Metabarcoding occurrences from Kenai National Wildlife Refuge Pollinator Surveys - 2022

データ レコード

この オカレンス（観察データと標本) リソース内のデータは、1 つまたは複数のデータ テーブルとして生物多様性データを共有するための標準化された形式であるダーウィン コア アーカイブ (DwC-A) として公開されています。 コア データ テーブルには、4,391 レコードが含まれています。

拡張データ テーブルは2 件存在しています。拡張レコードは、コアのレコードについての追加情報を提供するものです。 各拡張データ テーブル内のレコード数を以下に示します。

この IPT はデータをアーカイブし、データ リポジトリとして機能します。データとリソースのメタデータは、 ダウンロード セクションからダウンロードできます。 バージョン テーブルから公開可能な他のバージョンを閲覧でき、リソースに加えられた変更を知ることができます。

バージョン

次の表は、公にアクセス可能な公開バージョンのリソースのみ表示しています。

権利

研究者は権利に関する下記ステートメントを尊重する必要があります。:

パブリッシャーとライセンス保持者権利者は United States Fish and Wildlife Service。 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登録

このリソースをはGBIF と登録されており GBIF UUID: 86875091-d166-4986-802a-343b341424c6が割り当てられています。   GBIF-US によって承認されたデータ パブリッシャーとして GBIF に登録されているUnited States Fish and Wildlife Service が、このリソースをパブリッシュしました。

キーワード

Occurrence; bees; pollinators

連絡先

地理的範囲

We surveyed on the Kenai National Wildlife Refuge on the Kenai Peninsula, Alaska.

生物分類学的範囲

We surveyed for pollinators, mainly Hymenoptera, Diptera, Lepidoptera, and Coleoptera.

時間的範囲

プロジェクトデータ

In 2022 we collected pollinators as part of the Alaska Bee Atlas effort (https://accs.uaa.alaska.edu/wildlife/ak-bee-atlas). We collected pollinators on Kenai National Wildlife Refuge using blue vane traps, pollinator cups, and aerial nets.

プロジェクトに携わる要員:

収集方法

We followed the guidance of the sampling plan of Fulkerson et al. (2021). Most of KNWR lies within lowest priority hexagons of Fulkerson et al. (2021), but the southernmost part of the Refuge lies within a medium priority hexagon. We prioritized sampling in this area, but access in this region is difficult. We surveyed only at Emerald Lake in this medium priority hexagon. We surveyed for insect pollinators at a variety of other sites on the Refuge, trying to sample in diverse habitats. We sampled dry, rocky slopes off of Skilak Lake Road following the advice of Justin Fulkerson. We accessed sites by road and floatplane.

Method step description:

1. Field Methods We sampled pollinators using bee bowl traps, blue vane traps, and aerial nets, generally following the field methods of Fulkerson et al. (2021) with the exception that we collected specimens into SK picglobal 99.9% pure propylene glycol.
2. Specimen Processing Samples were stored in a -23°C freezer except when samples were being processed. Invertebrates were separated from debris by hand under a dissecting microscope. Care was taken to reduce possible cross-contamination of DNA among samples. We separated samples that were all or mostly bees from samples that were mostly flies and other invertebrates. We shipped 12 samples of bees to the Alaska Center for Conservation Science3, University of Alaska Anchorage, Anchorage, Alaska to be processed. We homogenized the remaining 19 samples plus one legacy bulk pollinator sample from a previous project (Bowser 2012) using a blender and cleaning between samples with DIY-DS cleaning solution as described by Buchner et al. (2021). Our sample homogenization protocol is included below. We homogenized samples using a Nutri Ninja QB3000SS blender (Euro-Pro Operating LLC 2015). DIY-DS recipe 20 g NaOH 20 g Alconox 15.1 g NaHCO3 267 ml 4.5% bleach deionized water to fill to 2 l Preparation 120 ml plastic cups should be washed with DIY-DS and rinsed before sampling. Finish by rinsing inside the 120 ml cup with deionized water. Hand dry 120 ml cup with paper towel. Homogenize samples Before running samples, rinse blender by running 100 ml of deionized water for 20 s. Pre-label a 10 ml plastic vial with the specimen GUID and add a barcode vial label. Also pre-label and add a barcode label to a 120 ml specimen cup. Clean forceps with DIY-DS. Take the label out of the original container with the cleaned forceps and place into the new 120 ml sample container. Add the contents of the sample vial to the blender. Rinse original sample vial with cold, clean propylene glycol and pour rinsate in the blender with the rest of the sample. Fill blender to 100 ml with cold, clean propylene glycol. Blend for 90 s. Using a new disposable pipette, fill the pre-labelled 10 ml plastic vial with about 9.5 ml of homogenate. Pour the rest of the sample into the pre-labeled 120 ml specimen cup. Rinse blender by running 100 ml tap water for 10 s. Wash blender by running 100 ml of DIY-DS for 10 s. Rinse this out in the lab sink with tap water. Rinse blender by running 100 ml deionized water for 10 s. We shipped 9 ml of homogenate from each of the 20 homogenized samples to Molecular Research Laboratory, Shallowater, Texas for metabarcoding.
3. Molecular Methods We chose to use the mlCOIintF/jgHCO2198 (GGWACWGGWT GAACWGTWTA YCCYCC / TAIACYTCIG GRTGICCRAA RAAYCA) 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 barcodes on the forward primer in 30–35 PCR cycles 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 30s, 53°C for 40 seconds and 72°C for 1 minute, after which a final elongation step at 72°C for 5minutes 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.
4. Bioinformatics The bioinformatics pipeline was run on the Yeti supercomputer (USGS Advanced Research Computing 2021). We used the MetaWorks pipeline, version 1.11.3 (Porter and Hajibabaei 2022) with the RDP classifier (Wang et al. 2007) and the Eukaryote CO1 reference set for the RDP Classifier, version 4.0.1 (Porter and Hajibabaei 2018). We processed data in R, version 4.2.2 and 4.2.3 (R Core Team 2022, 2023) using the R packages ape, version 5.7-1 (Paradis and Schliep 2019); Biostrings, version 2.66.0 (Pagès et al. 2022); bold, version 1.2.0 (Chamberlain 2021a); ips, version 0.0.11 (Heibl 2008); msa, version 1.30.1 (Bodenhofer et al. 2015); openssl, version 2.0.6 (Ooms 2023a); reshape2, version 1.4.4 (Wickham 2007); ritis, version 1.0.0 (Chamberlain 2021b); and uuid, version 1.1-0 (Urbanek and Ts’o 2022). We compared our sequences to sequences from a local reference library (Bowser 2022a) using the vsearch --usearch_global command of vsearch, version 2.21.1 (Rognes et al. 2016).

追加のメタデータ