WE035
Multi-locus DNA metabarcoding of western spotted skunk diet in the McKenzie River Ranger District of the Willamette National Forest from 2017-2019

CREATOR(S): Marie I Tosa, Taal Levi, Damon Lesmeister

PRINCIPAL INVESTIGATOR(S): Taal Levi, Damon Lesmeister

ORIGINATOR(S): Marie I Tosa

DATA SET CONTACT PERSON: Marie I Tosa

METADATA CONTACT: Marie I Tosa

DATA SET CREDIT:

Funding: USDA Forest Service, PNW research station National Science Foundation (LTER7 DEB-1440409) Oregon State University Northwest Ecological Research Institute ARCS Oregon Chapter Acknowledgments of persons: A. Coombs, B. Murley, K. Van Neste, and R. Rich and Rogue Detection Dogs (formerly with Conservation Canines) – H. Smith, J. Hartman, M. Poisson, C. Yee, Chester, Jack, and Scooby – for field support. We thank Levi Lab technicians for laboratory support. We also thank J. Hedges, S. Speir, and H. Thomas for mechanically sorting scats. Facilities were provided by the H. J. Andrews Experimental Forest and the Long Term Ecological Research program, administered cooperatively by the USDA Forest Service Pacific Northwest Research Station, Oregon State University, and the Willamette National Forest.

METADATA CREATION DATE:

29 Nov 2022

MOST RECENT METADATA REVIEW DATE:

6 Dec 2022

KEYWORDS:

food webs, predators, H. J. Andrews Experimental Forest (AND), genetics, predation, forest disturbance, timber harvest, terrestrial ecosystems, forest ecosystems, animals, invertebrates, vertebrates, salamanders, birds, mammals, plants

PURPOSE:

To provide a baseline for western spotted skunk diets in the forests of the Pacific Northwest

METHODS:

Experimental Design - WE035:

Description: Our western spotted diet study was part of a larger study on their spatial ecology in the temperate rainforest ecosystem of western Oregon that was conducted between April 2017 – September 2019. During this study, we set and maintained 112 baited trail cameras and captured and tracked western spotted skunks (nF = 12, nM = 19) using Tomahawk traps (Model 102 and 103, Tomahawk Live Trap Co., Hazelhurst, WI) and VHF radio-collars (M1545, 16 g; Advanced Telemetry Systems, Isanti, MN). Cameras placed in the HJA were paired with previously established long-term songbird monitoring (Frey, Hadley, and Betts 2016) and small mammal monitoring sites (Weldy et al. 2019). Cameras placed outside of the HJA were stratified based on elevation and old-growth structural index (Spies and Franklin 1988) and chosen randomly within logistical constraints. Both cameras and live traps were baited with a frozen house mouse (Mus musculus), a can of sardines (Culpidae), and/or various carnivore scent lures. We located skunks using radio-telemetry triangulation and homing techniques daily, weather permitting. Homing techniques were mainly used to locate rest site locations during the day whereas triangulation was used to locate skunks during the night when skunks were most active. All animal capture and handling were conducted in accordance with the guidelines set by the American Society of Mammalogists and were approved by the USDA Forest Service Institutional Animal Care and Use Committee (IACUC #2016-015) and the Oregon Department of Fish and Wildlife (Permit #107-17, 059-18, 081-19).

Field Methods - WE035:

Description: We collected western spotted skunk scat in multiple ways: 1) during western spotted skunk capture, 2) opportunistically while tracking western spotted skunks with radio-collars and checking trail cameras, and 3) using detection dog teams (summer and fall of 2018). Detection dog teams either surveyed 3 x 3 km grids within the study area for a minimum of 6 hours near camera trap locations where we detected western spotted skunk or focused their surveys around known spotted skunk rest sites. Focused surveys were necessary to increase scat sample sizes and increase spotted skunk scat detection rates. Moreover, western spotted skunk scats were difficult to locate opportunistically because typically, they were deposited after we tracked skunks to their rest sites, were in hard to search locations such as in hollow logs or a short distance from the rest site. We froze all scat samples until we processed them in the laboratory, and processed scats were dried for long-term storage. Detection dog location and collection times where recorded with a handheld GPS. The collection times were originally recorded in local time (Pacific Standard, after 11/4/2018 01:59 AM, or Pacific Daylight, before 11/4/2018 02:00 AM, depending on the date). Pacific Daylight times were converted to Pacific Standard time in the database.

Laboratory Methods - WE035:

Description: In the lab, we identified the diet of western spotted skunks using DNA metabarcoding (Massey et al. 2021; Eriksson et al. 2019) and mechanical sorting. For DNA metabarcoding, we extracted DNA in a laboratory dedicated to processing degraded DNA using the DNeasy Blood and Tissue kit (Qiagen, Germantown, Maryland) or the QIAamp Fast DNA Stool Mini Kit (Qiagen, Germantown, Maryland). We included an extraction blank with every batch of extractions as a negative control, where we used the same protocol but without a fecal sample (hereafter called extraction blanks). We kept extraction blanks throughout the DNA metabarcoding process. Following DNA extraction, we amplified 3 regions of the mitochondria and chloroplast DNA. First, we amplified a ~100 base-pair DNA segment of the ribosomal mitochondrial 12S gene using universal vertebrate primers (12S-V5-F’: YAGAACAGGCTCCTCTAG and 12S-V5-R: TTAGATACCCCACTATGC) (Kocher et al. 2017; Riaz et al. 2011) and the chloroplast-encoded intron region of the trnL gene using universal plant primers (g-F: GGGCAATCCTGAGCCAA and h-R: CCATYGAGTCTCTGCACCTATC) (Taberlet et al. 2007) in a multiplex polymerase chain reaction (PCR). In a separate singleplex PCR reaction, we amplified the mitochondrial-encoded cytochrome oxidase subunit I (COI) gene using ANML universal arthropod primers (LCO1490-F: GGTCAACAAATCATAAAGATATTGG and CO1-CFMRa-R: GGWACTAATCAATTTCCAAATCC) (Jusino et al. 2019). We performed 3 PCR replicates per sample using the QIAGEN Multiplex PCR kit (Qiagen, Germantown, Maryland) (Appendix S1). To aid in identifying contamination, we performed PCR on a negative control on each plate (hereafter called PCR blanks) in addition to the extraction blanks. Each reaction was amplified with identical 8 base pair tags on the 5’ end of the forward and reverse primer that were unique to each sample to identify individual sample after pooling and to prevent misidentification of prey samples due to tag jumping (Schnell, Bohmann, and Gilbert 2015). We normalized and pooled the PCR products and used NEBNext Ultra II Library Prep Kit (New England BioLabs, Ipswich, Massachusetts) to adapt the library pools into Illumina sequencing libraries (Illumina Inc., San Diego, California). We purified libraries using the Solid Phase Reversible Immobilization beads and sent libraries to the Center for Genome Research and Biocomputing at Oregon State University for 150 base pair paired-end sequencing on the Illumina HiSeq 3000. We paired raw sequence reads using PEAR (Zhang et al. 2014) and demultiplexed samples based on the 8-base pair-index sequences using a custom shell script (Appendix S2). We counted unique reads from each sample replicate and assigned taxonomy using BLAST against the 12S, COI, and trnL sequences in a local database and GenBank (www.ncbi.nlm.nih.gov/blast). Scat amplification was considered successful if DNA sequencing produced over 100 total reads per replicate, and we limited the effects of contamination by retaining only species that consisted of more than 1% of the total reads. Furthermore, we used extraction and PCR negative controls to set additional filtering thresholds for species read counts. Species were only retained in the final species list if it was present in at least 2 of the 3 replicates and if their species distribution maps included our study area or were included on the species lists of the study area (https://andrewsforest.oregonstate.edu/about/species). We identified plants to genus since congeners are difficult to differentiate using these primers. To mechanically sort scats, we placed dried scat contents in a petri dish and separated items using forceps. We identified remains macroscopically to the lowest taxonomic order possible (typically class or order). If we had used all fecal matter for DNA metabarcoding, we relied on notes on identifiable parts from when the scat was collected or processed samples for DNA extraction. Once mechanically sorted, we compared our findings to the DNA metabarcoding results for each scat. If the identified taxon was not included in the DNA metabarcoding results, we augmented the results with the missing taxon. We used mechanical sorting to augment results from DNA metabarcoding because of known biases introduced by mismatches in the universal invertebrate ANML primers we used, which is attributed to a lack of conserved regions across all invertebrates (Deagle et al. 2014). We confirmed scats as defecated by western spotted skunks using the metabarcoding data following criteria: 1) western spotted skunk was the only carnivore (order: Carnivora) identified in the scat, or 2) western spotted skunk was one of the carnivores identified in the scat and the other carnivores consisted of less than 10% of the read count. We confirmed the predator in this way because predators are frequently misidentified through scat morphology (Morin et al. 2016; Lonsinger, Gese, and Waits 2015).

Citation: Deagle, Bruce E., Simon N. Jarman, Eric Coissac, François Pompanon, and Pierre Taberlet. 2014. “DNA Metabarcoding and the Cytochrome c Oxidase Subunit I Marker: Not a Perfect Match.” Biology Letters 10 (9): 20140562. https://doi.org/10.1098/rsbl.2014.0562. Eriksson, Charlotte E., Katie M. Moriarty, Mark A. Linnell, and Taal Levi. 2019. “Biotic Factors Influencing the Unexpected Distribution of a Humboldt Marten (Martes Caurina Humboldtensis) Population in a Young Coastal Forest.” PLOS ONE 14 (5): e0214653. https://doi.org/10.1371/journal.pone.0214653. Jusino, Michelle A., Mark T. Banik, Jonathan M. Palmer, Amy K. Wray, Lei Xiao, Emma Pelton, Jesse R. Barber, et al. 2019. “An Improved Method for Utilizing High-Throughput Amplicon Sequencing to Determine the Diets of Insectivorous Animals.” Molecular Ecology Resources 19 (1): 176–90. https://doi.org/10.1111/1755-0998.12951. Kocher, Arthur, Benoit de Thoisy, François Catzeflis, Mailis Huguin, Sophie Valière, Lucie Zinger, Anne-Laure Bañuls, and Jérôme Murienne. 2017. “Evaluation of Short Mitochondrial Metabarcodes for the Identification of Amazonian Mammals.” Methods in Ecology and Evolution 8 (10): 1276–83. https://doi.org/10.1111/2041-210X.12729. Lonsinger, Robert C., Eric M. Gese, and Lisette P. Waits. 2015. “Evaluating the Reliability of Field Identification and Morphometric Classifications for Carnivore Scats Confirmed with Genetic Analysis.” Wildlife Society Bulletin 39 (3): 593–602. https://doi.org/10.1002/wsb.549. Massey, Aimee L., Gretchen H. Roffler, Tessa Vermeul, Jennifer M. Allen, and Taal Levi. 2021. “Comparison of Mechanical Sorting and DNA Metabarcoding for Diet Analysis with Fresh and Degraded Wolf Scats.” Ecosphere 12 (6): e03557. https://doi.org/10.1002/ecs2.3557. Morin, Dana J., Summer D. Higdon, Jennifer L. Holub, David M. Montague, Michael L. Fies, Lisette P. Waits, and Marcella J. Kelly. 2016. “Bias in Carnivore Diet Analysis Resulting from Misclassification of Predator Scats Based on Field Identification.” Wildlife Society Bulletin 40 (4): 669–77. https://doi.org/10.1002/wsb.723. Riaz, Tiayyba, Wasim Shehzad, Alain Viari, François Pompanon, Pierre Taberlet, and Eric Coissac. 2011. “EcoPrimers: Inference of New DNA Barcode Markers from Whole Genome Sequence Analysis.” Nucleic Acids Research 39 (21): e145–e145. https://doi.org/10.1093/nar/gkr732. Schnell, Ida Bærholm, Kristine Bohmann, and M. Thomas P. Gilbert. 2015. “Tag Jumps Illuminated – Reducing Sequence-to-Sample Misidentifications in Metabarcoding Studies.” Molecular Ecology Resources 15 (6): 1289–1303. https://doi.org/10.1111/1755-0998.12402. Taberlet, Pierre, Eric Coissac, François Pompanon, Ludovic Gielly, Christian Miquel, Alice Valentini, Thierry Vermat, Gérard Corthier, Christian Brochmann, and Eske Willerslev. 2007. “Power and Limitations of the Chloroplast TrnL (UAA) Intron for Plant DNA Barcoding.” Nucleic Acids Research 35 (3): e14–e14. https://doi.org/10.1093/nar/gkl938. Zhang, Jiajie, Kassian Kobert, Tomáš Flouri, and Alexandros Stamatakis. 2014. “PEAR: A Fast and Accurate Illumina Paired-End ReAd MergeR.” Bioinformatics 30 (5): 614–20. https://doi.org/10.1093/bioinformatics/btt593.

SITE DESCRIPTION:

This study was centered around the H. J. Andrews Experimental Forest (HJA), which is located on the western slope of the Cascade Mountain Range near Blue River, Oregon (Figure 1). The area is surrounded by the McKenzie River Ranger District of the Willamette National Forest. Elevations range from 410 m to 1,630 m. The maritime climate consists of warm, dry summers and mild, wet winters. Mean monthly temperatures range from 1°C in January to 18°C in July. Precipitation falls primarily as rain, is concentrated from November through March, and averages 230 cm at lower elevations and 355 cm at higher elevations (Greenland 1993; Swanson and Jones 2002). During 2018 – 2019, western Oregon experienced an extreme drought (USDM 2022). In Lane County, drought severity was greatest during August 2018 – February 2019, but abnormally dry conditions began as early as January 2018 and moderate drought conditions began as early as June 2018 (Appendix S1: Figure S1). Lower elevation forests are dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga hetemphylla), and western red cedar (Thuja plicata). Upper elevation forests are dominated by noble fir (Abies procera), Pacific silver fir (Abies amabilis), Douglas-fir, and western hemlock. The understory is variable and ranged from open to dense shrubs. Common shrubs included Oregon grape (Mahonia aquifolium), salal (Gaultheria shallon), sword fern (Polystichum munitum), vine maple (Acer circinatum), Pacific rhododendron (Rhododendron macrophyllum), huckleberry (Vaccinium spp.), and blackberry and salmonberry (Rubus spp.). Before timber cutting in 1950, 65% of the HJA was covered in old-growth forest. Approximately 30% of the HJA was clear cut or shelterwood cut to create plantation forests varying in tree composition, stocking level, and age. In 1980, the HJA became a charter member of the Long Term Ecological Research network and no logging has occurred since 1985. The Willamette National Forest immediately surrounding the HJA has a similar logging history, but logging continues to occur. Currently, the HJA consists of a higher percentage of old-growth forest than the surrounding Willamette National Forest (approximately 58% in the HJA vs. 37% in the study area) (Davis et al. In Press). Wildfires are the primary disturbance type, followed by windthrow, landslides, root rot infections, and lateral stream channel erosion. Mean fire return interval of partial or complete stand-replacing fires for this area is 166 years and ranges from 20 years to 400 years (Teensma 1987; Morrison and Swanson 1990).

TAXONOMIC SYSTEM:

ITIS, the Integrated Taxonomic Information System

GEOGRAPHIC EXTENT:

HJ Andrews Experimental Forest and the surrounding Willamette National Forest (Blue River and Lookout Creek watersheds)

ELEVATION_MINIMUM (meters):

438

ELEVATION_MAXIMUM (meters):

1500

MEASUREMENT FREQUENCY:

irregular

PROGRESS DESCRIPTION:

Complete

UPDATE FREQUENCY DESCRIPTION:

notPlanned

CURRENTNESS REFERENCE:

Ground condition