Arachnida

To assess the feasibility of simultaneously detecting multiple bat species in a collection of guano taken from a roost, we employed the NGS universal tail, dual-indexed amplicon sequencing approach of Colman et al. [42 (link)] for use with Illumina short-read sequencers. We vortexed each pooled guano sample taken from roosts in abandoned mines (sampling described above) into a semi-slurry, then subsampled 0.22g into a 1.5 mL centrifuge tube and performed the TE soak mentioned above, and then extracted DNA via QiaAmp Fast Stool Mini Kit. Using samples of known multiple species composition, we also tested whether subsampling four times from the same conical resulted in more bat species detected.
We performed a 2-step target-specific PCR on a MJ Research PTC-200 thermocycler, using our optimal unlabeled SFF primer pair (see Results below) in the first reaction followed by a second reaction with incorporated universal tails (UT1 on 5’ end of forward primer: ACCCAACTGAATGGAGC; UT2 to 5’ end of reverse primer: ACGCACTTGACTTGTCTTC). The purpose of the 2-step PCR was to minimize potential primer bias from the incorporated universal tails as well as to prevent extensive primer dimer from solely using the labeled primers, which reduces extraneous purification steps in library preparation, increases robustness of read count, and does not hinder taxonomic recovery. Fecal DNA pools were diluted to 1:5 with molecular grade water and PCR conditions were as described above, aside from an addition of 20 ng non-acetylated bovine serum albumin (Ambion Ultrapure BSA). The second PCR incorporating the UT-labeled primers was scaled up to a 20 μL reaction with 1 μL of undiluted PCR product from the first step, 0.1 U/μL Platinum Taq, and 35 cycles. The resulting amplicons from this initial PCR were employed as template in a subsequent Illumina (MiSeq) extension PCR using unique Illumina indices containing sequences complementary to the universal tails [42 (link)].
We removed primer and adapter sequences from Illumina reads with cutadapt 1.6 [68 ]. Initial sequence processing was performed with commands in mothur [64 (link)], and forward and reverse reads were assembled into contigs (make.contigs). We removed sequences containing ambiguous characters, sequences of inappropriate length (screen.seqs), duplicate sequences (unique.seqs), and chimeras (chimera.uchime) from the dataset [64 (link)]. Taxonomic classification of sequences was performed in QIIME [46 (link)] using the RDP Classifier [45 (link)] with a confidence threshold of 0.8. We found this threshold to be an acceptable tradeoff between the number of correctly classified sequences and the risk of false positives (see Results).
As an additional exercise to determine whether our SFF primers have the undesirable capacity to instead PCR amplify DNA of dietary items or arthropods living in the guano, we subjected our pooled fecal samples from the eight mines to custom reference libraries of the four classes of Arthropoda. Sequences and taxonomic information were downloaded from the BOLD database in February 2016 (search terms Insecta, Arachnida, Chilopoda, and Diplopoda), and the requisite FASTA and taxonomy files were created in the same manner as our custom bat reference library and tested with RDP Classifier at 0.8 confidence threshold.

DNA was isolated using the Qiagen DNeasy Blood and Tissue Kit, following the spin-column protocol. Quality of the isolation was estimated using a NanoDrop 2000c Spectrophotometer, and samples were repeated where possible if the 260:280 nm UV absorbance ratio fell outside the range of 1.4 to 2.2. For most taxa 1 to 4 legs were used for DNA extraction, but the entire prosoma was used for Padillothoraxbadut (specimen d548) and Helpisminitabunda (specimen NZ19-9152). For the target enrichment UCE sequencing, dual-indexed TruSeq-style libraries were prepared following methods previously used in arachnids (e.g., Starrett et al. 2017 (link); Derkarabetian et al. 2018 (link); Hedin et al. 2018 (link); Kulkarni et al. 2019 (link)). Targeted enrichment was performed using either the myBaits Arachnida 1.1Kv1 (Arbor Biosciences; Faircloth 2017 (link); Starrett et al. 2017 (link)) or the Spider 2Kv1 kit (Arbor Biosciences; Kulkarni et al. 2019 (link)) following the myBaits v4.01 protocol (https://arborbiosci.com/wp-content/uploads/2018/04/myBaits-Manual-v4.pdf). Libraries were sequenced on partial lanes of Illumina NovaSeq 6000 S4 runs with 150 bp paired end reads. To the resulting set of reads we added those from two amycoid taxa, Breda and Colonus, obtained by Maddison et al. (2020) (link), to assist as outgroups. Raw demultiplexed reads were processed with Phyluce version 1.6 (Faircloth 2016 (link)), quality control and adapter removal were conducted with the Illumiprocessor wrapper (Faircloth 2013 ), and assemblies were created with SPAdes version 3.14.1 (Nurk et al. 2013 (link)), using the meta option, at default settings.
From among the contigs thus assembled, those matching particular UCE probes were pulled out using the Phyluce pipeline at default settings. Because some taxa were captured using the arachnid probeset (outgroups Attulus, Breda, Colonus, Salticus), and others using the spider probeset (remaining outgroups, and all baviines), a blended probeset file was needed to best pull out UCE contigs, because each of the arachnid and spider probesets includes loci not included by the other. Kulkarni et al.’s (2019) (link) spider probeset includes (i) some of Starrett et al.’s (2017) (link) arachnid probes directly, (ii) others for the same loci but modified to target spiders better, and (iii) others for new loci. Because Kulkarni et al. do not identify probes of the second category as such, we sought to identify whether spider probes are orthologous to arachnid probes. We then deleted from the probeset file those arachnid probes matching spider probes, as including duplicate homologs reduces data recovery (contigs matching two probes are removed by Phyluce for being problematical). To determine homology, contigs from 18 diverse species in the Salticinae, 12 captured with arachnid probes, 6 with spider probes, were each matched against both arachnid and spider probesets. Any instance of a contig matching both a spider probe and arachnid probe, as assessed using a script examining .lastz files, was taken as indicating homology between the probes. Arachnid probes that showed no such hint of homology to spider probes were then added to Kulkarni et al.’s (2019) (link) spider probeset to generate the blended probeset (see Suppl. material 1). The spider probeset includes 15015 probes and probe parts; the arachnid probeset, 14799; blended, 25689. The efficacy of the blended probeset can be seen in the numbers of loci recovered in the baviine dataset reported here: the arachnid probset pulled out on average 134 loci from spider-enriched taxa and 411 from arachnid-enriched taxa; the spider probeset pulled out on average 1118 and 113 respectively; the blended probeset pulled out on average 1123 and 415. Nonetheless, many of the UCE loci recovered from the arachnid-enriched taxa were only among those taxa; this explains why many were subsequently deleted when a filter for occupancy among ingroups (see below) was applied.
Recovered UCE loci were aligned with MAFFT (Katoh and Standley 2013 (link)) and trimmed with Gblocks (Castresana 2000 (link); Talavera and Castresana 2007 (link)), using –b1 0.5, –b2 0.5, –b3 10, –b4 4 settings in the Phyluce pipeline. Among the loci recovered, those with fewer than 6 taxa total or fewer than 3 ingroups were deleted. As in the analysis of Maddison et al. (2020) (link), loci were also deleted over concerns about paralogy if their gene tree showed a very long branch, at least 5× longer than the second longest branch.

Distribution maps, created using ArcMap and ArcGIS, are presented for each species in New Brunswick. Every species is cited with current distribution in Canada and Alaska, using abbreviations for the state, provinces, and territories. New records for New Brunswick are indicated in bold under Distribution in Canada and Alaska. The following abbreviations are used in the text:PageBreak
Acronyms of collections referred to in this study are as follows:
AFC Atlantic Forestry Centre, Natural Resources Canada, Canadian Forest Service, Fredericton, New Brunswick, Canada
CNC Canadian National Collection of Insects, Arachnids and Nematodes, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada
CCC C. Chantal Collection, Varennes, Quebec, Canada
NBM New Brunswick Museum, Saint John, New Brunswick, Canada
RWC Reginald Webster Collection, Charters Settlement, New Brunswick, Canada

The Artemisia vulgaris L. used in this experiment was collected from Tangyin, Henan Province. A. vulgaris L. belongs to the Artemisia genus of the Compositae family. It is a perennial herb or slightly semishrubby plant with a strong fragrance. The taproot of the plant is obvious and slightly long, with many lateral roots, and the plant is 1.5 cm in diameter. This plant often has recumbent rhizomes and vegetative shoots. The stem of the plant grows singly or clustered, with distinct longitudinal ribs; their color is brown or tawny brown, the stem is slightly woody at the base, grassy above, with a few short branches, and the length is 3–5 cm. The stems and branches have gray arachnid-like hair. The leaves are thick, covered with gray–white pubescence above, with white glandular spots and small concave spots, and densely covered with gray–white spider silky hairs on the abaxial surface; the basal leaves have long stalks, fading at the anthesis. The shape of the lower stem leaves is suborbicular or broadly ovate, pinnate and deeply lobed, with 2–3 lobes on each side; the lobes’ shapes are oval or obovate and long elliptic, with 2–3 small teeth on each of them. The main and lateral veins on the back of the stem are dark brown or rusty, and the length of petiole is 0.5–0.8 cm. Middle leaves are ovate, triangular-ovate or subrhomboid, with one (to two) pinnate deeply lobed to hemiclobed, with 2 or 3 lobes on each side; the shape of lobes is ovate, ovate-lanceolate or lanceolate, the leaf base is broadly cuneate tapering into a short petiole, and the leaf veins are conspicuous, bulging on the back, with the leaf stalk base usually without false stipes or very small false stipes. Upper leaves and bracts are pinnatifid lobed, deeply 3-lobed, 3-lobed, or not divided, but the shape is elliptic, long elliptic lanceolate, lanceolate or linear-lanceolate.
The shape of the capitulum is oval, sessile or subsessile, with several to more than 10 of them arranged in small spikes or compound spikes on branches, and usually reconstituted in narrow pinnacle-shaped panicles on the stems, with capitulum decumbent after flowers. Involucral bracts have 3–4 layers, arranged in a imbricate shape, the outer involucral bracts are small, in ovate or narrowly ovate shapes, abaxially densely covered with grayish white silky spider hair, and the edge is membranous; the middle involucral bracts are longer than the outer ones, with a long ovate shape, abaxially covered with silky spider silky hair, and the inner involucral bracts are thin, abaxially nearly glabrous. The inflorescence torr is small, (6–10 in female flowers). The purple corolla is narrowly tubular, 2-lobed on the leaf, and the style is slender, 2-forked at the apex. The inflorescence of bisexual flowers is 8–12, the corolla is tubular or goblet shaped, with glandular spots on outside, and the leaves are purple. The anthers are narrowly linear, with long triangular and pointed apical appendages and inconspicuous pinnacles on the base of anther. The achene has a long ovate or oblong shape. The flowering and fruiting period is from July to October.

To test hypotheses about students’ prior knowledge of biodiversity and the taxonomic expertise of team members, we gathered data before the course on their familiarity with eleven taxonomic groups. Using a survey, we asked students to estimate their familiarity with mammals, birds, reptiles/amphibians, fish, insects, arachnids and relatives, crustaceans, mollusks, “all the worms,” cnidarians, and sponges. For each taxonomic group, students assessed their knowledge on a scale of 1 to 10, corresponding to “know little” to “know lots!”, respectively. In total, 177 students filled out the survey in 2021, although given that some of these dropped the course, in the end, we had 165 (of 180) students with data on taxonomic expertise (distribution of responses in [50 (link)]). Two metrics were taken from the biodiversity knowledge surveys: an individual’s “total expertise,” that is, a summary of scored expertise across the eleven taxa, and a measure of their diversity of knowledge using a commonly used measure of diversity from the ecology literature—the Shannon index (Sum(pi(LNpi)) across all taxonomic groups), which adjusts for the evenness of knowledge across taxonomic groups, with higher values representing more consistent knowledge across taxonomic groups.
To test the role of individual expertise versus what students were learning from their peers, we compared the effects of their individual expertise versus that of team members with whom they interacted after a brainstorming assignment (i.e., group interactions were expected to affect individual brainstorming exercises later in the semester after multiple assignment discussions). Student teams were constructed prior to the first day of the course in a semi-systematic, semi-randomized way. Expertise was first sorted within each taxonomic group, and then student teams were assigned (up to 30 total teams to distribute “experts” in different taxonomic groups across student teams). Otherwise, sorting into student teams was performed blind to student identity. We were sure to account for variation across teams in students missing taxonomic score data (these individuals were spread evenly across student teams 17–30). Our method of constructing teams seemed to work well, as there were no significant differences in individual total expertise scores across the 30 student teams (total expertise: F_29,136 = 0.47, p = 0.98), nor was there a difference in Shannon diversity scores across student teams (F_29,136 = 0.71, p = 0.86).
Student teams (breakout groups) were the same throughout the semester. Students went into these teams at least once per class, sometimes 2–3 times in a class session (of 75 min). During the first breakout session on the first day of the course, students were asked to complete a team norming exercise where they shared some information about their backgrounds and worked out guidelines for interactions over Zoom. Other team activities included sharing ideas from pre-class assignments, designing a composite “sensory robot” (for a team prize), and completing statistical problems together.
In our analyses, we focused on an individual’s incoming expertise scores and how they influenced their response to the first individual brainstorming assignment (“force”), which came at the beginning of the course before team interactions. We were also interested in whether students could learn from team members with different taxonomic expertise, which was predicted to broaden the diversity of ideas when completing individual brainstorming assignments later in the semester. To do this, we looked at an individual’s expertise scores relative to their team members (student score minus the average of the other team members) to see how this influenced responses on later assignments that occurred after team interactions (“grief”, “anxiety”, “movement”, “nutrition”). Thus, positive values indicated that an individual self-reported higher expertise than their team members, while negative values indicated that an individual self-reported lower expertise than their team members.