Kenai National Wildlife Refuge Pollinator Surveys

資料紀錄

此資源出現紀錄的資料已發佈為達爾文核心集檔案（DwC-A），其以一或多組資料表構成分享生物多樣性資料的標準格式。 核心資料表包含 3,986 筆紀錄。

亦存在 3 筆延伸集的資料表。延伸集中的紀錄補充核心集中紀錄的額外資訊。 每個延伸集資料表中資料筆數顯示如下。

此 IPT 存放資料以提供資料儲存庫服務。資料與資源的詮釋資料可由「下載」單元下載。「版本」表格列出此資源的其它公開版本，以便利追蹤其隨時間的變更。

版本

以下的表格只顯示可公開存取資源的已發布版本。

權利

研究者應尊重以下權利聲明。:

此資料的發布者及權利單位為 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 註冊

此資源已向GBIF註冊，並指定以下之GBIF UUID: 86875091-d166-4986-802a-343b341424c6。  United States Fish and Wildlife Service 發佈此資源，並經由U.S. Geological Survey同意向GBIF註冊成為資料發佈者。

關鍵字

Occurrence; bees; pollinators

聯絡資訊

Matthew Bowser

• 元數據提供者 ●
• 出處 ●
• 連絡人

Fish and Wildlife Biologist

USFWS Kenai National Wildlife Refuge

PO Box 2139

99669 Soldotna

Alaska

US

matt_bowser@fws.gov

https://orcid.org/0000-0003-4879-3997

Anya Bronowski

• 出處

Biological Technician

U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge

Soldotna

Alaska

US

Dom Watts

• 出處

Pilot/Biologist

U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge

Soldotna

Alaska

US

地理涵蓋範圍

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

分類群涵蓋範圍

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

時間涵蓋範圍

計畫資料

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.

取樣方法

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.

方法步驟描述:

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).

額外的詮釋資料