Metabarcoding Data from an Inventory of Freshwater Invertebrates from the Miller Creek Watershed, Kenai Peninsula, Alaska, USA

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Versões

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Como citar

Pesquisadores deveriam citar esta obra da seguinte maneira:

Bowser M L, Artaiz S I, Danner J M, Dent K K, Meyer B, Watts D, Wyrick W R (2022): Metabarcoding Data from an Inventory of Freshwater Invertebrates from the Miller Creek Watershed, Kenai Peninsula, Alaska, USA. v1.3. United States Geological Survey. Dataset/Occurrence. https://bison.usgs.gov/ipt/resource?r=knwr_miller_creek_2021&v=1.3

Direitos

Pesquisadores devem respeitar a seguinte declaração de direitos:

O editor e o detentor dos direitos deste trabalho é United States Geological Survey. To the extent possible under law, the publisher has waived all rights to these data and has dedicated them to the Public Domain (CC0 1.0). Users may copy, modify, distribute and use the work, including for commercial purposes, without restriction.

GBIF Registration

Este recurso foi registrado no GBIF e atribuído ao seguinte GBIF UUID: 9d7baaac-57db-4852-9993-7f0e7f15635b.  United States Geological Survey publica este recurso, e está registrado no GBIF como um publicador de dados aprovado por GBIF-US.

Palavras-chave

Occurrence

Dados externos

Os dados de recurso também estão disponíveis em outros formatos

Contatos

Cobertura Geográfica

The geographic coverage is part of the Miller Creek watershed including North Vogel Lake, Vogel Lake, and Miller Creek.

Cobertura Taxonômica

Annelida, Arthropoda, Mollusca

Cobertura Temporal

Dados Sobre o Projeto

Because benthic macroinvertebrates and zooplankton are susceptible to the pesticide rotenone, surveys of freshwater macroinvertebrates were conducted in the Miller Creek Watershed, Kenai Peninsula, Alaska ahead of a planned rotenone treatment in fall 2021. Currently, 32 of 32 planned samples have been collected in 2021. Another 32 post-treatment invertebrate samples are planned in 2022 to enable comparison of pre- and post-treatment freshwater invertebrate communities.

O pessoal envolvido no projeto:

Métodos de Amostragem

Field methods generally followed the methods of Massengill (2014, 2017). We took vertical plankton tows in the deepest parts of the lakes using an Aquatic Research Instruments Wisconsin net. We sampled littoral areas using with D-nets. We obtained benthic samples using either an AMS Incorporated model 445.11 Ekman dredge or an AMS Incorporated model 445.60 stainless steel dredge. Most benthic samples were sorted using a series of sieves.

Descrição dos passos do método:

1. Metabarcoding samples were stored in a -23 °C freezer exept when samples were being processed. Invertebrates were separated from debris by hand under a dissecting microscope. Care was taken to reduce possible crosscontamination of DNA among samples. Samples were shipped out on ice on September 29, 2021, arriving the next day at MR DNA (Shallowater, Texas, http://www. mrdnalab.com). We chose to use the mlCOIintF/jgHCO2198 primer set of Leray et al. (2013) for PCR, targeting a 313 bp region of the COI DNA barcoding region. The mlCOIintF/jgHCO2198 primer pair was used with barcode on the forward primer in a 30–35 PCR using the HotStarTaq Plus Master Mix Kit (Qiagen, USA) under the following conditions: 94 °C for 3 minutes, followed by 30– 35 cycles of 94 °C for 30 s, 53 °C for 40 seconds and 72 °C for 1 minute, after which a final elongation step at 72 °C for 5 minutes was performed. After amplification, PCR products were checked in 2% agarose gel to determine the success of amplification and the relative intensity of bands. Multiple samples were pooled together in equal proportions based on their molecular weight and DNA concentrations. Pooled samples were purified using calibrated Ampure XP beads. The pooled and purified PCR product was used to prepare an illumina DNA library. Sequencing was performed at MR DNA on a MiSeq following the manufacturer’s guidelines. We processed the raw sequence data on the USGS Yeti supercomputer (USGS Advanced Research Computing, 2021) using R, version 4.1.1 for manipulating data and Je, version 2.0.RC (Girardot et al., 2016) for demultiplexing. The raw data included reads in alternating directions with the sample barcodes only on one read. Accordingly, we exectued the je demultiplex command, accepting the defaults that only require one of the two reads to contain a sample barcode. Raw read data were processed using MetaWorks, version 1.9.5 (Porter and Hajibabaei, 2020), running the default analysis options for metazoan COI DNA barcode sequences. Sequences were identified using the RDP classifier, version 2.13 (Wang et al., 2007) and the CO1 Classifier, version 4.0.1 reference library (Porter, 2017; Porter and Hajibabaei, 2018). Finally, we also compared the sequences to libraries we had obtained in previous metabarcoding projects on the Kenai Peninsula.

Citações bibliográficas

1. Massengill R (2014) Control Efforts for Invasive Northern Pike on the Kenai Peninsula, 2009. Special Publication 14-11. URL http://www.adfg.alaska.gov/FedAidPDFs/SP14-11.pdf http://www.adfg.alaska.gov/FedAidPDFs/SP14-11.pdf
2. Massengill R (2017) Stormy Lake Restoration: Invasive Northern Pike Eradication, 2012. Special Publication 17-18, Alaska Department of Fish and Game, Divisions of Sport Fish and Commercial Fisheries. http://www.adfg.alaska.gov/FedAidPDFs/SP17-18.pdf http://www.adfg.alaska.gov/FedAidPDFs/SP17-18.pdf
3. Leray M, Yang Y J, Meyer C P, Mills S C, Agudelo N, Ranwez V, Boehm J T, Machida R J (2013) A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Frontiers in Zoology https://doi.org/10.1186/1742-9994-10-34 https://doi.org/10.1186/1742-9994-10-34
4. USGS Advanced Research Computing (2021) USGS Yeti Supercomputer. U.S. Geological Survey. https://doi.org/10.5066/F7D798MJ https://doi.org/10.5066/F7D798MJ
5. Girardot C, Scholtalbers J, Sauer S, Su S-Y, Furlong E E (2016) Je, a versatile suite to handle multiplexed NGS libraries with unique molecular identifiers. BMC Bioinformatics 17:419. https://doi.org/10.1186/s12859-016-1284-2 https://doi.org/10.1186/s12859-016-1284-2
6. Porter T M, Hajibabaei M (2020). METAWORKS: A flexible, scalable bioinformatic pipeline for multi-marker biodiversity assessments. BioRxiv, 2020.07.14.202960. https://doi.org/10.1101/2020.07.14.202960 https://doi.org/10.1101/2020.07.14.202960
7. Wang Q,Garrity G M, Tiedje J M, Cole J R (2007) Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology 73:5261. https://doi.org/10.1128/AEM.00062-07 https://doi.org/10.1128/AEM.00062-07
8. Porter T M (2017) Eukaryote CO1 Reference Set For The RDP Classifier. https://doi.org/10.5281/zenodo.4741447 https://doi.org/10.5281/zenodo.4741447
9. Porter T M, Hajibabaei M (2018) Automated high throughput animal CO1 metabarcode classification. Scientific Reports 8:4226. https://doi.org/10.1038/s41598-018-22505-4 https://doi.org/10.1038/s41598-018-22505-4

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