DOI: 10.5061/dryad.gqnk98snc
title: Effects of consumer surface sterilization on diet DNA metabarcoding data of terrestrial invertebrates in natural environments and feeding trials

description: These are data and code from a study examining the potential for surface
 contamination to influence diet DNA metabarcoding datasets when DNA is
 sequenced from full body parts (in this case, the opisthosomas of spider
 individuals). These datasets include the raw sequencing data, all
 downstream datasets, and taxonomic assignments collected from database
 searches on BOLD and GenBank (accessed 2019). The code includes code to
 reproduce all bioinformatics (merge, filter, match to taxonomies, rarefy,
 sort) as well as all statistics and figures generated from analyses. Raw
 data are from DNA extractions of predator gut regions (opisthosomas) and
 amplification of the CO1 gene using PCR. The predator species is
 Heteropoda venatoria collected individually with sterilized implements and
 either the diet sequences from their natural diets were extracted or their
 diets following feeding spiders in a feeding trial. 
description: <strong>Field site and collections</strong><br>
 Specimens were collected on Palmyra Atoll, Northern Line Islands, USA
 (5º53’ N, 162º05’W). <em>Heteropoda venatoria </em>is a
 generalist, active hunting spider species (<em>Heteropoda
 venatoria</em>). All individuals were stored individually in
 sterilized containers. <strong>Natural
 environment consumer collection</strong><br> In 2015, we
 collected consumers (n = 47) from natural environments, which had fed on
 available diet items and come into contact with environmental surfaces, to
 test if DNA metabarcoding detects diet DNA effectively. We froze all
 individuals at -80ºC immediately following collection until surface
 sterilization and DNA extraction in 2019.
 <strong>Feeding trial consumer set-up and
 feeding</strong><br> In 2017, we conducted laboratory trials
 (n = 26) to test if DNA metabarcoding detects DNA from diet items offered
 in a contained environment. We created feeding environments from one-liter
 yogurt containers with holes for air transfer, and placed one <em>H.
 venatoria </em>in each container. After 12 hours, we placed one
 large grasshopper (<em>Oxya japonica</em>) in each container
 and left all containers for 24 hours. We then froze (-20ºC) each
 <em>H. venatoria</em> that had killed the grasshopper (n = 25,
 consumption was not easily detectable and thus not considered in
 analyses). We cleaned all containers between trials with 10% bleach
 solution. <strong>Surface
 Sterilization</strong> To test surface
 sterilization’s efficacy at removing possible contaminants, we used a
 surface sterilization treatment on ~half the consumers for each set: those
 collected from the natural environment, and those subjected to controlled
 feeding trials.  We submerged and stirred each (whole) consumer in 10%
 commercial bleach by volume for two minutes and washed each in deionized
 water for two minutes. Similar bleach submersion leads to undetectable DNA
 degradation in similar soft-exoskeleton consumers. Natural environment
 consumers (2015) had been frozen at -80ºC since collection; we surface
 sterilized these consumers in a sterilized laminar flow hood in 2019 just
 before DNA extraction (n = 22 surface sterilized, n = 25 not surface
 sterilized). We surface sterilized feeding trial consumers (2017) in the
 lab on the atoll in 2017 following freezing at -20ºC, then stored each in
 individual vials of 95% ethanol in a -20ºC freezer until DNA extraction
 (no -80ºC freezer was available at the field station that year) (n = 10
 surface sterilized; n = 14 not surface sterilized). Prior to DNA
 extraction, all samples dried for 1-3 hours in a sterilized laminar flow
 hood and the opisthosoma (hind gut region) was removed using a sterilized
 scalpel.  Between all steps, tools were sterilized with either ethanol and
 flame (scalpels and forceps) or 10% bleach (surfaces) between handling
 each individual.  <strong>DNA extraction and
 removal of consumer DNA with Ampure XP beads</strong><br> We
 extracted DNA from each consumer following a modified CTAB extraction
 protocol. We quantified DNA using a Qubit (Invitrogen) fluorometer with
 the high sensitivity double-stranded DNA quantification kit. We followed
 Krehenwinkel et al. (2017) to isolate a proportion of lower molecular
 weight DNA with Ampure XP beads prior to PCR. We diluted each DNA sample
 to 20ng/microliter (creating a total sample volume of 40microLiter), mixed
 each sample using Ampure XP beads (0.75x bead-to-DNA ratio), and kept the
 supernatant. With the supernatant, we precipitating the DNA pellets with
 isopropanol and 5M potassium acetate, and washed DNA pellets with ethanol.
 We quantified this cleaned DNA again using a Qubit fluorometer and diluted
 all samples to 10ng/microLiter prior to PCR steps. All DNA pellets were
 stored in and diluted with TE buffer. <strong>PCR
 amplification, library preparation, and
 sequencing</strong><br> We amplified the CO1 gene with general
 metazoan primers (m1COIF/Fol-degen-rev). We performed all PCR preparation
 steps in a UV-sterilized biosafety cabinet. We used PCR reaction volumes
 of 25microLiter (9microLiter nuclease free water, 12.5microLiter GoTaq
 Green Master Mix (Promega Corp.), 1.25 microLiter of each of the primers
 (at 10mM), and 1 microLiter of DNA template (at 10ng/microLiter)). We ran
 each sample in duplicate along with duplicated negative samples each PCR
 run. PCR reactions are as follows: initial denaturation step at 95C for 3
 minutes, then 35 cycles of: 1) 95ºC for 30 seconds, 2) 46ºC for 30
 seconds, and 3) 72ºC for one minute, followed by a final 5 minutes at
 72ºC. We cleaned PCR products with Ampure XP beads at a 0.8x bead-to-DNA
 ratio and resuspended from beads using a 10mM TRIS buffer. 
 We attached Illumina index primers with an additional PCR step
 following standard protocols (Nextera XT Index Kit v2, Illumina, 2019). We
 combined duplicate samples for which both duplicates successfully
 amplified and diluted to a concentration of 5nM. We multiplexed all
 samples with one negative control and two fungal clone positive controls
 (GenBank accession numbers: MG840195 and MG840196). We submitted
 multiplexed samples for sequencing at the University of California, Santa
 Barbara Biological Nanostructures Laboratory Genetics Core. Samples were
 run on an Illumina MiSeq platform (v2 chemistry, 500 cycles, paired-end
 reads) with a 15% spike-in of PhiX. Following sequencing, samples were
 demultiplexed using Illumina’s bcl2fastq conversion software (v2.20) at
 the Core facility. <strong>Sequence merging,
 filtering, and clustering with UNOISE3</strong><br> We merged,
 filtered (max ee  = 1.0), and denoised (clustered) our sequences around
 amplicon sequence variants (ASVs) using the UNOISE3 algorithm (unoise3
 command in the open-source USEARCH 32-bit version 11.0.667). Prior to
 denoising with UNOISE3, we used cutadapt (version 1.18) to remove primers
 from each sequence. We also repeated analyses with the DADA2 algorithm run
 through R (dada2 package version 1.1.14.0) and with a data cleaning step
 run through BBSplit to remove consumer DNA prior to ASV assignment
  (because ASV assignment is abundance-sensitive). We considered analyses
 from the UNOISE3 algorithm only because UNOISE3 assigned more sequence
 reads to positive controls than DADA2 (on average, 3x as many reads per
 positive control) and the cleaning step paired with either DADA2 or
 UNOISE3 did not increase potential diet DNA detection. 
 We created a list of unique ASVs and a matrix of ASV abundances
 across samples. We matched ASVs to taxonomies in the GenBank and BOLD
 databases. For GenBank, we used BLAST (version 2.7.1) with the blastn
 command for taxonomic assignment of each ASV using the computing cluster
 at UC Santa Barbara, comparing against the GenBank nucleotide database
 with an evalue of 0.01 (downloaded on November 20, 2019). We visualized
 and exported taxonomic alignment using MEGAN Community Edition (version
 6.18.0), using default settings (LCA=naïve, MinScore = 50.0, MaxExpected
  = 0.01, TopPercent = 10.0, MinSupportPercent = 0.05) and selecting the
 subtree with all possible diet items for this species (Kingdom: Animalia,
 Clade: Bilateria). For taxonomies which were not assigned below the order
 level (n =24), we submitted each ASV individually to the BLAST Basic Local
 Alignment Search Tool and assigned them a family based on the best
 sequence match in the database, given that the top ten database matches
 were from the same family. For BOLD taxonomic assignment, we used the BOLD
 IDEngine of the CO1 gene with Species Level Barcode Records (accessed
 February 5-16, 2020; 3,825,490 Sequences, 216,704 Species, and 95,537
 Interim Species in database) to match each ASV list to taxonomies. We
 combined taxonomic assignments from both programs and discarded taxonomic
 assignments that were mismatched at the family level or higher.
 <strong>Detection of potential diet
 items</strong><br> For consumers from both the natural
 environment and feeding trials, we asked whether surface sterilization
 altered detection of potential diet items for each consumer. For natural
 environment consumers, we examined all potential diet items (which could
 represent either diet or surface contaminants). For feeding trial
 consumers, we focused our detection analysis on the offered diet item we
 provided the consumers in the feeding trial environment (O. japonica,
 which all consumers were observed to have killed, but not necessarily
 ingested). We rarefied based on the sample with the lowest sequencing
 depth which had been sequenced with 95%+ sampling completeness based on
 iNEXT (version 2.0.20) interpolation and extrapolation methods (16,004
 reads for natural environment and 55,205 reads for feeding trial
 consumers). We rarefied using the rrarefy() function in the vegan (version
 2.5.6) package in R and rarefied the field and lab consumers
 separately.  We then selected all ASVs that matched
 potential diet items for the natural environment consumers (Kingdom:
 Animalia; Clade: Bilateria, excluding consumer DNA) and just the offered
 diet item for the feeding trial consumers (including species:
 <em>Oxya japonica</em>, genus: <em>Oxya</em>,
 family: Acrididae, excluding those which only matched to order). Because
 the consumer species <em>H. venatoria</em> is the only species
 in the family Sparassidae on Palmyra Atoll, removing consumer DNA meant
 excluding all ASVs that received a family-level taxonomic assignment of
 “Sparassidae”. As all ASVs received family-level taxonomic assignment, we
 pooled ASVs that matched at the family level into one taxonomic unit using
 cumulative read abundance (i.e. all ASVs matched to diet family A were
 pooled into diet family A taxonomic unit), a practice common in diet
 metabarcoding and predator-prey interaction studies.
 <strong>Statistical analyses</strong><br> For
 potential diet detection and rarefied abundance in both sets of consumers
 (natural environment and feeding trial) we used generalized linear models
 to assess the effect of surface sterilization treatment. For prey
 detection, we used all potential (natural environment) or offered (feeding
 trial) diet item detection (presence-absence per sample) as the response
 variable in the full model with surface sterilization as a fixed effect
 and a binomial distribution. For rarefied diet abundance, we only assessed
 consumers for which we had detected diet and not those with no diet
 detection (n = 33 of 37 for natural environment; n = 14 of 19 for feeding
 trials). For this model, we treated the number of all potential (natural
 environment) or offered (feeding trials) diet DNA reads per sample as the
 response variable, surface sterilization treatment as a fixed effect,
 total read abundance of the sample (constant across all) as an offset
 term, and a Poisson or negative binomial distribution (to correct for
 overdispersion when needed). We assessed differences in per sample
 potential diet richness among sterilization treatments for the natural
 environment consumers using generalized linear models with the number of
 potential diet items per sample as the response variable (both
 family-level taxonomic units or ASVs), surface sterilization treatment as
 the fixed effect and a Poisson or negative binomial distribution (to
 correct for overdispersion when needed). We assessed differences in
 potential diet item composition with family-level taxonomic units between
 surface sterilized and unsterilized consumers using a presence-absence
 PERMANOVA model fit with a binomial mixed effects model with surface
 sterilization treatment as a fixed effect, a random intercept term for
 potential diet item, and a random slope term for surface sterilization
 treatment. We also assessed ASV composition as a representation of
 potential prey composition using a canonical correspondence analysis (CCA)
 with surface sterilization as a predictor variable.   
 For all generalized linear models and mixed models, we performed
 model selection by comparing the full model (including the fixed effect of
 surface sterilization treatment) to a null model without this effect. All
 models were called in the glmmTMB package (version 1.0.0) in R (version
 3.6.1) We chose the best fitting model based on size corrected AIC values
 (MuMIn package version 1.43.15). For responses for which the best model
 included the surface sterilization treatment term, we examined the model
 summary to determine the standardized coefficients (β) and p-value of the
 significance between marginal means of the levels of the surface
 sterilization fixed effect. We assessed model fit using diagnostics in the
 DHARMa package (version 0.2.7), including tests for heteroskedasticity,
 and for count models (Poisson or negative binomial), zero inflation and
 overdispersion. We performed the CCA analysis using the vegan package in
 R, comparing a model with surface sterilization as a fixed effect to a
 null model using an ANOVA. 
description: The corresponding raw DNA sequencing data associated with this
 project can be found on GenBank (BioProject: PRJNA639981).