contain metadate column 1- sample = sequencing sample ID column 2- sample1 = linked sample ID in the sample x SNP matrix column 3- state = state in which sample was obtained column 4- region = 2-level factor of Mainland (Tas) or King Island column 5- popn= 11 level factor for sample subpopulation as shown in Figure 1 of the manuscript. Also includes 'Weinglangta' where individual was collected for genome sequencing column 6- popn2 = 3 level factor for sampling area - mainland (Tas), east coast (Tas) or King Island column 7 & 8 - WGS84 decimal lat/long of sampling location column 9- treatment - all 'wild' column 10- sampling date in dd/mm/yyyy column 11- sex - molecular sex of sample column 12- notes - whether or not individual showed baldness on crown column 13- tech rep- whether or not sample was a technical replicate in the sequencing. no = 'blank', yes = 'Technical Replicate' column 14- specimen_id- Threatened species initiative specimen ID ## Sharing/Access information The genome assembly and raw transcriptome data will be made available under NCBI’s BioProject PRJNA1014961. The raw PacBio HiFi reads are publicly available from the Bioplatforms Australia Threatened Species Initiative: [https://data.bioplatforms.com/organization/threatened-species](https://data.bioplatforms.com/organization/threatened-species). The assembled genome, global transcriptome, and genome annotation generated in this study are available on Amazon Web Services Australasian Genomes Open Data Store: [https://awgg-lab.github.io/australasiangenomes/genomes.html](https://awgg-lab.github.io/australasiangenomes/genomes.html).   ## Code/Software All analysis was conducted in R using the package versions stated within the manuscript.