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Effects of consumer surface sterilization on diet DNA metabarcoding data of terrestrial invertebrates in natural environments and feeding trials



• 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. • 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. • 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 sterilization, but this term was not statistically significant (β = −2.3, p-value = .07). • 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.
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 .
Funding information
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–
prey interactions
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.
2.1 | 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
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
Amplified and
sequenced Extracted
Amplified and
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
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
with UNOISE3
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; 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 (53′) Source
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.
3.1 | 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
surface sterilized.
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
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
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).
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)
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
and environments.
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.
None declared.
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).
Raw sequence data are available on GenBank (BioProject:
PRJNA639981). Cleaned sequence data and analyses are available
on Dryad (DOI: 8snc).
Ana Miller- ter Kuile
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. 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.
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.
org /10.1101/132753
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.
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:// - 83822
01400 0300030
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.
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.
sb.v64 i0.15598
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.
org /10.1101/081257
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. 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.
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), 335. https://doi.
org/10.1890/04- 0922
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.
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. 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. 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: // 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.
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:// 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. 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
plasticity across its entire fragmented range. PLoS One, 8(3), ht tps:// al.pone.0058971
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. 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.
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.
org/10.1111/j.1365- 2435.2005.01041.x
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.
org/10.1371/journ al.pone.0115335
Stork, N. E. (2018). How many species of insects and other terrestrial ar-
thropods are there on earth? Annual Review of Entomology, 63, 3145.
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.
org/10.1371/journ al.pone.0040863
Tylianakis, J., Didham, R., Bascompte, J., & Wardle, D. (2008). Global change
and species interactions in terrestrial ecosystems. Ecology Letters, 11,
1351– 1363. 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),
344– 346.
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.
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. 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.
pnas.12098 72109
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.
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Microbiomes are increasingly recognized as widespread regulators of function from individual organism to ecosystem scales. However, the manner in which animals influence the structure and function of environmental microbiomes has received considerably less attention. Using a comparative field study, we investigated the relationship between freshwater mussel microbiomes and environmental microbiomes. We used two focal species of unionid mussels, Amblema plicata and Actinonaias ligamentina , with distinct behavioral and physiological characteristics. Mussel microbiomes, those of the shell and biodeposits, were less diverse than both surface and subsurface sediment microbiomes. Mussel abundance was a significant predictor of sediment microbial community composition, but mussel species richness was not. Our data suggest that local habitat conditions which change dynamically along streams, such as discharge, water turnover, and canopy cover, work in tandem to influence environmental microbial community assemblages at discreet rather than landscape scales. Further, mussel burrowing activity and mussel shells may provide habitat for microbial communities critical to nutrient cycling in these systems.
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Predator–prey interactions shape ecosystems and can help maintain biodiversity. However, for many of the earth's most biodiverse and abundant organisms, including terrestrial arthropods, these interactions are difficult or impossible to observe directly with traditional approaches. Based on previous theory, it is likely that predator–prey interactions for these organisms are shaped by a combination of predator traits, including body size and species‐specific hunting strategies. In this study, we combined diet DNA metabarcoding data of 173 individual invertebrate predators from nine species (a total of 305 individual predator–prey interactions) with an extensive community body size dataset of a well‐described invertebrate community to explore how predator traits and identity shape interactions. We found that 1) mean size of prey families in the field usually scaled with predator size, with species‐specific variation to a general size scaling relationship (exceptions likely indicating scavenging or feeding on smaller life stages). We also found that 2) although predator hunting traits, including web and venom use, are thought to shape predator–prey interaction outcomes, predator identity more strongly influenced our indirect measure of the relative size of predators and prey (predator:prey size ratios) than either of these hunting traits. Our findings indicate that predator body size and species identity are important in shaping trophic interactions in invertebrate food webs and could help predict how anthropogenic biodiversity change will influence terrestrial invertebrates, the earth's most diverse animal taxonomic group.
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Ecological phenomena are often measured in the form of count data. These data can be analyzed using generalized linear mixed models (GLMMs) when observations are correlated in ways that require random effects. However, count data are often zero-inflated , containing more zeros than would be expected from the standard error distributions used in GLMMs, e.g., parasite counts may be exactly zero for hosts with effective immune defenses but vary according to a negative binomial distribution for non-resistant hosts. We present a new R package, glmmTMB , that increases the range of models that can easily be fitted to count data using maximum likelihood estimation. The interface was developed to be familiar to users of the lme4 R package, a common tool for fitting GLMMs. To maximize speed and flexibility, estimation is done using Template Model Builder ( TMB ), utilizing automatic differentiation to estimate model gradients and the Laplace approximation for handling random effects. We demonstrate glmm TMB and compare it to other available methods using two ecological case studies. In general, glmm TMB is more flexible than other packages available for estimating zero-inflated models via maximum likelihood estimation and is faster than packages that use Markov chain Monte Carlo sampling for estimation; it is also more flexible for zero-inflated modelling than INLA , but speed comparisons vary with model and data structure. Our package can be used to fit GLMs and GLMMs with or without zero-inflation as well as hurdle models. By allowing ecologists to quickly estimate a wide variety of models using a single package, glmm TMB makes it easier to find appropriate models and test hypotheses to describe ecological processes.
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Predator–prey interactions in natural ecosystems generate complex food webs that have a simple universal body-size architecture where predators are systematically larger than their prey. Food-web theory shows that the highest predator–prey body-mass ratios found in natural food webs may be especially important because they create weak interactions with slow dynamics that stabilize communities against perturbations and maintain ecosystem functioning. Identifying these vital interactions in real communities typically requires arduous identification of interactions in complex food webs. Here, we overcome this obstacle by developing predator-trait models to predict average body-mass ratios based on a database comprising 290 food webs from freshwater, marine and terrestrial ecosystems across all continents. We analysed how species traits constrain body-size architecture by changing the slope of the predator–prey body-mass scaling. Across ecosystems, we found high body-mass ratios for predator groups with specific trait combinations including (1) small vertebrates and (2) large swimming or flying predators. Including the metabolic and movement types of predators increased the accuracy of predicting which species are engaged in high body-mass ratio interactions. We demonstrate that species traits explain striking patterns in the body-size architecture of natural food webs that underpin the stability and functioning of ecosystems, paving the way for community-level management of the most complex natural ecosystems.
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High-throughput sequencing of taxon-specific loci, or DNA metabarcoding, has become an invaluable tool for investigating the composition of plant-associated fungal communities and for elucidating plant–fungal interactions. While sequencing fungal communities has become routine, there remain numerous potential sources of systematic error that can introduce biases and compromise metabarcoding data. This chapter presents a protocol for DNA metabarcoding of the leaf mycobiome based on current best practices to minimize errors through careful laboratory practices and validation.
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Network approaches to ecological questions have been increasingly used, particularly in recent decades. The abstraction of ecological systems – such as communities – through networks of interactions between their components indeed provides a way to summarize this information with single objects. The methodological framework derived from graph theory also provides numerous approaches and measures to analyze these objects and can offer new perspectives on established ecological theories as well as tools to address new challenges. However, prior to using these methods to test ecological hypotheses, it is necessary that we understand, adapt, and use them in ways that both allow us to deliver their full potential and account for their limitations. Here, we attempt to increase the accessibility of network approaches by providing a review of the tools that have been developed so far, with – what we believe to be – their appropriate uses and potential limitations. This is not an exhaustive review of all methods and metrics, but rather, an overview of tools that are robust, informative, and ecologically sound. After providing a brief presentation of species interaction networks and how to build them in order to summarize ecological information of different types, we then classify methods and metrics by the types of ecological questions that they can be used to answer from global to local scales, including methods for hypothesis testing and future perspectives. Specifically, we show how the organization of species interactions in a community yields different network structures (e.g., more or less dense, modular or nested), how different measures can be used to describe and quantify these emerging structures, and how to compare communities based on these differences in structures. Within networks, we illustrate metrics that can be used to describe and compare the functional and dynamic roles of species based on their position in the network and the organization of their interactions as well as associated new methods to test the significance of these results. Lastly, we describe potential fruitful avenues for new methodological developments to address novel ecological questions.
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Parasites and other symbionts are crucial components of ecosystems, regulating host populations and supporting food webs. However, most symbiont systems, especially those involving commensals and mutualists, are relatively poorly understood. In this study, we have investigated the nature of the symbiotic relationship between birds and their most abundant and diverse ectosymbionts: the vane‐dwelling feather mites. For this purpose, we studied the diet of feather mites using two complementary methods. First, we used light microscopy to examine the gut contents of 1,300 individual feather mites representing 100 mite genera (18 families) from 190 bird species belonging to 72 families and 19 orders. Second, we used high‐throughput sequencing (HTS) and DNA metabarcoding to determine gut contents from 1,833 individual mites of 18 species inhabiting 18 bird species. Results showed fungi and potentially bacteria as the main food resources for feather mites (apart from potential bird uropygial gland oil). Diatoms and plant matter appeared as rare food resources for feather mites. Importantly, we did not find any evidence of feather mites feeding upon bird resources (e.g., blood, skin) other than potentially uropygial gland oil. In addition, we found a high prevalence of both keratinophilic and pathogenic fungal taxa in the feather mite species examined. Altogether, our results shed light on the long‐standing question of the nature of the relationship between birds and their vane‐dwelling feather mites, supporting previous evidence for a commensalistic–mutualistic role of feather mites, which are revealed as likely fungivore–microbivore–detritivore symbionts of bird feathers.
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Pollination is a key ecosystem service for agriculture and wider ecosystem function. However, most pollination studies focus on Hymenoptera, with hoverflies (Syrphidae) frequently treated as a single functional group. We tested this assumption by investigating pollen carried by eleven species of hoverfly in five genera, Cheilosia, Eristalis, Rhingia, Sericomyia and Volucella, using DNA metabarcoding. Hoverflies carried pollen from 59 plant taxa, suggesting they visit a wider number of plant species than previously appreciated. Most pollen recorded came from plant taxa frequently found at our study sites, predominantly Apiaceae, Cardueae, Calluna vulgaris, Rubus fruticosus agg., and Succisa pratensis, with hoverflies transporting pollen from 40% of entomophilous plant species present. Overall pollen transport network structures were generalised, similar to other pollination networks elsewhere. All hoverfly species were also generalised with few exclusive plant/hoverfly interactions. However, using the Jaccard Index, we found significant differences in the relative composition of pollen loads between hoverfly genera, except for Volucella, demonstrating some degree of functional complementarity. Eristalis and Sericomyia species had significant differences in relative pollen load composition compared to congeners. Our results demonstrate the range of pollens transported by hoverflies and the potential pollination function undertaken within this ecologically and morphologically diverse guild.
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Background Understanding feedback between above- and below-ground processes of biological communities is a key to the effective management of natural and agricultural ecosystems. However, as above- and below-ground food webs are often studied separately, our knowledge of material flow and community dynamics in terrestrial ecosystems remains limited. Results We developed a high-throughput sequencing method for examining how spiders link above- and below-ground food webs as generalist predators. To overcome problems related to DNA-barcoding-based analyses of arthropod–arthropod interactions, we designed spider-specific blocking primers and Hexapoda-specific primers for the selective PCR amplification of Hexapoda prey sequences from spider samples. By applying the new DNA metabarcoding framework to spider samples collected in a temperate secondary forest in Japan, we explored the structure of a food web involving 15 spider species and various taxonomic groups of Hexapoda prey. These results support the hypothesis that multiple spider species in a community can prey on both above- and below-ground prey species, potentially coupling above- and below-ground food-web dynamics. Conclusions The PCR primers and metabarcoding pipeline described in this study are expected to accelerate nuclear marker-based analyses of food webs, illuminating poorly understood trophic interactions in ecosystems. Electronic supplementary material The online version of this article (10.1186/s40851-018-0088-9) contains supplementary material, which is available to authorized users.
Food webs form the basis of biological communities, though empirical research has been hindered by difficulties in quantifying interactions. Metabarcoding from predator gut content extractions with universal primers promises to provide simple and rapid insights into food web interactions. However, the highly overabundant predator DNA often completely outcompetes that of the digested prey DNA during PCR, impeding the ability to assess the abundance and diversity of prey items. Focusing on the issue of overabundance of predator DNA amplified by a commonly used COI primer pair, we use predator lineage‐specific SNPs at the 3’‐end of PCR primers to selectively block out predators from amplification. While this approach largely prevents predator amplification, it retains high taxonomic versatility for prey lineages. We introduce a novel multilocus assay, targeting four nuclear and mitochondrial rDNA markers and test our approach in a diverse set of spiders from 12 families. We estimate the recovered prey DNA proportions and compare the taxonomic composition of prey communities between markers. Using a feeding experiment, we also explore recovery of prey DNA over time. While commonly used COI primers yield low and very unpredictable amounts of prey DNA, our assay allows for a considerable and consistent prey enrichment across all tested species. The recovered prey's taxonomic composition is comparable between markers and supports results acquired by COI. The new marker set can be amplified in a simple multiplex PCR, considerably reducing the necessary workload. Our multi‐locus approach allows the generation of an unprecedented amount of prey data at low cost and effort. Lineage specific PCR is taxonomically versatile and could readily be adapted to any prey‐predator interaction, opening up the opportunity for community‐wide studies on food web interactions.
Ecological networks are composed of interacting communities that influence ecosystem structure and function. Fungi are the driving force for ecosystem processes such as decomposition and carbon sequestration in terrestrial habitats , and are strongly influenced by interactions with invertebrates. Yet, interactions in detritivore communities have rarely been considered from a network perspective. In the present study, we analyse the interaction networks between three functional guilds of fungi and insects sampled from dead wood. Using DNA metabarcoding to identify fungi, we reveal a diversity of interactions differing in specificity in the detritivore networks, involving three guilds of fungi. Plant pathogenic fungi were relatively unspecialized in their interactions with insects inhabiting dead wood, while interactions between the insects and wood-decay fungi exhibited the highest degree of specialization, which was similar to estimates for animal-mediated seed dispersal networks in previous studies. The low degree of specialization for insect symbiont fungi was unexpected. In general, the pooled insect-fungus networks were significantly more specialized, more modular and less nested than randomized networks. Thus, the detritivore networks had an unusual anti-nested structure. Future studies might corroborate whether this is a common aspect of networks based on interactions with fungi, possibly owing to their often intense competition for substrate.
In the last decade, new methods of estimating global species richness have been developed and existing ones improved through the use of more appropriate statistical tools and new data. Taking the mean of most of these new estimates indicates that globally there are approximately 1.5 million, 5.5 million, and 7 million species of beetles, insects, and terrestrial arthropods, respectively. Previous estimates of 30 million species or more based on the host specificity of insects to plants now seem extremely unlikely. With 1 million insect species named, this suggests that 80% remain to be discovered and that a greater focus should be placed on less-studied taxa such as many families of Coleoptera, Diptera, and Hymenoptera and on poorly sampled parts of the world. DNA tools have revealed many new species in taxonomically intractable groups, but unbiased studies of previously wellresearched insect faunas indicate that 1-2% of species may be truly cryptic. Expected final online publication date for the Annual Review of Entomology Volume 63 is January 7, 2018. Please see for revised estimates.