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Research Article
A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera)
expand article infoMatthew L. Bowser, Stephen J. Burr§, Isaac Davis|, Garret D. Dubois§, Elizabeth E. Graham|, Jason E. Moan, Steven W. Swenson#
‡ U.S. Fish & Wildlife Service, Kenai National Wildlife Refuge, Soldotna, Alaska, United States of America
§ USDA Forest Service, Fairbanks, Alaska, United States of America
| USDA Forest Service, Juneau, Alaska, United States of America
¶ Alaska Division of Forestry, Anchorage, Alaska, United States of America
# USDA Forest Service, Anchorage, Alaska, United States of America
Open Access

Abstract

In response to the threat of introductions of non-native forest insects, the Early Detection and Rapid Response (EDRR) program in Alaska monitors for arrivals of non-native insects, an effort that is limited by the time required to process samples using morphological methods. We compared conventional methods of processing EDRR traps with metabarcoding methods for processing the same samples.

We deployed Lindgren funnel traps at three points of entry in Alaska using standard EDRR methods and trap samples were later processed using routine sorting and identification based on morphology. Samples were then processed using High Throughput Sequencing (HTS) metabarcoding methods. In three samples bycatch was included and in three samples non-native species were added.

Morophological and HTS methods yielded generally similar results for scolytine and cerambycid beetle assemblages, but HTS provided more species-resolution identifications (46 species) than morphological methods (4 species plus the 3 non-native species known a priori). None of the non-native species were detected by HTS. Including bycatch did not appear to hinder identifications of scolytine and cerambycid beetles by HTS. From among the bycatch, two Palearctic species adventive to North America, Placusa incompleta Sjöberg, 1934 and Hydrophoria lancifer (Harris, 1780), are newly reported from Alaska.

We do not recommend replacing our current morphological monitoring methods with HTS methods because we believe that we would be more likely to detect known non-native pest species using morphology. However, we would use HTS to increase our sample size without greatly increasing time required to process samples. We would also recommend HTS methods for surveillance monitoring where the set of target taxa is not limited to known pest species.

Keywords

Scolytinae, Cerambycidae, biomonitoring, High-Throughput Sequencin

Introduction

The introduction and establishment of non-native forest insects is considered to be one of the greatest threats to forest health (Rabaglia et al. 2008, Ramsfield et al. 2016). In response to the potential of non-native forest pests being introduced into Alaska, an Early Detection and Rapid Response (EDRR) program has been implemented to detect, delimit and monitor newly introduced non-native bark and wood-boring beetles at selected high-risk forest areas and quickly assess and respond to new infestations.

A methodological bottleneck constraining the EDRR program in Alaska is the time and expertise required to process EDRR trap catches, limiting the number of traps that can be deployed each season. A potential solution to this taxonomic bottleneck is the use of recently developed metabarcoding methods. Biomonitoring by metabarcoding has been advocated for arthropods because these methods have the potential to be much faster and less costly than identifications obtained by morphology (Hajibabaei et al. 2011, Baird and Hajibabaei 2012, Watts et al. 2019). Metabarcoding methods are already being adopted for biomonitoring of invertebrates (Gibson et al. 2015, Hajibabaei et al. 2016, Bush et al. 2019). Identifications obtained through metabarcoding should be of better taxonomic resolution in Alaska, where a deliberate effort has been made to construct a reference library of DNA barcode sequences useful for species identifications of terrestrial arthropods (Sikes et al. 2017), than in regions where such libraries are lacking (see Watts et al. 2019).

We sought to compare EDRR trapping results obtained by conventional means and by metabarcoding to determine if metabarcoding methods would be more appropriate for EDRR monitoring than methods currently used.

Methods

Field sampling

Lindgren funnel traps were deployed at three sites in Alaska: Joint Base Elmendorf-Richardson, Anchorage Borough (61.2547 °N, 149.7698 °W); Eielson Air Force Base, Fairbanks North Star Borough (64.6576 °N, 147.0535 °W); and the Juneau Ferry Terminal, Juneau Borough (58.3779 °N, 134.6983 °W) (Fig. 1). All three sites are points of entry into the state of Alaska, either by sea or air travel, where routine monitoring for non-native beetles has been ongoing.

Figure 1.  

Map of sampling locations generated using SimpleMappr (Shorthouse 2010).

Field methods followed standard EDRR methods as described by Rabaglia et al. (2008). Trapping began on April 26-27, 2017 and traps were run for two-week trapping sessions through August 30, 2017. Three traps were deployed at each site, each baited with ethanol, a combination of ethanol and α-pinene, or Ips typographus pheromone lure.

Laboratory methods

Twenty-six samples were processed using standard EDRR methods where all bark and wood-boring beetles were separated from bycatch by hand and identified by morphology. Forest health specialists trained in insect taxonomy identified specimens to the finest level of taxonomic resolution that their training allowed. In a typical EDRR work flow, some specimens would have been selected to be sent out to specialists for expert identification, but in this project all bark and wood-boring beetles were retained for DNA sequencing. Separating bycatch required roughly 64 work hours (2.1 hours/sample) and the identification step took another 64 work hours.

Four additional samples were used to test detection of exotic species and to determine if skipping the time-consuming step of removing bycatch reduced our ability to identify bark and wood-boring beetles. Dried specimens of Ips typographus (Linnaeus, 1758) were added to sample JBER10MAY17-R2, dried specimens of Tetropium fuscum (Fabricius, 1787) were added to sample JNUF20JUN17-R, and a live Halyomorpha halys Stål, 1855 was added to sample JBER10MAY17-R1. Bycatch was included in three samples (JBER10MAY17-R1, JBER10MAY17-R2, and JBER20JUN17-R). See Suppl. material 1 for details.

Thirty samples were delivered to RTL Genomics (http://rtlgenomics.com) on October 23, 2017. The E.Z.N.A. Insect DNA Kit (Omega Bio-tek, Inc., Norcross, Georgia, USA) were used for extractions. Insects were ground in liquid nitrogen using a pre-chilled mortar. Using a small spatula, a pea size amount of the insect homogenate was loaded into a sample tube. The remaining ground homogenate was saved and stored in the freezer. To each tube, 350 μl of CTL buffer and 25 μl of Proteinase K was added and the sample pipette mixed to gently mix the sample and buffers. The sample was incubated at 60 °C for 30 minutes with gentle shaking. After the incubation, 350 μl Chloroform: isoamyl alcohol (24:1) was added and gently vortexed. Samples were centrifuged for 2 minutes at 10,000 g, then 300 μl of supernatant was transferred to a new tube. Equal volumes of BL buffer and 2 μl RNase A were added and mixed by pipette. This was incubated at 70 °C for 10 minutes. Equal volumes of 100% ethanol was added and mixed by pipette, then 750 μl of lysate were transferred to HiBind DNA Mini Columns and centrifuged at maximum speed for 1 minute. Flow through was discarded. This was repeated until all lysate has been loaded onto the column. The column was transferred to a new collection tube, 500 μl of HBC buffer was added, and this and centrifuged at maximum speed for 30 seconds. Flow through was discarded, 700 μl of DNA Wash buffer was added, and the column was centrifuged at maximum speed for 1 minute. Flow through was discarded and the wash was repeated. The column was dried by centrifuging at maximum speed for 2 minutes, then 50 μl of pre-warmed Elution Buffer was added to the center of the column membrane and incubated at room temperature for 2 minutes. This was centrifuged at maximum speed for 1 minute. Eluate was recovered from the tube and added back to the center of the column, then the column was incubated for 2 minutes at room temperature. This was centrifuged at maximum speed for 1 minute.

Sequencing was performed on an Illumina MiSeq platform and reads were processed using RTL Genomics’ standard methods (Suppl. material 2) with the mlCOIlintF/HCO2198 primer set of Leray et al. (2013), yielding a 313 bp region of the COI gene. We selected this primer set because it has been shown to amplify well across a broad set of arthropod groups (Brandon-Mong et al. 2015, Hajibabaei et al. 2019). Paired, demultiplexed FASTQ files were delivered 73 days later on January 4, 2018 at a cost of 3,045 USD (101.50 USD/sample).

Metagenomics pipeline

Most of the metagenomic portion of the analysis was carried out on the Yeti supercomputer (Falgout and Gordon 2017) using QIIME 2 (Bolyen et al. 2018).

A regional reference library of Cytochrome Oxidase I DNA barcode sequences (Hebert et al. 2003) was constructed by downloading publicly available DNA barcode sequences and corresponding specimen data of arthropods from Alaska, Yukon Territory, British Columbia, and Russa from BOLD (Ratnasingham and Hebert 2007) on March 15, 2019, yielding 415,779 records. Search strings and numbers of records obtained are included in Table 1.

Search strings and numbers of records downloaded from BOLD for library construction.

Search String Records
"Arthropoda"[tax] "Alaska"[geo] 10,227
"Arthropoda"[tax] "British Columbia"[geo] 306,010
"Arthropoda"[tax] "Russia"[geo] 22,032
"Arthropoda"[tax] "Yukon Territory"[geo] 77,510

The library was first dereplicated using VSEARCH (Rognes et al. 2016). From among each set of replicate sequences, we used an R script run in R version 3.5.1 (R Core Team 2018) to select records having the most useful specimen data. First, if any of a set of replicate sequences had species-resolution identifications, these were selected. If none had species-resolution identifications, then the records having the most complete taxonomy were selected. Next, if some records had BIN assignments (Ratnasingham and Hebert 2013) while others did not, those having BIN assignments were chosen. Records from Alaska were chosen over records from other regions. Finally, if more than one record met all of these criteria, one record was randomly chosen using the sample function of R. Finally, the dereplicated sequences were clustered using VSEARCH with options "--cluster_fast --iddef 0 --id 0.99", yielding a referece library of 140,244 sequences.

The FASTQ files delivered by RTL Genomics were imported into QIIME 2 using qiime tools import with options "--type 'SampleData[PairedEndSequencesWithQuality]' --input-format PairedEndFastqManifestPhred33". The paired-end sequences were denoised and dereplicated using qiime dada2 denoise-paired (Callahan et al. 2016) with options "--p-trim-left-f 26 --p-trim-left-r 26 --p-trunc-len-f 200 --p-trunc-len-r 200 --p-n-threads 2". The resulting representative sequences were classified using qiime feature-classifier classify-consensus-vsearch (Bokulich et al. 2018) with options "--p-perc-identity 0.90 --p-maxaccepts 1", querying against the reference library described above.

The resulting amplicon sequence variant (ASV) table was filtered using the LULU algorithm (Frøslev et al. 2017) with parameters minimum_ratio_type = "min", minimum_match = 97, minimum_relative_cooccurence = 0.95. Detections with read counts less than 10 were then removed. A phylogenetic tree of the retained ASVs was generated using qiime phylogeny align-to-tree-mafft-fasttree. The tree was visualized interactively using using the Interactive Tree Of Life (Letunic and Bork 2019). The tree was trimmed so that fungi, one rotifer sequence, and human sequences were removed; only sequences that were mapped to arthropod and nematode clades were retained. This phylogenetic filtering was then applied to the ASV table. Finally, ASV identifications were improved by checking them against public databases via BOLD's Identification Engine and NCBI BLAST (Altschul et al. 1990). In assigning names, we sought to follow the Open Nomenclature guidelines of Sigovini et al. (2016).

Excluding library construction, which took roughly 16 work hours, HTS methods required about 40 work hours or 1.3 hours per sample.

Data resources

We sought to publish all of our data following the guidlines of Penev et al. (2017). Our reference library and scripts from producing it are available at https://github.com/mlbowser/AKTerrInvCOILib. Complete specimen and occurrence data are available via an Arctos (https://arctosdb.org/) archive at https://arctos.database.museum/archive/2017_edrr_ngs_test_records and are also available on GBIF (https://www.gbif.org/) via Arctos. Sequence data have been deposited in the NCBI Sequence Read Archive under BioProject PRJNA542936 and original, raw FASTQ files are provided in Bowser et al. (2019). Complete occurrence and identification data are provided in Suppl. material 1. Sequences of ASVs are provided in Suppl. material 4 and the ASV table is provided in Suppl. material 3.

Results

Assemblages of bark and wood-boring beetles detected by both methods were largely congruent, composed mostly of Curculionidae and Cerambycidae. High Throughput Sequencing consistently yielded a higher diversity of taxa and provided identifications mostly at the species or BIN resolution; in contrast, morphological methods yielded lower diversity, with identifications mostly at the resolution of genera.

A total of 85 unique identifications were obtained from both methods combined (Table 2, Fig. 2), including 52 species or BIN resolution identifications. Morophological methods yielded 30 unique identifications of which 4 were at the species resolution besides the three exotic species that were added and known a priori. High Throughput Sequencing yielded 59 unique identifications including 46 species or BIN resolution identifications (see phylogenetic tree, Fig. 3). None of the three exotic species that had been added to the samples were detected by HTS. Only a single species, the cerambycid Acmaeops proteus (Kirby 1837), was identified by both methods.

Identifications obtained by morphological and HTS methods.

Phylum Class Order Family Genus and Species Morphology HTS
Arthropoda Arachnida Araneae Clubionidae Clubiona kulczynskii Lessert, 1905
Arthropoda Arachnida Araneae Philodromidae Tibellus sp. BOLD:AAA7188
Arthropoda Arachnida Sarcoptiformes Compactozetidae Cepheus sp.
Arthropoda Collembola Entomobryomorpha Entomobryidae Entomobrya nivalis (Linnaeus, 1758)
Arthropoda Insecta
Arthropoda Insecta Coleoptera
Arthropoda Insecta Coleoptera Anobiidae
Arthropoda Insecta Coleoptera Cerambycidae
Arthropoda Insecta Coleoptera Cerambycidae Acmaeops proteus (Kirby 1837)
Arthropoda Insecta Coleoptera Cerambycidae Meriellum proteus (Kirby, 1837)
Arthropoda Insecta Coleoptera Cerambycidae Neospondylis sp.
Arthropoda Insecta Coleoptera Cerambycidae Neospondylis upiformis (Mannerheim, 1843)
Arthropoda Insecta Coleoptera Cerambycidae Phymatodes sp.
Arthropoda Insecta Coleoptera Cerambycidae Phymatodes dimidiatus (Kirby, 1837)
Arthropoda Insecta Coleoptera Cerambycidae Phymatodes maculicollis LeConte, 1878
Arthropoda Insecta Coleoptera Cerambycidae Plectrura sp.
Arthropoda Insecta Coleoptera Cerambycidae Plectrura spinicauda Mannerheim, 1852
Arthropoda Insecta Coleoptera Cerambycidae Stenocorus obtusus Mannerheim, 1852
Arthropoda Insecta Coleoptera Cerambycidae Tetropium sp.
Arthropoda Insecta Coleoptera Cerambycidae Tetropium cinnamopterum
Arthropoda Insecta Coleoptera Cerambycidae Tetropium fuscum (Fabricius, 1787)
Arthropoda Insecta Coleoptera Cerambycidae Xylotrechus sp.
Arthropoda Insecta Coleoptera Cerambycidae Xylotrechus longitarsis Casey, 1912
Arthropoda Insecta Coleoptera Cerambycidae Xylotrechus undulatus (Say, 1824)
Arthropoda Insecta Coleoptera Cleridae
Arthropoda Insecta Coleoptera Cleridae Thanasimus undatulus (Say, 1835)
Arthropoda Insecta Coleoptera Curculionidae
Arthropoda Insecta Coleoptera Curculionidae Cryphalus sp.
Arthropoda Insecta Coleoptera Curculionidae Cryphalus ruficollis Hopkins, 1915
Arthropoda Insecta Coleoptera Curculionidae Dendroctonus sp.
Arthropoda Insecta Coleoptera Curculionidae Dendroctonus rufipennis Kirby, 1837
Arthropoda Insecta Coleoptera Curculionidae Dolurgus sp.
Arthropoda Insecta Coleoptera Curculionidae Dolurgus pumilus (Mannerheim, 1843)
Arthropoda Insecta Coleoptera Curculionidae Dryocoetes sp.
Arthropoda Insecta Coleoptera Curculionidae Dryocoetes affaber Leconte, 1876
Arthropoda Insecta Coleoptera Curculionidae Dryocoetes autographus (Ratzeburg, 1837)
Arthropoda Insecta Coleoptera Curculionidae Hylurgops sp.
Arthropoda Insecta Coleoptera Curculionidae Hylurgops rugipennis (Mannerheim, 1843)
Arthropoda Insecta Coleoptera Curculionidae Ips perturbatus (Eichhoff, 1869)
Arthropoda Insecta Coleoptera Curculionidae Ips typographus (Linnaeus, 1758)
Arthropoda Insecta Coleoptera Curculionidae Orthotomicus sp.
Arthropoda Insecta Coleoptera Curculionidae Orthotomicus caelatus (Eichhoff, 1868)
Arthropoda Insecta Coleoptera Curculionidae Pissodes sp.
Arthropoda Insecta Coleoptera Curculionidae Pityophthorus sp.
Arthropoda Insecta Coleoptera Curculionidae Polygraphus sp.
Arthropoda Insecta Coleoptera Curculionidae Polygraphus rufipennis (Kirby, 1837)
Arthropoda Insecta Coleoptera Curculionidae Pseudips concinnus (Mannerheim, 1852)
Arthropoda Insecta Coleoptera Curculionidae Rhyncolus sp.
Arthropoda Insecta Coleoptera Curculionidae Trypodendron sp.
Arthropoda Insecta Coleoptera Curculionidae Trypodendron lineatum (Olivier, 1795)
Arthropoda Insecta Coleoptera Elateridae
Arthropoda Insecta Coleoptera Elateridae Idolus debilis (LeConte, 1884)
Arthropoda Insecta Coleoptera Elateridae Liotrichus sagitticollis (Eschscholtz, 1829)
Arthropoda Insecta Coleoptera Elateridae Pseudanostirus hoppingi (Van Dyke, 1932)
Arthropoda Insecta Coleoptera Eucnemidae Epiphanis cornutus Eschscholtz, 1829
Arthropoda Insecta Coleoptera Melandryidae
Arthropoda Insecta Coleoptera Melandryidae Serropalpus sp.
Arthropoda Insecta Coleoptera Melandryidae Serropalpus substriatus Haldeman, 1848
Arthropoda Insecta Coleoptera Ptinidae
Arthropoda Insecta Coleoptera Ptinidae Hemicoelus carinatus (Say, 1823)
Arthropoda Insecta Coleoptera Ptinidae Microbregma emarginatum (Duftschmid, 1825)
Arthropoda Insecta Coleoptera Staphylinidae Placusa incompleta Sjöberg, 1934
Arthropoda Insecta Coleoptera Zopheridae Lasconotus sp.
Arthropoda Insecta Diptera Anthomyiidae Hydrophoria lancifer (Harris, 1780)
Arthropoda Insecta Diptera Canthyloscelidae Synneuron decipiens Hutson, 1977
Arthropoda Insecta Diptera Chironomidae Allocladius sp. BOLD:AAH3022
Arthropoda Insecta Diptera Muscidae Coenosia conforma Huckett, 1934
Arthropoda Insecta Diptera Muscidae Phaonia errans (Meigen, 1826)
Arthropoda Insecta Diptera Muscidae Phaonia luteva (Walker, 1849)
Arthropoda Insecta Diptera Muscidae Spilogona sororcula (Zetterstedt, 1845)
Arthropoda Insecta Diptera Mycetophilidae Exechia sp. BOLD:ACO2323
Arthropoda Insecta Diptera Phoridae Gymnophora subarcuata Schmitz, 1952
Arthropoda Insecta Diptera Phoridae Phora sp. BOLD:AAU5644
Arthropoda Insecta Diptera Sciaridae Sciaridae sp. BOLD:AAP9896
Arthropoda Insecta Diptera Tipulidae Prionocera turcica (Fabricius, 1787)
Arthropoda Insecta Diptera Tipulidae Tipula platymera Walker, 1856
Arthropoda Insecta Diptera Xylophagidae Xylophagus fulgidus Webb, 1979
Arthropoda Insecta Hemiptera Acanthosomatidae Elasmostethus interstinctus (Linnaeus, 1758)
Arthropoda Insecta Hemiptera Pentatomidae Halyomorpha halys Stål, 1855
Arthropoda Insecta Hymenoptera Ichneumonidae Ichneumoninae sp. BOLD:AAU8831
Nematoda Chromadorea
Nematoda Chromadorea Rhabditida
Nematoda Chromadorea Tylenchida
Nematoda Chromadorea Tylenchida Sphaerulariidae
Nematoda Secernentea Tylenchida Aphelenchoididae Bursaphelenchus sp.
Figure 2.  

Comparison of identifications based on morphological and HTS methods. Columns are samples and rows are identifications. White: non-detections. Blue: morphological detections. Red: HTS detections. Purple: detections by both methods.

Figure 3.  

Phylogenetic tree of HTS sequences generated using qiime phylogeny align-to-tree-mafft-fasttree, accepting default parameters. The graphic was rendered using the Interactive Tree Of Life (Letunic and Bork 2019). An interactive version of this tree is available at https://itol.embl.de/tree/1641591522462921555104654. Colors hightlight major taxonomic groups.

In many cases, coarser identifications at genus resolution from the morphology-based dataset corresponded with species identifications from the HTS data. Molecular identifications of Neospondylis upiformis (Mannerheim, 1843), Dolurgus pumilus (Mannerheim, 1843), and Orthotomicus caelatus (Eichhoff, 1868) corresponded exactly with identifcations of these genera based on morphology. Identifications of Thanasimus undatulus (Say, 1835) and Hemicoelus carinatus (Say, 1823) obtained by HTS corresponded to morphological identifications of Cleridae and Ptinidae, respectively. More generally, HTS identfications of Cryphalus ruficollis Hopkins, 1915; Dendroctonus rufipennis Kirby, 1837; Dryocoetes affaber Leconte, 1876; Hylurgops rugipennis (Mannerheim, 1843); and Trypodendron lineatum (Olivier, 1795) mostly corresponded to morphological identifications of these genera.

In the three samples where bycatch was included, 14 species or BINs of flies, the acanthosomatid bug Elasmostethus interstinctus (Linnaeus, 1758), and an ichneumonid wasp identified as Ichneumoninae sp. BOLD:AAU8831 were also identified. Detections of the targeted scolytine and cerambycid beetles were not notably reduced in these samples.

High Throughput Sequencing also yielded detections of nematodes including an ASV identified as Bursaphelenchus sp.

From the perspective of work hours required by our team, the 1.3 hours per sample for processing HTS data was 65% of the 2.1 hours per sample required for identification using morphology.

Discussion

Although HTS methods outperformed morphological identfication methods in this case in terms of taxonomic resolution of identifications, it should be noted that only the initial steps of a typical EDRR processing workflow were followed. If specimens had been sent out to taxonomic specialists, then most bark and wood-boring beetle specimens would have received species-resolution identifications. Obtaining expert identifications would have also substantially increased processing costs and processing time.

In terms of processing time, HTS methods outperformed morphological methods, with HTS methods requiring 65% of the work hours needed for the morphological identifications. However, this does not take into account processing time at the sequencing lab. In this example, results from the morphological dataset were available 4 days after processing was commenced by a team of workeers while HTS methods required 1 day for shipping, 73 days for processing at the sequencing lab, and about 5 work days for metagenomic processing. Both methods returned results before the next growing season, but morphological methods would have allowed for a rapid response in the fall almost 3 months before the results from HTS were available. However, in more recent sampling efforts comparable sequencing has required only 4 weeks from the sequencing lab. Also, HTS processing time per sample should decrease as the sample size is increased because much of the pipeline can be run in parallel.

We did not attempt to quantify rates of false presences and false absences in the HTS dataset, but it was clear that some taxa observed in the morphological dataset and the three known exotic species in particular were not detected by HTS. We were not surprised that the preserved specimens of Ips typographus and Tetropium fuscum were not detected because these specimens may have had degraded DNA and they represented small portions of the samples. We had expected that the live specimen of Halyomorpha halys, a relatively large insect where DNA degredation should not have been a problem, would have been detected. Potential causes for this non-detection include a failure to homogenize the sample completely or failure to amplify sequences of this species due to primer bias.

In any single primer pair, differences in binding to DNA templates lead to amplification biases, affecting both read abundances and detections of species so that any single primer set will lead to detections of a subset of species (Hajibabaei et al. 2019, Elbrecht and Leese 2017). The mlCOIlintF/HCO2198 primer pair we used amplifies well across a broad range of arthropod taxa (Leray et al. 2013, Brandon-Mong et al. 2015), making it an appropriate choice for surveillance monitoring (as defined by Nichols and Williams 2006) of arthropods. In future efforts we would select the mlCOIlintF/jgHCO2198 primer pair, which has a more degenerate reverse primer and amplifies well across a broader range of arthropod taxa than the mlCOIlintF/HCO2198 pair (Elbrecht and Leese 2017). To optimize EDRR efforts for Curculionidae and Cerambycidae, an appropriate next step would be to compare performance of additional primer sets for amplyfying DNA of these target groups.

In future applications of HTS for biomonitoring we would consider using the SCVUC COI metabarcode pipeline (https://github.com/EcoBiomics-Zoobiome/SCVUC_COI_metabarcode_pipeline) used by Hajibabaei et al. (2019) instead of the QIIME 2 pipeline we employed.

Notes on selected taxa

Placusa incompleta Sjöberg, 1934 (Coleoptera: Staphylinidae) is a Palearctic rove beetle species adventive in North America was first reported in North America by Klimaszewski et al. (2001) and is now present on both coasts from Washington and British Columbia to Quebec (Klimaszewski et al. 2016). Our detection of P. incompleta represents a new record for Alaska. Larvae and adults of Placusa species are associated with bark beetle galleries in wood, where they feed on fungi (Klimaszewski et al. 2001).

Hydrophoria lancifer, a Palearctic species, was introduced into eastern North America in the 1920s and since that time has become established on both coasts, ranging from British Columbia and Washington to New York and Newfoundland (Griffiths 1998, GBIF.org 2019). Our detection of H. lancifer represents a new record for Alaska. Larvae of H. lancifer develop in dung (Komzáková 2012). Adults of H. lancifer have been observed to prey on simuliid larvae (Werner and Pont 2006) and to feed on tree sap (Wolton and Luff 2016), aphid honeydew (Tiensuu 1936), and nectar (Pont 1993).

Even though our sequence of a Tibellus was 100% similar (p-dist) to sequences of Tibellus maritimus (Menge, 1875) and Tibellus oblongus (Walckenaer, 1802) on BOLD, this sequence could not be unequivocally identified as one species or the other becuase these two species are not separable by their COI sequences (Blagoev et al. 2015, Astrin et al. 2016) and both species occur in Alaska.

Phaonia luteva (Walker, 1849) was recently resurrected as a species distinct from Phaonia errans (Meigen, 1826) by Renaud et al. (2012). Both Phaonia errans and P. luteva (as Phaonia errans luteva) were previously known from Alaska (Huckett 1965).

Conclusions

Because our HTS methods failed to detect Ips typographus and Tetropium fuscum, two species of concern for our EDRR monitoring program, we do not recommend wholly replacing our current morphological monitoring methods with HTS methods. Even though our morphological identifcations were mostly at the taxonomic resolution of genera, we believe that we would be more likely to detect certain exotic species by using morphology than by using HTS. However, HTS methods would be especially appropriate as a complement to current EDRR methods for survellaince monitoring. Our detections of two new exotic species for Alaska highlight the effectiveness of HTS methods for detecting species that were not being looked for. In addition, because the results of both methods were consistent overall, HTS methods would be appropriate for increasing sample size without greatly increasing the time required to process specimens, especially if bycatch is included, removing the time-consuming manual sorting step.

Acknowledgements

We are grateful to the Yeti team and USGS for use of the Yeti Cluster. We thank Derek Sikes for helpful comments that improved an early draft of this manuscript.

Funding program

This project was funded by the USDA Forest Service, Forest Health Protection, Special Technology Development Program.

References

Supplementary materials

Suppl. material 1: Trap data, morphological identifications, molecular identifications, and resulting occurrence data 
Authors:  Matthew Bowser, Elizabeth E. Graham
Data type:  occurences
Brief description: 

This file contains the specimen data from the original trap samples, morphological identifications, molecular identifications, and resulting occurrence data.

Suppl. material 2: RTL Genomics Data Analysis Methodology 
Authors:  RTL Genomics
Data type:  methodology
Suppl. material 3: ASV Table 
Authors:  Matthew L. Bowser
Data type:  ASV by sample matrix
Brief description: 

Amplicon sequence variant table in standard text format

Suppl. material 4: ASV Sequences 
Authors:  Matthew L. Bowser
Data type:  metagenomic
Brief description: 

Sequences of amplicon sequence variants in FASTA format.