Ecology and Evolution. 2021;00:1–10.
Received: 2 April 2021
Revised: 14 July 2021
Accepted: 16 July 2021
DOI: 10.1002 /ece3.7968
Effects of consumer surface sterilization on diet DNA
metabarcoding data of terrestrial invertebrates in natural
environments and feeding trials
Ana Miller- ter Kuile | Austen Apigo | Hillary S. Young
This is an op en access arti cle under the ter ms of the Creat ive Commo ns Attri bution License, which p ermit s use, distribution an d reproduction in any medium,
provide d the original wor k is properly cited.
© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Depar tment of Ecology, Evolution, and
Marine Biolog y, University of California
Santa Barbara, Sant a Barba ra, CA, USA
Ana Miller- ter Kuile, Depar tment of Ecology,
Evolution, and Marine Biology, Universit y of
California Santa Barbara, S anta Barbara, CA
93106, USA .
Nationa l Geogr aphic Societ y; National
Science Foundation; Universit y of California
Santa Barbara Faculty Senate
1. DNA metabarcoding is an emerging tool used to quantify diet in environments
and consumer groups where traditional approaches are unviable, including small-
bodied invertebrate taxa. However, metabarcoding of small taxa often requires
DNA extraction from full body parts (without dissection), and it is unclear whether
surface contamination from body parts alters presumed diet presence or diversity.
2. We examined four different measures of diet (presence, rarefied read abundance,
richness, and species composition) for a terrestrial invertebrate consumer (the
spider Heteropoda venatoria) both collected in its natural environment and fed
an offered diet item in contained feeding trials using DNA metabarcoding of full
body parts (opisthosomas). We compared diet from consumer individuals surface
sterilized to remove contaminants in 10% commercial bleach solution followed by
deionized water with a set of unsterilized individuals.
3. We found that surface sterilization did not significantly alter any measure of diet
for consumers in either a natural environment or feeding trials. The best- fitting
model predicting diet detection in feeding trial consumers included surface steri-
lization, but this term was not statistically significant (β = −2.3, p- value = .07).
4. Our results suggest that surface contamination does not seem to be a significant
concern in this DNA diet metabarcoding study for consumers in either a natural
terrestrial environment or feeding trials. As the field of diet DNA metabarcoding
continues to progress into new environmental contexts with various molecular
approaches, we suggest ongoing context- specific consideration of the possibility
of surface contamination.
consumptive interactions, contamination, diet analysis, food web, invertebrates, predator–
MILLER- TER KUI LE ET aL.
1 | INTRODUCTION
Biological communities and ecosystem function are shaped by inter-
actions bet ween organisms (Hooper et al., 2005). Among the many
interaction types, consumptive interactions (including herbivory,
predation, and parasitism) can shape the stability of biologically di-
verse communities (Delmas et al., 2019). Until recently, consumptive
interactions were most often measured by visual observations of
feeding or by gut dissection or inspection of fecal contents (Baker
et al., 2014; Nielsen et al., 2018), which made it challenging or impos-
sible to conduct diet analyses for many consumer groups. Specifically,
these diet analyses are not possible for consumers that (a) are too
small for dissec tion and food identification and (b) have feeding hab-
its or food items which make diet visually unidentifiable (Sheppard
& Harwood, 2005). This group of consumers, including terrestrial
insect s, spiders, and other arthropods, form the base of most ter-
restrial food webs and are integral to maintaining biodiversity and
ecosystem functioning in ecosystems worldwide (Wilson, 1987). For
these consumer groups, the use of high- throughput sequencing is
one of the most promising emerging approaches for determining
gut contents. High- throughput sequencing (hereafter referred to
as “diet DNA metabarcoding”) can identify a suite of diet species
at once and provides a comprehensive and efficient method for
determining intrapopulation, intraspecific, and interspecific diets
(Lucas et al., 2018; Pompanon et al., 2012; Quéméré et al., 2013;
Soininen et al., 2015). These methods have already illuminated new
interactions and ecological trends in a variety of environments (e.g.,
host– parasitoid: (Wirta et al., 2014); plant– herbivore: (Kartzinel
et al., 2015); host– parasite: (Schnell et al., 2012); and predator– prey:
(Toju & Baba, 2018).
As diet DNA metabarcoding methods continue to advance,
however, they need to be validated so that the ecological inference
made from them is robust. Focusing on the challenges of small or-
ganisms where small body size has limited other diet analysis meth-
ods, DNA diet analyses are often performed on full organisms or
body par ts without gut dissection (e.g., Jacobsen et al., 2018; Toju
& Baba, 2018). The necessity to use full organisms or body parts
increases the possibility of surface contamination, altering detection
and species composition of presumed diet items. Surface steriliza-
tion, the use of chemical treatments or physical action to remove
surface contaminants, is systematically used in other fields to reduce
the risk of contamination in DNA metabarcoding datasets (Burgdorf
et al., 2014; Zimmerman & Vitousek, 2012). However, surface steril-
izatio n has not been systematically used in diet metaba rcoding stud-
ies. While some fields have developed informed protocols based on
decades of research into best practices and study- specific consider-
ations (Brown et al., 2018), the field of diet DNA metabarcoding has
not developed a similarly systematic approach (e.g., ethanol: Doña
et al ., 2019, bl each: Ansl an et al ., 2016 , an d no steriliza ti on: Jacobsen
et al., 2018; Wirta et al., 2014). The lack of systematic surface ster-
ilization in diet DNA metabarcoding when using full individuals or
body parts may be due to the desire to avoid DNA destruction in
relatively permeable animal cells (Greenstone et al., 2012). However,
without considering surface sterilization as a treatment for surface
contamination, we have limited ability to confidently assign DNA
sequences to ingested diet items (Greenstone et al., 2011, 2012;
Linville & Wells, 2002).
In this study, we look at the effects of surface sterilization to re-
move surface contaminants on our understanding of consumer diets
where the DNA of full body parts (no internal dissection) is used for
diet DNA metabarcoding. Targeting the CO1 gene region, we pro-
duced high- throughput sequencing results from the full body parts
(opisthosomas without gut dissection) of an invertebrate consumer
species (the spider, Heteropoda venatoria). We surface sterilized half
of the consumers prior to DNA extraction using a series of washes
in a 1:10 dilution of bleach (10% commercial bleach) and deionized
water; we left the other half of consumers unsterilized. We first de-
termined how sur face sterilization to remove contaminants impacts
presumed diet from consumers collected in their natural environ-
ment, comparing surface sterilized individuals to those which were
not surface sterilized, to ask whether surface sterilization influences
(a) detection, (b) rarefied abundance, (c) richness, and (d) composition
of potential diet items. We then performed a laboratory feeding trial,
comparing surface sterilized individuals to those which were not
surface sterilized to ask whether surface sterilization influenced (a)
detection or (b) rarefied abundance of offered diet items. Exploring
these questions in natural and contained settings addresses whether
surface contamination alters interpretations of feeding interactions
and thus whether it should be incorporated into standard protocols
in diet metabarcoding.
2 | MATERIALS AND METHODS
2.1 | Field site and collections
We conducted fieldwork on Palmyra Atoll National Wildlife Refuge,
Northern Line Islands, USA (5°53′N, 162° 05′W). Palmyra Atoll has
a well- characterized species list and is relatively species poor, al-
lowing for relatively complete characterization of consumer and
diet items (Handler et al., 2007). We targeted a generalist, active
hunting spider species (Heteropoda venatoria) because (a) it oc-
curs in high abundance on the atoll and is easy to collect, (b) it is
a generalist species that feeds on a wide suite of organisms (in-
cluding spiders, other invertebrates, and two geckos in the genus
Lepidodactylus), an d (c) it is the onl y sp ec ie s in its famil y on the atoll,
meaning consumer DNA can be differentiated from potential diet
DNA. All individuals were stored individually in sterilized containers
(Greenstone et al., 2011).
2.2 | Natural environment consumer collection
In 2015, we collected consumers (n = 47) from natural environ-
ments, which had fed on available diet items and come into contact
with environmental surfaces, to test whether DNA metabarcoding
MILLER- TER KUIL E ET aL.
detects diet DNA ef fectively. Consumers were collected at night via
eye shine while they were actively hunting. We collected the first
individuals we observed in each survey period and so they represent
the distribution of body size and population demographics of this
species that actively hunt in that environment. We froze all individu-
als at −80℃ immediately following collection until surface steriliza-
tion and DNA extraction in 2019.
2.3 | Feeding trial consumer setup and feeding
In 2017, we cond uct ed labo rator y trials (n = 26) to test whet her DNA
metabarcoding detects DNA from diet items offered in a contained
environment. We created feeding environments from one- liter plas-
tic yogur t containers with holes for air transfer and placed one H.
venatoria in each container. After 12 hr, we placed one large grass-
hopper (Oxya japonica, a likely diet item (Handler et al., 2007)) in each
container and left all containers for 24 hr. We then froze (−20℃)
each H. venatoria that had killed the grasshopper (n = 25, consump-
tion was not easily detectable and thus not considered in analyses).
We cleaned all containers between trials with 10% bleach solution.
To test surface sterilization's efficacy at removing possible
contaminants, we used a surface sterilization treatment (Burgdorf
et al., 2014; Schulz et al., 1993) on ~half the consumers for each
set: those collected from the natural environment and those sub-
jected to controlled feeding trials. We submerged and stirred each
(whole) consumer in 10% commercial bleach by volume for 2 min and
washed each in deionized water for 2 min. Similar bleach submersion
leads to undetectable DNA degradation in similar soft- exoskeleton
consumers (Greenstone et al., 2012; Linville & Wells, 2002). Natural
environment consumers (2015) had been frozen at −80°C since col-
lection; we surface sterilized these consumers in a sterilized laminar
flow hood in 2019 just before DNA extraction (n = 22 surface ster-
ilized, n = 25 not surface sterilized; Table 1). We surface sterilized
feeding trial consumers (2017) in the laboratory on the atoll in 2017
following freezing at −20℃ and then stored each in individual vials
of 95% ethanol in a −20°C freezer until DNA extraction (no −80 °C
freezer was available at the field station that year) (n = 10 surface
sterilized; n = 14 not surface sterilized). Prior to DNA extraction, we
dried all samples for 1– 3 hr in a sterilized laminar flow hood and then
removed the full opisthosoma (containing the hind gut region) using
a sterilized scalpel. Between all steps, tools were sterilized with ei-
ther ethanol and flame (scalpels and forceps) or 10% bleach (sur-
faces) between handling each individual.
2.4 | DNA extraction and removal of consumer
DNA with AMPure XP beads
We extracted DNA from each consumer following a modified CTAB
extraction protocol (Fulton et al., 1995). 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 propor tion of lower molecular weight DNA
with AMPure XP beads prior to PCR (Appendix S5, Figure S1). We
diluted each DNA sample to 20ng/μl (creating a total sample volume
of 40μl), mixed each sample using AMPure XP beads (0.75x bead-
to- DNA ratio), and kept the supernatant. With the supernatant, we
precipitated the DNA pellets with isopropanol and 5 M potassium
acetate and washed DNA pellets with ethanol (Appendix S6). We
quantified this cleaned DNA again using a Qubit fluorometer and
diluted all samples to 10 ng/μl prior to PCR steps. All DNA pellets
were stored in and diluted with TE buffer.
2.5 | PCR amplification, library
preparation, and sequencing
We amplified the CO1 gene with general metazoan primers
(Krehenwinkel et al., 2017; Leray et al., 2013; Yu et al., 2012; Table 2).
We performed all PCR preparation steps in a UV- sterilized biosafety
cabinet. We used PCR volumes of 25μl (9μl nuclease free water,
12.5μl GoTaq Green Master Mix (Promega Corp.), 1.25 μl of each of
the primers (at 10 mM), and 1 μl of DNA template (at 10 ng/μl)). We
ran each sample in duplicate along with duplicated negative samples
each PCR run. PCRs are as follows: initial denaturation step at 95℃
for 3 min and then 35 cycles of (a) 95℃ for 30 s, (b) 46℃ for 30 s, and
(c) 72℃ for 1 min, followed by a final 5 min at 72℃. We cleaned PCR
products with AMPure XP beads at a 0.8x bead- to- DNA ratio and
resuspended from beads using a 10 mM 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 5 nM. We multiplexed all samples with one negative control
and two fungal clone positive controls (GenBank accession num-
bers: MG8 40195 and MG840196; Apigo & Oono, 2018; Clark
et al., 2016; Toju et al., 2012). We submitted multiplexed samples for
sequencing at the University of California, Santa Barbara Biological
Nanostructures Laboratory Genetics Core. Samples were run on an
Surface sterilized Unsterilized
Natural environment 22 18 25 19
Feeding trial 10 814 11
Note: Bold numbers indicate final sample sizes for statistical analyses.
TABLE 1 Sample sizes for successfully
extracted and PCR- amplified samples
of surface sterilized and unsterilized
Heteropoda venatoria individuals in the
natural environment and feeding trial
MILLER- TER KUI LE ET aL.
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. Our full protocol from DNA extraction through
submission for Illumina sequencing can be found in Appendix S6.
2.6 | Sequence merging, filtering, and clustering
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; Edgar, 2016, Appendix S5,
Figure S3). Prior to denoising with UNOISE3, we used cutadapt (ver-
sion 1.18, Martin, 2011) to remove primers from each sequence. We
also repeated analyses with the DADA2 algorithm run through R
(dada2 package version 220.127.116.11; Callahan et al., 2016) and with a
data cleaning step run through BBSplit (Bushnell, 2019) 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, 3× as many reads per
positive control) and the cleaning step paired with either DADA2 or
UNOISE3 did not increase potential diet DNA detection (summar y
and comparisons in Appendices S1 and S2).
We created a list of unique ASVs and a matrix of ASV abun-
dances 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
eval ue of 0. 01 (dow nl oad e d on 20 No ve mbe r 2019). We vis ualiz ed
and exported taxonomic alignment using MEGAN Community
Edition (version 6.18.0, Huson et al., 2016), using default set-
tings (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 as-
signed below the order level (n = 24), we submitted each ASV
individually to the BL AST 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 5– 16 February 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 as-
signments from both programs and discarded taxonomic assign-
me nts th at wer e mism atc hed at th e f ami ly level or hi ghe r (El b rec ht
et al., 2017).
2.7 | Detection of potential diet items
For consumers from both 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 fo-
cused 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 in-
gested). We rarefied (McKnight et al., 2019, Appendix S5, Figure S 4)
based on the sample with the lowest sequencing depth which had
been se qu en ced with 95% + sampling completeness based on iNEXT
(version 2.0.20) interpolation and extrapolation methods (Hsieh &
Chao, 2017, 16,004 reads for natural environment and 55,205 reads
for feeding trial consumers). We rarefied using the rrarefy() func tion
in the vegan (version 2.5.6) package in R and rarefied the field and
laboratory consumers separately.
We then selected all ASVs that matched potential diet items for
the natural environment consumers (diet filtered to include all ASVs
in the Kingdom: Animalia; Clade: Bilateria, excluding consumer DNA)
and jus t the offered diet item for the feeding trial cons um er s (includ-
ing species: Oxya japonica, genus: Oxya, and family: Acrididae, ex-
cluding those which only matched to order). Because the consumer
species H. venatoria 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 (Kartzinel et al., 2015) and predator– prey inter-
action (Brose et al., 2019) studies.
2.8 | Statistical analyses
For potential diet detection and rarefied abundance in both sets
of consumers (natural environment and feeding trial), we used
TABLE 2 Primers with Illumina overhang adapters (in bold) used to amplif y the CO1 region in this study
Primer Sequence (5′– 3′) Source
mICOIintF TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGGWACWG GW TGA AC WGTW TAYCCYCC Yu et al. (2012)
F o l - d e g e n - r e v GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGTANACYTCNGGRTGNCCRAARAAYCA Leray et al. (2013)
MILLER- TER KUIL E ET aL.
ge n e r aliz e d line ar mo del s to as s e ss the ef fect of su r fac e st e rili z a tion
treatment. For prey detection, we used all potential (natural envi-
ronment) 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 environ-
ment) or offered (feeding trials) diet DNA reads per sample as the
response variable, surface sterilization treatment as a fixed ef fect,
total read abundance of the sample (constant across all) as an off-
set 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 sterili-
zation treatment as the fixed effect and a Poisson or negative bino-
mial distribution (to correct for overdispersion when needed). We
assessed dif fe rences in potent ial diet item comp osition with famil y-
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
al so as s ess e d AS V com p osi tio n as a repr esen t ati on of po ten tia l prey
composition using a canonical correspondence analysis (CCA) with
surface sterilization as a predictor variable. We performed these
analyses along with multiple other supplementary analyses and ap-
proaches, which can be found in the Supplementary Information
(Appendices S4 and S5).
For all generalized linear models and mixed models, we per-
formed 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 pack-
age (version 1.0.0, Brooks et al., 2017) 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 heteroscedasticity, and for count mo dels (Poisson or negative bi-
nomial), zero inflation and overdispersion (Bolker et al., 2009; Zuur
et al., 2009). We performed the CCA using the vegan package in R,
comparing a model with surface sterilization as a fixed effect to a
null model using an ANOVA. All raw data, data cleaning, and data
analyses are available online (Miller- ter Kuile, 2020a, 2020 b), and
model outputs for primar y and supplemental models can be found in
Appendices S3 and S4.
3 | RESULTS
3.1 | PCR success, sequence merging, filtering, and
clustering with UNOISE3 and DADA2
We successfully extracted DNA from 100% of samples (n = 72).
Amplification success across all samples was 78%, with 56 of 72
initially extracted samples successfully amplified and were thus
sequenced (Table 1). Seventy- three percent (128 of 176) of ASVs
matched to a taxonomic assignment. Twenty- three percent of the
total ASVs corresponded to potential diet items (41 of 176), and eight
percent (14 of 176) corresponded to consumer DNA (the remaining
73 ASVs corresponded to nondiet items, including fungi, bacteria,
and human DNA). Amplicon sequence variants that matched to the
consumer comprised the majorit y of each sample (98 ± 0.6% of
rarefied abundance compared to 1.5 ± 0.6% for potential diet and
0.3 ± 0.1% for nondiet). Eighty- five percent of the potential diet
ASVs received a species- level taxonomic assignment (35 of 41) from
either BLAST or BOLD taxonomic assignments, and every potential
diet species received a family- level and order- level taxonomic as-
signment. In MEGAN, the family- level assignments corresponded
to 100% coverage results suggesting evidence of no mitochondrial
pseudogenes (NUMTs) at the family level (Saitoh et al., 2016). There
were no conflicting taxonomic assignment s at the family level or
higher between the BOLD and BLAST assignments.
3.2 | Detection of potential diet items
We detected potential diet in 89% (33 of 37) of natural environ-
ment consumers and the offered diet in 74% (14 of 19) of feeding
trial consumers. For natural environment consumers, family- level
taxonomic units corresponded to 20 families of potential diet items.
The best model for potential diet detection in natural environment
consumers was the null model that did not include surface steriliza-
tion treatment as a fixed effect (Figure 1, Appendix S4). For feeding
trial consumers, one ASV matched to the of fered diet (species: O.
japonica, genus: Oxya, and family: Acrididae), and the best model for
diet detection included the fixed effect of surface sterilization treat-
ment, though the model without the surface sterilization term was
within two AICc values (ΔAICc = 1.59) and the ef fect of the surface
sterilization term was not statistically clear ( β = −2.3; p- value = .07).
We detected offered prey in 50% of consumers that had been sur-
face sterilized compared to 91% of those consumers that were not
3.3 | Proportion of potential diet DNA
For natural environment consumers, potential diet rarefied DNA
sequence reads represented 2.0% (±1.0%) of total per- sample DNA
sequence abundance (Figure 2). In feeding trial consumers, offered
MILLER- TER KUI LE ET aL.
diet DNA sequence reads represented 0.8% (±0.7% SE) of total per-
sample DNA sequence abundance. For both natural environment
and feedin g trial consum er s, the null models that did not incl ude sur-
face sterilization treatment as a fixed effect were the best models of
diet DNA read abundance.
3.4 | Potential diet richness and composition in
natural environment consumers
For family- level taxonomic unit s, potential diet richness per natu-
ral environment consumer was an average 2.08 (±0.26 SE) fami-
lies per individual sample, with a maximum of 5 diet families in one
consumer diet (Figure 3). Richness of potential diet ASVs for these
consumers was similar, with an average of 2.32 (±0.31) potential
diet ASVs per sample with a maximum of 7 ASVs in one consumer
(Figure 3). The best models for per- sample potential diet richness
for both family- level taxonomic unit s and ASV- level, as well as
both family- level PERMANOVA and ASV- level CCA, were the null
FIGURE 1 (a) Detection of all potential diet DNA in natural
environment consumers that were and were not surface sterilized.
Detection of diet DNA did not change with sterilization treatment.
(b) Detection of offered diet (Oxya japonica) DNA in feeding trial
consumers that were and were not surface sterilized. While the
best- fitting model based on AICc values indicated an effect of
surface sterilization treatment (a decrease from 91% without
surface sterilization to 50% with surface sterilization), the effect of
this term in the model was statistically unclear (p- value = .07)
FIGURE 2 Neither the (a) proportion of total potential diet DNA
in natural environment consumers or the (b) propor tion of offered
diet item DNA in feeding trial consumers significantly changed with
surface sterilization treatment
FIGURE 3 In natural environment consumers, surface
sterilization did not alter per- sample diet richness of either family-
level or ASV- level taxonomic units
MILLER- TER KUIL E ET aL.
models which did not include surface sterilization treatment as
a predictor (Figure 4, Figure S1). Diet families came from insect,
arachnid, and centipede orders (insects: Diptera (5), Dermaptera (1),
Blattodea (3), Lepidoptera (3), Orthopotera (3), Hymenoptera (1),
Odonata (1); Arachnids: Araneae (2); Scorpiones (1); and Centipedes:
Geophilomorpha (1), Figure 4).
4 | DISCUSSION
Surface sterilization does not change diet measures in diet DNA
metabarcoding data for the predatory consumer H. venatoria in ei-
ther natural settings or a feeding trial environment, suggesting that
surface sterilization is not a necessary step for this consumer. Our
results suggest that various measures of diet, including potential diet
detection, rarefied abundance, richness, and composition, are not
significantly altered by sur face sterilizing consumers prior to DNA
metabarcoding. For potential diet richness and composition, in par-
ticular, these results did not change when considering potential diet
in combined family- level taxonomic units (making them comparable
with food web studies in this field, e.g., Brose et al., 2019) and when
considering richness of molecular taxonomic units (ASVs). We de-
tected diet across 8 4% of the total consumers in our study (n = 47
of 56), including 20 diet families. Diet DNA metabarcoding has high
potential to contribute diet information for small consumers with
cryptic feeding habits. Furthermore, it appears that current proto-
cols that do not include surface sterilization steps are sufficient to
determine potential diet for these consumers.
The field of diet DNA metabarcoding has not universally adopted
surface sterilization practices into common protocols, in particular
for studies including DNA extraction of full organisms or body parts
without dissection (e.g., Jacobsen et al., 2018; Wirta et al., 2014).
We demonstrate that sur face sterilization does not seem necessary
to avoid contamination effects. The evident lack of the effects of
surface contaminant s in our study contrasts with obvious sur face
contaminants that alter ecological interpretations in other fields
using high- throughput sequencing to determine community diver-
sity, particularly fungal endophyte studies (Burgdorf et al., 2014).
One reason for this difference may be that fungal spores are wide-
spread on and in the surfaces of most environments and organisms
(Després et al., 2012) and likely to contaminate studies targeting
specific subgroups of these communities. Indeed, even in our data-
set, some sequences matched to fungal taxonomies. The fact that
these nontarget sequences did not alter our DNA metabarcoding
data by hiding target diet DNA, even with the relative rarity of diet
DNA compared to consumer DNA (0.006%– 26% of each sample),
is likely due to differences in biomass of these sources of DNA in
our samples and the specificity of our DNA size- selection protocol
and PCR primers (Elbrecht et al., 2017; Krehenwinkel et al., 2017).
Therefore, our results are promising both in validating the robust-
ness of findings from past diet DNA studies that have not imple-
mented surface sterilization treatments, but also highlight that diet
DNA metabarcoding using broad, universal primer sets (e.g., those in
this study) is an effective tool even when DNA sequence data con-
tain potential environmental contaminants (Appendix S5, Figure S5).
While we saw no widespread support of the necessity for surface
sterilization in our study, a model from the feeding trial that includes
surface sterilization performed slightly better than one without this
treatment (ΔAICc = 1.59). Thus, it is possible that contained envi-
ronments may be more prone to contamination than open terres-
trial environments. We see this result as an ideal starting point for
next steps in validating diet DNA metabarcoding in similar contexts.
Specifically, because this study had a relatively limited sample size
(n = 8 and 11 in each sterilization treatment group) and because
we did not confirm ingestion, a similar trial including crossed treat-
ments of sterilization with different forms of diet item contac t (e.g.,
Greenstone et al., 2012) would provide additional evidence of the
effects of surf ace sterilization or sur face contamination. Further ex-
ploration of these results might reveal that the decision to surface
sterilize prior to diet DNA metabarcoding may matter more in some
FIGURE 4 For natural environment consumers, surface
sterilization did not alter the composition (either with a presence–
absence of abundance model) of potential diet items of either
family- level taxonomic units or ASV- level taxonomic units. In
this figure of family- level taxonomic units by surface sterilization
treatment, presence is indicated by a colored box and abundance is
indicated by color depth (divided by quartiles due to wide variation
in DNA sequence abundance)
MILLER- TER KUI LE ET aL.
environments and experiments than others (e.g., where diet items
are in high density or consumers have long handling times (Abrams &
Ginzburg, 200 0; Samu & Biro, 1993). Furthermore, as earlier studies
targeting particular consumer diet pairs explored (e.g., Greenstone
et al., 2012), the field of diet DNA metabarcoding is ripe for a com-
parison of surface sterilization techniques.
Diet DNA metabarcoding can empirically provide diet descrip-
tions for a suite of consumers important to food web ecology and
the maintenance of biodiversit y on the planet (Stork, 2018).
Characterizing consumptive interactions for small, cryptic spe-
cies for the first time will build a better picture of nature's com-
plexity and allow ecologists to confidently query how species
interactions will change with continued anthropogenic disturbance
(Tylianakis et al., 20 08). Like any method for determining con-
sumptive interactions in nature, DNA metabarcoding continues
to be refined, especially as tools and data emerge (Krehenwinkel
et al., 2019; Kvist, 2013). This study builds on past efforts to refine
diet DNA metabarcoding by using surface sterilization to pinpoint
potential sources of error in diet DNA data. Here, we found that,
on the whole, surface sterilization seems unnecessary in two con-
texts (terrestrial environments and contained feeding trials) when
extracting DNA from body par ts of invertebrate taxa. Continued
context- specific refinement of surface sterilization protocols, and
of other steps in diet DNA metabarcoding, will improve the wide-
spread utility of diet DNA metabarcoding across consumer groups
This project was funded by the National Science Foundation (DEB
#1457371), National Geographic Society (#9698- 15), and a Faculty
Research Grant from the UC Santa Barbara Academic Senate. We
would like to thank M. Lee, C. Motta, J. Smith, E. Lutz, and T. Chou
for help with field and laboratory work. We would like to thank
the U.S. Fish and Wildlife Service and The Nature Conservancy on
Palmyra Atoll for supporting fieldwork for this project. R. Oono pro-
vided laborator y space and equipment and we acknowledge the use
of the Biological Nanostructures Laboratory within the California
NanoSystems Institute, supported by the University of California
(UC) Santa Barbara and the Universit y of California Office of the
President. We acknowledge the use of computational facilities at
the Center for Scientific Computing (CSC), which was purchased
with funds from the National Science Foundation (CNS- 1725797)
and is supported by the California NanoSystems Institute and the
Materials Research Science and Engineering Center (MRSEC; NSF
DMR 1720256) at UC Santa Barbara. We thank D. Orr, E. Forbes, H.
Lowman, A. Bui, D. Preston, D. Trovillion, E. Crone, E. Sauer, L. Falke,
B. DiFiore, C. Jerde, M. Lee, and R. Ramiro for help in aspects of ed-
iting this manuscript. We thank four anonymous reviewers for their
help revising this manuscript. This is publication number PARC- 160
from the Palmyra Atoll Research Consor tium.
CONFLICT OF INTEREST
Ana Miller- ter Kuile: Conceptualization (equal); data curation (lead);
formal analysis (lead); funding acquisition (equal); investigation
(lead); methodology (equal); project administration (lead); visualiza-
tion (lead); writing– original draft (lead); writing– review and editing
(lead). Austen Apigo: Conceptualization (equal); formal analysis (sup-
porting); investigation (supporting); methodology (equal); project ad-
ministration (supporting); visualization (supporting); writing– original
draft (supporting); writing– review and editing (supporting). Hillary
S. Young: Conceptualization (supporting); formal analysis (support-
ing); funding acquisition (equal); investigation (supporting); project
administration (supporting); super vision (lead); writing– original draft
(supporting); writing– review and editing (supporting).
DATA AVAILAB ILITY STATE MEN T
Raw sequence data are available on GenBank (BioProject:
PRJNA639981). Cleaned sequence data and analyses are available
on Dryad (DOI: https://doi.org/10.5061/dryad.gqnk9 8snc).
Ana Miller- ter Kuile https://orcid.org/0000-0003-2599-5158
Abr am s, P. A., & Gi nzburg, L. R . (200 0). The natu re of pre dation: Prey de-
pendent, ratio dependent or neither? Trends in Ecolog y and Evolution,
15( 8 ) , 3 3 7 – 3 4 1 . h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / S 0 1 6 9 - 5 3 4 7 ( 0 0 ) 0 1 9 0 8 - X
Anslan, S., Bahram, M., & Tedersoo, L. (2016). Temporal changes in fun-
gal communities associated with guts and appendages of Collembola
as based on culturing and high- throughput sequencing. Soil Biology
and Biochemistry, 96, 152– 159. https://doi.org/10.1016/j. soilb
Apigo, A., & Oono, R. (2018). MG840195 and MG840196.
Baker, R., Buckland, A., & Sheaves, M. (2014). Fish gut content analysis:
Robust measures of diet composition. Fish and Fisheries, 15(1), 170 –
177. ht tps://doi .org /10.1111 /fa f.12026
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J.
R., Stevens, M. H. H., & White, J. S. S. (2009). Generalized linear
mixed models: A pr actical guide for ecology and evolution. Trends
in Ecology and Evolution, 24(3), 127– 135. https://doi.org/10.1016/j.
Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A.,
Berg, C. W., Nielsen, A., & Bolker, B. M. (2017). Modeling zero-
inflated count data with glmmTMB. BioRxiv, 13275 3, h tt p s ://d oi.
Brose, U., Archambault, P., Barnes, A. D., Bersier, L.- F., Boy, T., Canning-
Clode, J., Conti, E., Dias, M., Digel, C., Dissanayake, A., Flores, A.
A. V., Fussmann, K., Gauzens, B., Gray, C., Häussler, J., Hirt, M. R.,
Jacob, U., Jochum, M., Kéfi, S., … Iles, A. C. (2019). Predator traits
determine food- web architecture across ecosystems. Nature
Ecology and Evolution, 3(6), 919– 927. https://doi.org/10.1038/s4155
9 - 0 1 9 - 0 8 9 9 - x
Brown, S. P., Leopold, D. R., & Busby, P. E. (2018). Protocols for inves-
tigating the leaf mycobiome using high- throughput DNA sequenci.
In Plant pathogenic fungi and oomycetes: Methods and protocols (Vo l.
1848) . h t t p s : / / d o i . o r g / 1 0 . 1 0 0 7 / 9 7 8 - 1 - 4 9 3 9 - 8 7 2 4 - 5
Burgdorf, R. J., Laing, M. D., Morris, C. D., & Jamal- Ally, S. F. (2014). A
procedure to evaluate the efficiency of surface sterilization methods
in culture- independent fungal endophyte studies. Brazilian Journal of
Microbiology, 45(3), 977– 983. ht tps://doi.org/10.1590/S1517 - 83822
MILLER- TER KUIL E ET aL.
Bushnell, B. (2019). BBMap.
Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A.
J. A ., & Holmes, S. P. (2016). DADA2: High- resolution sample infer-
ence from Illumina amplicon data. Nature Methods, 13(7), 581– 583.
Clark, K., Karsch- Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W.
(2016). GenBank. Nucleic Acids Research, 44(D1), D67– D72. https://
Delmas, E., Besson, M., Brice, M.- H., Burkle, L. A., Dalla Riva, G. V., Fortin,
M.- J., Gravel, D., Guimarães, P. R., Hembry, D. H., Newman, E. A.,
Olesen, J. M., Pires, M. M., Yeakel, J. D., & Poisot, T. (2019). Analysing
ecological net works of species interactions. Biological Reviews, 94 (1),
16– 36. https://doi.org/10.1111/brv.12433
Després, V. R., Huffman, J. A., Burrows, S. M., Hoose, C., Safatov, A.
S., Buryak, G., Fröhlich- Nowoisky, J., Elbert, W., Andreae, M. O.,
Pöschl, U., & Jaenicke, R. (2012). Primary biological aerosol par-
ticles in the atmosphere: A review. Tellus, Serie s B: Chemical and
Physical Meteorology, 64(1), 15598. https://doi.org/10.3402/tellu
Doña, J., Proctor, H., Serrano, D., Johnson, K. P., van Oploo, A. O., Huguet-
Tapia, J. C., Ascunce, M. S. & Jovani, R. (2019). Feather mites play a
role in cleaning host feather s: New insights from DNA metabarcod-
ing and microscopy. Molecular Ecology, 28(2), 203– 218. https://doi.
org /10.1111/me c.14581
Edgar, R. C. (2016). UNOISE2: Improved error- correction for Illumina
16S and ITS amplicon sequencing. BioRxiv, 081257. https://doi.
Elbrecht, V., Peinert, B., & Leese, F. (2017). Sorting things out: Assessing
effects of unequal specimen biomass on DNA metabarcoding.
Ecology and Evolution, 7(17 ), 6918– 6926. htt ps: //doi.o rg /10.10 02/
Fulton, T. M., Chunwongse, J., & Tanksley, S. D. (1995). Microprep pro-
tocol for ex trac tion of DNA from tomato and other herbaceous
plants. Plant Molecular Biolog y Reporter, 13 (3), 207– 209. ht tps://doi.
org /10.1007/BF026 70897
Greens tone, M . H., Weber, D. C., Coudron, T. C., & Payton, M. E. (2011).
Unnecessary roughness? Testing the hypothesis that predators des-
tined for molecular gut- content analysis must be hand- collected to
avoid cross- contamination. Molecular Ecology Resources, 11(2), 286–
293. https://doi.org/10.1111/j.1755- 0998.2010.02922.x
Greens tone, M. H., Weber, D. C., Coudron, T. A., Pay ton, M. E., &
Hu, J. S. (2012). Removing external DNA contamination from
arthropod predators destined for molecular gut- content anal-
ysis. Molecular Ecology Resources, 12(3), 464– 469. https://doi.
org /10.1111/j .1755- 0 998.201 2.0311 2. x
Handler, A., Gruner, D., Haines, W., Lange, M., & Kaneshiro, K. (20 07).
Arthropod sur veys on Palmyra Atoll, Line Islands, and insights
into the decline of the native tree Pisonia grandis (Nyctaginaceae).
Pacific Science, 61(4), 485– 502. https://doi.org/10.2984/153
4- 618 8( 20 07)61
Hooper, D. U., Chapin, F. S., Ewel, J. J., Hector, A., Inchausti, P., Lavorel,
S., Lawton, J. H., Lodge, D. M., Loreau, M., Naeem, S., Schmid, B.,
Setälä, H., Symstad, A. J., Vandermeer, J., & Wardle, D. A. (2005).
Effects of biodiversity on ecosystem functioning: A consensus of
current knowledge. Ecological Monographs, 75(1), 3– 35. https://doi.
Hsieh, T. C., & Chao, A. (2017). Rarefaction and extrapolation: Making fair
comparison of abundance- sensitive phylogenetic diversity among
multiple assemblages. Systematic Biology, 66(1), 10 0– 111. htt ps://doi.
org/10.10 93/sysbi o/sy w073
Huson, D. H., Beier, S., Flade, I., Górska, A., El- Hadidi, M., Mitra, S.,
Tappu, R. (2016). MEGAN community edition - Interactive explora-
tion and analysis of large- scale microbiome sequencing data. PLoS
Computational Biology, 12(6), 1– 12. https://doi.org/10.1371/journ
Illumina, (2019). Illumina. Nextera X T DNA Library Prep Reference Guide.,
(May), Document # 15031942 v05.
Jacobsen, R. M ., Sverdrup- Thygeson, A., Kauserud, H., & Birkemoe, T.
(2018). Revealing hidden insect- fungus interactions; moderately spe-
cialized, modular and anti- nested detritivore networks. Proceedings.
Biological Sciences, 285(1876), 2017283 3. https://doi.org/10.10 98/
Kart zinel, T. R., Chen, P. A., Coverdale, T. C., Erickson, D. L ., Kress, W. J.,
Kuzmina, M. L ., Rubenstein, D. I., Wang, W., & Pringle, R . M. (2015).
DNA metabarcoding illuminates dietary niche partitioning by African
large herbivores. Proceedings of the National Academy of Sciences,
112(26), 8019– 8024. https://doi.org/10.1073/pnas.15032 83112
Krehenwinkel, H., Kennedy, S. R., Adams, S. A., Stephenson, G. T., Roy,
K., & Gillespie, R. G. (2019). Multiplex PCR targeting lineage- specific
SNPs: A highly efficient and simple approach to block out predator
sequences in molecular gut content analysis. Methods in Ecology and
Evolution, 10 (7 ), 982– 993. https: //doi.org/10.1111/2041- 210X.13183
Krehenwinkel, H., Kennedy, S., Pekár, S., & Gillespie, R. G. (2017). A cost-
efficient and simple protocol to enrich prey DNA from extr actions of
predatory arthropods for large- scale gut content analysis by Illumina
sequencing. Methods in Ecology and Evol ution, 8(1), 126– 134. https://
doi .org/10.1111/20 41- 210X .12647
Kvist, S. (2013). Barcoding in the dark?: A critical view of the sufficiency of
zoologic al DNA barcoding databases and a plea for broader integra-
tion of taxonomic knowledge. Molecular Phylogenetics and Evolution,
69(1), 39– 45. https://doi.org/10.1016/j.ympev.2013.05.012
Leray, M ., Yang, J. Y., Meyer, C . P., Mills, S. C., Agudelo, N., Ranwez, V.,
Boehm, J. T., & Machida, R. J. (2013). A new versatile primer set
targeting a short fragment of the mitochondrial COI region for me-
tabarcoding metazoan diversity: Application for characterizing coral
reef fish gut content s. Frontiers in Zoology, 10(34), 1– 14. https://doi.
o r g / 1 0 . 1 1 8 6 / 1 7 4 2 - 9 9 9 4 - 1 0 - 3 4
Linville, J. G., & Wells, J. D. (2002). Surface sterilization of a maggot using
bleach does not interfere with mitochondrial DNA analysis of crop
contents. Journal of Forensic Sciences, 47(5), 15532J. https://doi.
org /10.1520/jfs15 532j
Lucas, A ., Bodger, O., Brosi, B. J., Ford, C. R., Forman, D. W., Greig, C.,
Hegarty, M., Jones, L., Neyland, P. J., & De Vere, N. (2018). Floral
resource partitioning by individuals within generalised hoverfly polli-
nation networks revealed by DNA metabarcoding. Scientific Reports,
8( 1 ) , 1 – 1 1 . h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 5 9 8 - 0 1 8 - 2 3 1 0 3 - 0
Martin, M. (2011). Cutadapt removes adapter sequences from high-
throughput sequencing reads. Embnet Journal, 17(1), 10– 12. https://
doi.org/10.14 806/ ej.17.1.200
McKnight, D. T., Huerlimann, R ., Bower, D. S., Schwarzkopf, L., Alford, R .
A., & Zenger, K. R. (2019). Methods for normalizing microbiome data:
An ecological perspective. Methods in Ecology a nd Evolution, 10(3),
389– 400 . ht tps://doi.o rg /10.1111/2041- 210X.13115
Miller- ter Kuile, A. (2020a). BioProject: PRJNA639981.
Miller- ter Kuile, A., Apigo, A ., & Young, H. (2020b). Diet DNA metabar-
coding data from spiders (Heteropoda venatoria) from Palmyra Atoll
(2015- 2017) with both individual samples that have and have not
been sur face sterilized. https://doi.org/10.5061/dryad.gqnk9 8snc
Nielsen, J. M., Clare, E. L ., Hayden, B., Brett, M. T., & Kratina, P.
(2018). Diet tracing in ecology: Method comparison and selec-
tion. Methods in Ecolog y and Evolution, 9(2), 278– 291. https://doi.
org /10.1111/2041- 210X.12869
Pompanon, F., Deagle, B. E., Symondson, W. O. C., Brown, D. S., Jarman,
S. N., & Taberlet, P. (2012). Who is eating what: Diet assessment
using next generation sequencing. Molecular Ecology, 21(8), 1931–
1950. ht tps://doi .org /10.1111 /j.1365 - 294X. 2011.054 03.x
Quéméré, E., Hibert, F., Miquel, C., Lhuillier, E., Rasolondraibe, E.,
Champeau, J., Rabarivola, C., Nusbaumer, L., Chatelain, C., Gautier,
L., Ranirison, P., Crouau- Roy, B., Taberlet, P., & Chikhi, L. (2013).
A DNA metabarcoding study of a primate dietary diversity and
MILLER- TER KUI LE ET aL.
plasticity across its entire fragmented range. PLoS One, 8(3), ht tps://
Saitoh, S., Aoyama, H., Fujii, S., Sunagawa, H., Nagahama, H., Akutsu, M.,
Shinzato, N., Kaneko, N., & Nakamori, T. (2016). A quantitative pro-
tocol for DNA metabarcoding of springtails (Collembola). Genome,
59(9), 705– 723. https://doi.org/10.1139/gen- 2015- 0228
Samu, F., & Biro, Z. (1993). Functional response, multiple feeding and
wasteful killing in a wolf spider (Araneae: Lycosidae). Europ ean
Journal of Entomology, 90, 471– 476.
Schnell, I. B., Thomsen, P. F., Wilkinson, N., Rasmussen, M ., Jensen, L . R.
D., Willerslev, E., Bertelsen, M. F., & Gilber t, M. T. P. (2012). Screening
mammal biodiversity using DNA from leeches. Current Biology, 22(8),
R262– R263. https://doi.org/10.1016/j.cub.2012.02.058
Schulz, B., Wanke, U., Draeger, S., & Aust, H. J. (1993). Endophytes from
herbaceous plants and shrubs: Effectiveness of surface steriliza-
tion methods. Mycological Research, 97(1 2), 1447– 1450. http s://doi.
o r g / 1 0 . 1 0 1 6 / S 0 9 5 3 - 7 5 6 2 ( 0 9 ) 8 0 2 1 5 - 3
Sheppard, S. K., & Harwood, J. D. (2005). Advances in molec-
ular ecology: Tracking trophic links through predator- prey
food- webs. Functional Ecology, 19(5), 751– 762. https://doi.
Soininen, E. M., Gauthier, G., Bilodeau, F., Berteaux, D., Gielly, L.,
Taberlet, P., Gussarova, G., Bellemain, E., Hassel, K., Stenøien, H. K.,
Epp, L., Schrøder- Nielsen, A., Brochmann, C., & Yoccoz, N. G . (2015).
Highly overlapping winter diet in two sympatric lemming species
revealed by DNA metabarcoding. PLoS One, 10 (1), 1– 18. https://doi.
Stork, N. E. (2018). How many species of insects and other terrestrial ar-
thropods are there on earth? Annual Review of Entomology, 63, 31– 45.
h t t p s : / / d o i . o r g / 1 0 . 1 1 4 6 / a n n u r e v - e n t o - 0 2 0 1 1 7 - 0 4 3 3 4 8
Toju, H ., & Baba, Y. G . (2018). DNA met abarcoding of spiders, insects,
and springtails for exploring potential linkage between above- and
below- ground food webs. Zoological Letters, 4(1), 1– 12. https://doi.
o r g / 1 0 . 1 1 8 6 / s 4 0 8 5 1 - 0 1 8 - 0 0 8 8 - 9
Toju, H., Tanabe, A . S., Yamamoto, S., & Sato, H. (2012). High- coverage
ITS pr im er s for th e DN A- ba sed ide nt if icat io n of asc om ycetes and ba-
sidiomycetes in environmental samples. PLoS One, 7(7). https://doi.
Tylianakis, J., Didham, R., Bascompte, J., & Wardle, D. (2008). Global change
and species interactions in terrestrial ecosystems. Ecology Letters, 11,
1351– 1363. https://doi.org/10.1111/j.1461- 0248.2008.01250.x
Wilson, E. O. (1987). The little things that run the world (The impor-
tance and conservation of inver tebrates). Conservation Biology, 1(4),
Wirta, H. K., Hebert, P. D. N., Kaartinen, R., Prosser, S. W., Várkonyi, G.,
& Roslin, T. (2014). Complementar y molecular information changes
our perception of food web structure. Proceedings of the National
Academy of Sciences, 111(5), 1885– 1890. https://doi.org/10.1073/
pna s.13169 9 0111
Yu, D. W., Ji, Y., Emerson, B. C., Wang, X., Ye, C., Yang, C., & Ding, Z. (2012).
Biodiversity soup: Metabarcoding of arthropods for rapid biodiver-
sity assessment and biomonitoring. Methods in Ecology a nd Evolution,
3(4), 613– 623. https://doi.org/10.1111/j.2041- 210X.2012.00198.x
Zimmerman, N. B., & V itousek, P. M. (2012). Fungal endophy te com-
munities reflect environmental structuring across a Hawaiian land-
scape. Proceedings of the National Academy of Sciences of the United
States of America, 109(32), 13022– 13027. https://doi.org/10.1073/
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, Anatoly, A., & Smith, G. M.
(2009). Mixed effects models and extensions in ecology with R . Mixed
effect s models and extensions in ecology with R (Vo l. 53). https://doi.
org /10.1017/CBO97 81107 415 324. 004
Additional supporting information may be found online in the
Supporting Information section.
How to cite this article: Miller- ter Kuile, A., Apigo, A ., &
Young, H. S. (2021). Effects of consumer surface sterilization
on diet DNA metabarcoding data of terrestrial invertebrates
in natural environments and feeding trials. Ecology and
Evolution, 00, 1– 10. https://doi.org/10.1002/ece3.7968