Acknowledgements
The authors wish to thank a number of individuals who contributed to this study. Laboratory assistance was provided by Clare O’Connell, Michael Kiso, Jack Lemke and Evan Miller. Nancy Rotzel and Katie Murphy are thanked for performing the sequencing runs at the Center for Conservation Genomics, Smithsonian Conservation Biology Institute, and the Laboratory of Analytical Biology, National Museum of Natural History, Smithsonian Institution, respectively. Several museums allowed for destructive sampling of specimens; the Museum of Vertebrate Zoology, University of California Berkeley, Chris Conroy, Jim Patton, Eileen Lacey and Michael Nachman, Los Angeles County Museum, Jim Dines and Kayce Bell, and the Humboldt State University Vertebrate Museum, Alyssa Semerdjian, Nick Kerhoulas and Allison Bronson, and the University of Michigan Museum of Zoology, Cody Thompson. We also express gratitude to Beatrice Hahn and Jesse Connell for their assistance implementing the CHIIMP pipeline. This study was funded by MTRH’s discretionary funds, as well as a Grants-in-Aid award from the American Society of Mammalogists, Sigma Xi Grants-in-Aid of Research award (G201903158734905) and the Humboldt State University Department of Biology Master’s Student Grant.
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Figures:
Figure 1: Scatter plot of average quality of PCR replicates following QC from prinseq-lite. The replicates for each specimen are shown across the x axis, and the % of ‘good’ reads are shown on the y-axis. Samples are sorted by type: tissue, high quality museum specimen and low quality museum specimens. Individuals of the same type are separated by a dashed line.
Figure 2: A flow chart of best practices for sample analysis performed here, with particular emphasis on how to assess low quality museum specimens. Note: many steps detailed under ‘CHIIMP Output’ can be manipulated when running the pipeline.
Tables:
Table 1: Summary of samples used in this study. Color coded throughout as green= tissue sample, blue = high quality museum specimen (also referred to as HQMS hereafter), and red = low quality museum specimens (LQMS). Samples were acquired from approved destructive sampling requests from several national museum collections as follows: HSU= Humboldt State University Vertebrate Museum, UMMZ= University of Michigan Museum of Zoology, MVZ= Museum of Vertebrate Zoology, University of California Berkeley, and LACM= Los Angeles County Museum.
Table 2: Primers used for microsatellite and mitochondrial cytochrome b amplification. Microsatellites were characterized by the length (S=short, under 150 bp, M=medium, 150-200 bp, and L= long, over 200 bp) and repeat motif as simple (standard dinucleotide repeat motif in all included microsatellites) and complex (where repeat motifs were interrupted by variable repeat units- only seen in GLSA12 here). The reverse primer for mitochondrial cytochrome b was newly designed for this study as previously published internal cytochrome bprimers did not amplify in G. oregonensis .
Table 3. Quality of individually prepared libraries as assessed by prinseq-lite. The sample name and replicate number are indicated in the Sample ID field, and the ‘all’ row includes the replicate counts when bioinformatically summed together. The ‘combined’ ID (also shown in bold) represents the second library prep where all microsatellite replicates + cytochrome b were pooled and run through the CHIIMP pipeline. Only quality data from the forward read (R1) is shown here. Samples retain the same color coding for tissue= green, HQMS= blue and LQMS= red. Average quality is shown per replicate, read counts and the percentage of reads passing prinseq-lite quality filtering are shown across all microsatellite replicates as well. Range was calculated from each individual replicate and excluded bioinformatically summed and combined library prep data.
Table 4: Summary of recovered genotypes from the CHIIMP pipeline (Barbian et al. 2018). All recovered genotypes are provided, any areas where a ‘-’ is found indicates no recovered genotype from that replicate. The accuracy across each amplification was calculated as well as the average accuracy per microsatellite and across sample type (tissue, HQMS and LQMS). The bioinformatically combined dataset as well as the pooled dataset genotypes are also provided. CHIIMP output provides various metrics on quality and as such a * represents where possible PCR stutter was removed, a ▵ represents where PCR artifacts were removed and ❖ represents where more than two prominent sequences were found. Genotypes shown in red italics were generated from less than 500 raw reads and should not be used in downstream analyses.
Table 5: Descriptive Statistics of samples sorted by type, either ‘tissue’, ‘HQMS’ or ‘LQMS’.
Table 6: MicroDrop results for bioinformatically combined datasets with a comparison of the raw, initial CHIMP output to the final, processed data. Both locus specific and individual rates of estimated allelic dropout are provided.
Appendices
Appendix 1: PCR Amplification success of the five included microsatellites and mitochondrial cytochrome b gene. Success was determined by the presence of a band in the expected size range from an agarose gel. Note: HSU 8180 represents a tissue sample so only two PCR replicates of each marker were performed.
Appendix 2: Coverage of cytochrome b across all samples, bowtie 2 v 2.3.0 was used to map reads and Geneious Prime calculated the included quality metrics. Quality metrics provided from the 300 bp fragment amplified in all samples except HSU 8180 for which the entire CDS (1,140 bp) was amplified and analyzed.
Appendix 3: Mitochondrial minimum spanning network.