\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Invertebrates for vertebrate biodiversity monitoring: comparisons using
three insect taxa as iDNA samplers}



\author[1]{Aimee Massey}%
\author[2]{Roberta Bronzoni}%
\author[2]{David da Silva}%
\author[1]{Jennifer Allen}%
\author[3]{Patrick de Lazari}%
\author[3]{Manoel dos Santos Filho}%
\author[2]{Gustavo Canale}%
\author[2]{Christine Bernardo}%
\author[4]{Carlos Peres}%
\author[1]{Taal Levi}%
\affil[1]{Oregon State University}%
\affil[2]{Universidade Federal de Mato Grosso - Campus Sinop}%
\affil[3]{Universidade do Estado de Mato Grosso}%
\affil[4]{University of East Anglia}%


\vspace{-1em}



  
  \date{June 06, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Metabarcoding of environmental DNA (eDNA) is now widely used to build
diversity profiles from DNA that has been shed by species into the
environment. There is substantial interest in the expansion of eDNA
approaches for improved detection of terrestrial vertebrates using
invertebrate-derived DNA (iDNA) in which hematophagous, sarcophagous,
and coprophagous invertebrates sample vertebrate blood, carrion, or
feces. Here, we use metabarcoding and multiple iDNA samplers (carrion
flies, sandflies, and mosquitos) to profile gamma and alpha diversity in
a dry, tropical forest in the southern Amazon. Our main objectives were
to (1) compare diversity found with iDNA to camera trapping, which is
the conventional method of vertebrate diversity surveillance and (2)
compare each of the iDNA samplers to assess the effectiveness,
efficiency, and potential biases associated with each sampler. Carrion
flies were the most effective sampler, despite the least amount of
sampling effort and the fewest number of individuals captured for
metabarcoding, in describing vertebrate biodiversity followed by
sandflies. Camera traps had the highest median species richness at the
site-level but showed strong bias towards carnivore and ungulate species
and missed much of the diversity described by iDNA methods. Mosquitos
showed a strong feeding preference for humans as did sandflies for
armadillos, thus presenting potential utility to further study related
to host-vector interactions.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Rapid and cost-effective biodiversity surveillance is a critical,
burgeoning tool in ecology and conservation. DNA metabarcoding, or
high-throughput DNA sequencing of taxonomically informative barcode
genes, has become a well-established tool to efficiently detect many
species from bulk DNA samples for biodiversity surveillance. Such
sequencing is now frequently used to catalog species from the DNA that
organisms shed into the environment (eDNA) (Bohmann et al., 2014). For
example, aquatic vertebrate or invertebrate communities can be detected
with eDNA metabarcoding of water samples (Shaw et al., 2016; Stat et
al., 2017; Valentini et al., 2016), animal diets can be detected from
eDNA metabarcoding of feces (De Barba et al., 2014; Massey et al., in
press; Roffler et al., 2021), and plants and fungi can be detected by
eDNA metabarcoding of pollen or ice core samples (Bell et al., 2019;
Kraaijeveld et al., 2015; Varotto et al., 2021). Similarly, an emerging
technique to monitor the diversity of terrestrial vertebrates uses
animal-feeding invertebrates (whether feeding upon blood, flesh, or
scat) as direct sources of species' DNA and is called
invertebrate-derived DNA (iDNA) (Batovska et al., 2018;
Calvignac-Spencer et al., 2013; Drinkwater et al., 2019, 2020; Gogarten
et al., 2020; Kocher et al., 2017; P. S. Lee et al., 2016; Schnell et
al., 2012; van der Heyde et al., 2020; D. W. Yu et al., 2012). Similar
to camera traps, a conventional method for terrestrial vertebrate
surveillance, metabarcoding blood, carrion, or fecal meals from iDNA
samplers can be utilized as a non-invasive and efficient method at
profiling a diversity of species in often remote or dense habitats.
However, these methods differ in that camera trapping is strongly biased
for the detection of medium to large terrestrial mammals, with lower
detection probabilities of arboreal species, smaller bodied species, and
other non-mammal taxa (Burton et al., 2015; O'Brien \& Kinnaird, 2011).
These drawbacks could make iDNA metabarcoding a more attractive method
for biodiversity monitoring, particularly if iDNA is better able to
detect the birds, amphibians, reptiles, small mammals, and arboreal
mammals that camera trapping often fails to detect.

Leeches (Drinkwater et al., 2019; Schnell et al., 2012, 2018; Weiskopf
et al., 2018) and carrion flies (Calvignac-Spencer et al., 2013; P. S.
Lee et al., 2016; Lynggaard et al., 2019; Rodgers et al., 2017) have
been the primary sources of validating iDNA metabarcoding for monitoring
vertebrate biodiversity. The limited number of studies utilizing
metabarcoding with leeches or carrion flies as iDNA samplers have shown
that both taxa effectively profile biodiversity in remote or difficult
to access environments such as tropical forests (Drinkwater et al.,
2020; Gogarten et al., 2020; P. S. Lee et al., 2016; Weiskopf et al.,
2018). There is a growing literature of comparing carrion flies as iDNA
sources for metabarcoding to camera trapping (Gogarten et al., 2020; P.
S. Lee et al., 2016; Rodgers et al., 2017) but even with their utility
for profiling animal diversity, neither leeches nor carrions flies are
widely collected for other research purposes.

In contrast, some hematophagous insects such as mosquitos and sandflies
are important pathogen vectors worldwide and are routinely sampled for
disease surveillance. Should these pathogen vectors be effective
samplers of vertebrate biodiversity, then using iDNA to profile
biodiversity would be an attractive supplemental and/or complementary
pursuit to other study objectives using vector species (Batovska et al.,
2018; Kocher et al., 2017). We are aware of only one study that has
compared mosquitos and sandflies as iDNA samplers and found that both
were seemingly effective a describing vertebrate diversity (Kocher et
al., 2017). However, these results were not compared to data from a more
established iDNA sampler or to more conventional methods of diversity
surveillance.

A direct comparison of the emerging iDNA samplers (vectors such as
mosquitos and sandflies) to the commonly utilized iDNA taxa groups (such
as carrion flies or leeches) and finally to traditional methods of
biomonitoring (such as camera traps) is needed to evaluate the diversity
revealed by each iDNA source. Such a comparison will set the groundwork
for assessing common biases, benefits, and shortfalls associated with
each method given the current lack of comparison of established methods
to the emerging iDNA samplers of mosquitos and sandflies. Invertebrate
taxa differ in their feeding ecologies and other life history traits,
which can introduce taxonomic biases into biodiversity assessments when
these taxa are used as iDNA sources particularly when utilizing these
methods with small sample sizes or spatial scales. Thus, this comparison
is particularly needed at a spatial scale where these methods can be
evaluated for both alpha and gamma diversity given the same suite of
available species.

Here, we aim to assess the effectiveness of iDNA metabarcoding for
biodiversity surveillance using landscape scale, bulk sampling of insect
groups including carrion flies, sandflies, and mosquitos. We first
compare the three iDNA samplers to camera trapping (the established
method of vertebrate diversity surveillance) to determine if the two
methods reveal similar species richness, composition, and relative
abundance across the sampled landscape. Second, we compare the
biodiversity found with each of the iDNA samplers to assess the
efficiencies and potential biases with using different sources of iDNA
as biomonitoring tools. To achieve these comparisons, we examine both
gamma and alpha diversity with each iDNA sampler and tease apart the
likely causes of dissimilarity.

\textbf{Materials and Methods}

\emph{Study area}

This study region near Sinop, Mato Grosso, Brazil (-12\selectlanguage{ngerman}°04\selectlanguage{english}'73.9\selectlanguage{english}"S
-55deg43\selectlanguage{english}'85.0\selectlanguage{english}"W) represents topography and vegetation typical of the
ecotone between the Cerrado (dominated by shrubland and grassland) and
Amazonia (tropical forests) biomes (Mittermeier et al., 2003). The
southern border of the Amazon rainforest was once contiguous dry,
tropical forest that has been rapidly deforested by agricultural
activity and urbanization. This has resulted in a checkerboard-like
pattern of forest and agricultural patches (Fig. 1) with a mix of other
matrix types including urban areas and secondary vegetation.

The area is largely flat and dominated by a hot and humid climate with
daily temperatures showing greater fluctuation than annual mean low
(23degC in June) and high (26degC in September) temperatures. The climate is
dominated by dry (May to October) and wet (November to April) seasons
with monthly precipitation averages ranging from a low of 2 mm in July
to a high of 310 mm in February (climate data obtained to Sinop
municipality).

\emph{Insect sampling}

We collected bulk arthropods (sandflies, mosquitos, and carrion flies)
in a coordinated effort at 39 fragmented forest sites of varying size,
shape, and degree of fragmentation from April -- September in 2015 and
2016 (Fig. 1). At each site, we built a grid of three parallel 200 m
transects with each transect approximately 50 m apart (see Appendix S1:
Fig. S1 for trapping grid layout). Along each transect, we placed nine
UV LED CDC light traps (BioQuip; Catalog Number: 2770) with
approximately 30 m between traps for 27 total light traps per site. We
also set three homemade carrion fly traps at each grid in a triangular
arrangement with one trap at the end of the first transect, one at the
beginning of the second transect, and the final trap at the end of the
third transect (Appendix S1: Fig. S1). We engineered each carrion fly
trap from two 2 L soda bottles and gauze mesh. The trap was designed to
bait carrion flies with the smell of rotting meat while also keeping the
trapped carrion flies separated from the bait (see Appendix S1: Fig. S2
for carrion fly trap design). We trapped and collected the insects over
the course of 4 days and 3 trap nights at each site. We checked traps
each day and collected the collection cups containing the live insects
(and replaced this with a new collection cup) every 24 hours; we placed
the collection cups containing the live insects in a portable
refrigerator during transportation to Universidade Federal de Mato
Grosso (UFMT, Sinop campus) lab facilities. We immediately transferred
insect collections to a -20degC freezer for at least 30 minutes to stun
the insects in order to then separate out sandflies and mosquitos from
other insect by-catch. We sorted sandflies, mosquitos, and carrion flies
into separate pools based on site and date into 2 mL microtubes. Because
of differences in body size and weight, sandflies were pooled at 50
individuals, mosquitos were pooled at fifteen individuals, and carrion
flies were pooled at five individuals. Finally, we placed sorted insects
into a -80degC freezer until they were shipped using dry ice to our home
lab facility at Oregon State University where they were once again
frozen at -80degC until molecular processing.

\emph{DNA extraction, amplification, and sequencing}

Prior to DNA extraction, we macerated pooled samples using ceramic beads
(sandflies), disposable tissue grinding pestles (mosquitos), and manual
maceration using the blunt-end of a disposable tipped applicator
(carrion flies). We extracted DNA from sandflies and carrion flies with
the Qiagen Blood and Tissue Kit (Qiagen, Hilden, Germany) with slight
modifications to the manufacturer's specifications. Briefly, 200 uL of
Buffer ATL and 20 uL of Proteinase K were added to the sample in a 1.7
mL Eppendorf tube and the sample incubated for 3-5 hours at 56degC.
Post-incubation, samples were vortexed for 10 minutes and then purified
through washing. The DNA was eluted in a final volume of 100 uL. Because
we wanted to isolate both DNA and RNA from mosquitos for separate
studies of viral surveillance, we extracted nucleic acids following a
phenol-chloroform protocol modified from Griffiths et al. (2000) and
Simister et al. (2011) with final ethanol precipitation and purification
to isolate DNA. To purify for DNA, we resuspended the nucleic acid
pellet in 50 uL of RNase-free water and incubated half the sample (25
uL) with RNase A in a final concentration of 100 ug/mL for 10 minutes at
37degC. The other half of the sample was purified for RNA and then
synthesized for cDNA.

We amplified the extracted DNA from each iDNA sampler type in two
separate reactions using a slight modification of the pan-vertebrate
primer pair 12SV5F/12SV5R (Riaz et al., 2011), which targets
approximately 100 base pairs in the 12S region of the vertebrate
mitochondrial genome. We used the reverse primer 12SV5R
(TTAGATACCCCACTATGC) as Riaz et al. (2011) and a modified version (we
change the thymine to a degenerate base shown underlined) of the forward
primer 12SV5F to allow for broader binding of vertebrate targets
(\emph{Y} AGAACAGGCTCCTCTAG). In brief, PCR reactions were carried out
in a volume of 20 uL using 10 uL AmpliTaq Gold 360 Master Mix (final
concentration of 1x), 5 uL of forward and reverse primers (final
concentration of 0.25 uM), 3 uL of water, and 2 uL of DNA template. PCR
cycling was as follows: initial denaturing at 95degC for 10 minutes
followed by 40 cycles of 95degC for 30 seconds and 58degC for 30 seconds and
72degC for 30 seconds, and a final extension at 72degC for 7 minutes.

PCR amplicons were cleaned using PCRClean DX solid-phase reversible
immobilization magnetic beads (Aline Biosciences, Woburn, MA, USA). Each
PCR reaction was quantified using Accublue High Sensitivity dsDNA
Quantitation kit (Biotium, Fremont, CA, USA) and normalized to 6 ng/uL.
Each group of 384 PCR products was then pooled into a single library,
for a total of two libraries for carrion flies, four libraries for
sandflies, and two libraries for mosquitos. Individual libraries were
then tagged with an additional 6 base pair identifying index using the
NEBnext Ultra II DNA Library Prep kit (New England Biolabs, Ipswich, MA,
USA). Pooled samples were analyzed on a bioanalyzer to confirm fragment
size. The libraries were then sequenced using the Illumina HiSeq 3000
(Illumina, San Diego, CA, USA) at the Center for Genome Research and
Biocomputing at Oregon State University.

\emph{Sequence analysis}

We analyzed raw sequence reads using a bioinformatics pipeline designed
to trim and sort the sequence reads according to scat sample
identification. An outline of the bioinformatic process is as follows:
(1) raw reads were paired using PEAR software (Zhang et al., 2014); (2)
followed by demultiplexing using 8 basepair index sequences unique to
each sample (mismatches discarded) using a novel grep regular
expression; (3) lastly, OTUs from each sample were taxonomically
assigned using BLAST against 12S vertebrate sequences available in
GenBank.

We carried out a series of filtering and quality control measures on
taxonomically assigned sequences. For each of the three iDNA datasets,
we removed contaminant reads (primarily human DNA sequences) and removed
sample replicates that did not amplify (below a 500 read threshold). We
then removed OTU's with either a percent identity score less than 95\%
or 1\% of the total number of sequences in that sample. Finally, we
eliminated species that were not found in both sample replicates. We
then manually reviewed BLAST results for each purported species to
ensure that the 12S barcode discriminated species from sympatric
congeners or confamilials and to confirm that the taxonomic assignments
were for species regional to Mato Grosso, Brazil. If species were not
regional, we examined the other equal matches to reassign non-regional
species. If no suitable species level matches were discovered, then
these taxa were assigned at the genus level or removed from the dataset.

\emph{Camera traps}

To compare metabarcoding results to methods traditionally used for
profiling animal abundance and/or occurrence, we used digital infrared
camera traps (Bushnell\textsuperscript{(r)}) to sample vertebrate
diversity from May 2015 - March 2016 at 33 of the 39 sites where we
collected arthropods as iDNA sources. In total, we deployed 116 camera
traps for a total of 5,093 camera days and cameras per site varied from
two to seven although at the majority of sites three cameras were
utilized. We set up the camera traps 40 cm above the ground and they
were active 24-hours per day. Camera days per camera ranged from 26 --
92 with the average number of camera days equal to 44 days.

\emph{Data analysis}

Sample-size-based rarefaction curves with extrapolation (Chao et al.,
2014) were created using the iNEXT package in R (Hsieh et al., 2016) to
examine species richness across the three iDNA datasets and from the
camera trapping data. For the iDNA data, we converted species read
abundance to species incidence data (presence/absence) at the sample
level. Due to differences in the number of amplified samples across the
three iDNA sources, species richness was extrapolated to the iDNA source
with the greatest number of samples following Chao et al. (2014). For
the camera trapping data, we measured species richness across the total
number of camera days from all deployed cameras.

We used a relative abundance index (RAI) to compare the biodiversity
found using iDNA metabarcoding data and camera trap data. For
metabarcoding data, the RAI is equal to the sum of occurrences for
species \emph{i} divided by the total number of pooled samples. The RAI
measured from camera trapping data is calculated as the number of
species \emph{i} events divided by the total number of camera trap
nights. A single species event from camera trap data was defined by any
individual (or group of individuals) from one species captured by a
camera over a one-hour time frame. We then used RAI to visualize the
gamma diversity according to both iDNA and camera trapping methods by
binning species according to important life history traits to compare
the effectiveness of the biomonitoring methods in describing the gamma
diversity of different taxa groups. To compare RAI across iDNA and
camera traps, we only included sites that were sampled by both methods.

After comparing diversity from iDNA and camera traps, we then compared
the iDNA sources to each other to assess the similarities, efficiencies,
and potential biases of each sampler type. We used a Bray-Curtis
dissimilarity matrix of sites x species and applied a PERMANOVA test
using the \emph{adonis} function to test for significance in the
separation of the vertebrate community based on iDNA sampler. We then
used the \emph{vegan} package in R (Oksanen et al., 2013) and
the\emph{metaMDS} function to visualize dissimilarities in species
composition at sites using non-metric multidimensional scaling (NMDS).
Each site was designated three rows in our abundance matrix: one for
each iDNA type (carrion fly, sandfly, or mosquito). We also utilized
the\emph{ordiellipse} function to visualize the spread of the data based
on iDNA source with ellipses drawn to two standard deviation (95\%
confidence intervals) from the group mean. Lastly, significant species
vectors were calculated using the \emph{envfit} function.

To answer whether an iDNA sampler could replace another iDNA sampler in
profiling alpha diversity, we calculated RAI of species at the site
level for each of the iDNA datasets and compared species occurrences at
sites from the different iDNA samplers. We visualized the results in
scatterplots. If two iDNA datasets showed similar species composition at
sites, most data points would fall along an isometric line. If two iDNA
samplers revealed dissimilar species composition across sites, more data
points would fall along the x-axis or y-axis revealing occurrence of a
species in one of the iDNA datasets and an absence of that species in
the other iDNA dataset. Finally, to further explore the different
feeding ecologies associated with the insect taxa used as iDNA samplers,
we compared the RAI at sites for the most abundant species in each iDNA
dataset using boxplots.

\emph{Human DNA}

When analyzing metabarcoding data for profiling vertebrate diversity, we
remove all human DNA reads under the assumption that they are
contamination. However, we found a disproportionate amount of the DNA
sequences from mosquito pools was identified to be human and the total
human read abundance in samples was well above the level of human DNA
found in the extraction and PCR negative controls. Thus, we reexamined
levels of human DNA found in all three iDNA metabarcoding datasets. We
first eliminated non-human contaminants and non-amplifying samples
(replicates with less than 500 total reads). With the remaining dataset,
we culled all non-human read sequences. We closely examined the amount
of human DNA in the extraction and PCR negative controls to determine a
read threshold for a human positive sample. To be extremely conservative
in what we deemed as a sample positive for human, we set the threshold
as the highest read count in any negative control replicate for each
iDNA dataset. For example, if the highest read count for human DNA in a
negative control for the carrion fly metabarcoding data was 25,000 then
the threshold to be counted as a sample positive for human DNA was
25,000 reads in a replicate. Additionally, we also eliminated samples
where human DNA was not present in both sample replicates. A relative
abundance index (RAI) was calculated as the number of samples positive
for human DNA at a site divided by the total number of samples at a
site.

\textbf{Results}

Carrion fly 12S metabarcoding resulted in 16,130,638 paired sequence
reads from 368 pooled samples. The average read depth per sample
replicate was 21,976 reads. The sandfly 12S metabarcoding results showed
25,093,673 paired sequence reads from 943 pooled samples and the average
read depth per sample replicate is 13,780 reads. Finally, mosquito 12S
metabarcoding results resulted in 35,132,064 total paired sequence reads
from 322 pooled samples with an average read depth per sample replicate
of 55,152 reads.

After conservative quality measures designed to clean the raw data, the
following datasets were used for analysis (and excluded human DNA):
carrion fly data had 5,010,458 total paired sequences from 265 pooled
samples which represented 37 of the 39 sites; the sandfly data had
5,233,020 total paired sequences from 354 pooled samples which
represented 36 sites; finally, the mosquito data had 6,254,399 paired
sequences from 78 pooled samples which represented 25 sites (a summary
of site-level data for each iDNA method can be found in Appendix S1:
Table S1). Pooled samples of carrion flies showed the highest
amplification success followed by pooled sandfly samples and finally
pooled mosquito samples (Table 1), although these results exclude human
DNA which made up the majority of sequence reads in the mosquito
dataset.

\emph{Comparing biodiversity found with insect samplers and camera
traps}

In total, iDNA and camera trapping methods revealed 90 vertebrate
species across the sampled landscape (Table 2). iDNA alone described 82
species with carrion fly data describing the most diversity of any
sampler with 62 species while sandfly data revealed 49 species and
mosquito data revealed 16 species. Camera trap data revealed 28
vertebrate species and added 8 species to the diversity already
described with iDNA.

Species accumulation curves show a statistically significant difference
in species richness gleaned from each iDNA source and camera trapping
(Fig. 2). Carrion fly data and mosquito data are extrapolated to match
the number of sandfly samples, and consequently the extrapolated curves
provide less certainty than the interpolated curves. However, the
confidence intervals show significant separation in the total species
richness from each iDNA source across both the interpolated and
extrapolated curves. Using carrion flies as a measure of biodiversity
reveals a significantly greater number of vertebrate species across the
landscape than both sandflies and mosquitos. Additionally, the total
number of carrion flies analyzed (n = 1,759) was lower than both the
total number of sandflies (n = 48,686) and mosquitos (n = 4,776)
analyzed. The species accumulation curve for camera trap data shows that
species richness plateaus after approximately 1,500 camera days meaning
that few to no new species are revealed by camera traps even with
continued camera days (Fig. 2). The species richness from camera traps
was most similar to the species richness from the extrapolated curve for
the pooled mosquito samples, which was the least efficient iDNA sampler
in describing vertebrate diversity (Fig. 2; Table 1).

Comparisons of species' RAI from iDNA samplers and camera traps show
differences in diversity profiles across taxa groups (Fig. 3). As
expected, camera traps showed a high diversity of carnivore and ungulate
species and a low diversity of arboreal species or birds. Camera traps
also did not capture the occurrence or diversity of domestic species and
rodent species particularly when compared to the diversity of these
groups found using iDNA methods. Carrion fly data consistently showed a
high diversity of species in each taxa group. Sandfly data also showed
high diversity of species in most taxa groups with an especially high
diversity and relative abundance of armadillo species (Fig. 3; Table 2).
Lastly, mosquito data showed the lowest species diversity for many of
the taxa groups and only showed a high diversity of domestic species
when comparing mosquito data to the other datasets (although carrion fly
data still displayed greater diversity and relative abundance from
domestic species). When species incidence data was parsed at the site
level, camera traps showed the highest median species richness at sites
(Fig. 4) and a consistent diversity profile across sites (Appendix S1:
Fig. S3) indicating that camera trapping consistently samples the same
species across sites.

\emph{Comparing the different iDNA samplers for biomonitoring}

A PERMANOVA test and further visualization of the dissimilarity matrix
using NMDS (k = 2; stress = 0.17) show that the gamma diversity did not
differ significantly based on iDNA source (p-value = 0.23) (Fig. 5).
Although there was no statistically significant separation in the gamma
diversity found using the different iDNA methods, a site-by-site
comparison of the alpha diversity found with each iDNA method does not
support substantial overlap of the vertebrate communities revealed by
each iDNA source at the site-level (Fig. 5). Although carrion flies
revealed the greatest gamma diversity, when comparing species presence
at a site across iDNA datasets, we found that there were many species
absences in the carrion fly data at sites where sandflies or mosquito
samplers recorded a species presence (Appendix S1: Fig. S4). Carrion fly
data missed 181 of the 213 species occurrences from the sandfly data and
42 of the 63 total species occurrences from the mosquito data when
examined at the site-level (Appendix S1: Fig. S4). Arrowed vectors show
that the significant species in the ordination (p-value \textless{}
0.001) were also the most abundant in each iDNA dataset, and thus these
species likely drive the separation in the vertebrate communities at
sites as described by the different iDNA samplers (Fig. 5). These
significant, non-human vertebrate species include cattle (highest RAI in
carrion fly data), dog (highest RAI in mosquito data), nine-banded
armadillo (highest RAI in sandfly data), capuchin, lesser anteater, and
turkey (second highest RAI in mosquito data). This sampler bias was
confirmed by our analysis of the most abundant species in each of the
iDNA datasets which revealed the differing feeding associations of each
insect group (Fig. 6) and we found that these abundant species were
responsible for many of the site-level mismatches between the iDNA
samplers (Appendix S1: Fig. S4). Additionally, we determined that humans
were the primary feeding target of mosquitos (Fig. 6) and human DNA
accounted for more than 80\% of the total sequence reads in the mosquito
iDNA dataset.

\textbf{Discussion}

This study provides evidence that carrion flies are the superior method
for landscape scale biodiversity surveillance compared to both camera
traps and the other iDNA sampling methods (carrion fly iDNA identified
62 vertebrate species, sandfly iDNA detected 49 species, mosquito iDNA
detected 16 species, and camera traps identified 28 species). Previous
work has found that iDNA from carrion flies produced complementary
results when compared to more traditional methods of biomonitoring such
as camera traps (Gogarten et al., 2020; Rodgers et al., 2017). We found
a similar pattern to Rodgers et al. (2017) in their comparison of
carrion flies to existing camera trap data in that even with a small
sample size and effort, carrion flies used as iDNA samplers were able to
profile most of the diversity that had been found with long-term camera
trapping while also revealing new species diversity. However, with our
larger sampling effort, we were able to show that carrion flies were
much more effective at describing diversity at the landscape scale and
species richness likely would have increased for each iDNA sampler if we
had increased our sampling effort, particularly at the site level,
whereas species richness had fully saturated by 1,500 camera-days (Fig.
2 and Fig. 4).

While camera traps revealed lower gamma diversity than carrion flies and
sandflies, they had the greatest median species richness at sites (alpha
diversity) compared to each of the iDNA samplers. This points to camera
traps reliably revealing the same suite of species from across sites
resulting in higher alpha diversity but lower overall gamma diversity
when compared to iDNA samplers. The missing species richness exhibited
by camera traps at the landscape scale of our study is likely due to
camera traps preferentially targeting larger-bodied mammals (Burton et
al., 2015; O'Brien \& Kinnaird, 2011). We found support for this as
camera traps revealed the greatest diversity of carnivore species and
ungulate species while missing most of the iDNA-described diversity of
the arboreal species and the smaller-bodied mammals.

The improved biodiversity detection with carrion flies over other iDNA
sources and camera traps comes with additional logistical advantages.
Carrion flies can be trapped with inexpensive, homemade traps compared
to the more expensive camera traps or the UV LED CDC light traps used to
capture sandflies and mosquitoes. These homemade traps also did not
require batteries compared to the UV LED CDC light traps which require
6V batteries that need to be changed approximately every 24 hours. Our
trap design (Appendix S1: Fig. S2) was effective at attracting and
trapping carrion flies while at the same time preventing individuals
from accessing the bait. Using a blocking primer for the species used as
bait is also an option when utilizing carrion flies as an iDNA source.
Carrion flies are thus much more amenable to sampling in remote
landscapes where transporting and shipping pre-made traps or bulky
equipment can be difficult. We also identified the most species with
carrion flies compared to the other iDNA samplers despite a much lower
sampling effort of 3 traps per site compared to 27 traps per site for
mosquitos and sandflies, which resulted in much fewer individual carrion
flies (n = 1,759) compared to the total number of sandflies (n = 56,774)
and mosquitos (n = 4,776). Carrion fly samples had the greatest
amplification efficiency of non-human vertebrates (Table 1) despite only
5 individual flies coextracted compared with 15 mosquitos and 50
sandflies, although amplification efficiency reflected all captured
sandflies (not only blood-fed females) and did not include human DNA
which was highest in mosquito samples.

Although carrion flies were the most effective iDNA sampler, we wanted
to examine whether the gamma and alpha diversity described varied
substantially among iDNA sources to assess the effectiveness of
sandflies and mosquitos as biodiversity samplers. We found that the
overlap in vertebrate communities from each iDNA source was generally
supported at the landscape scale, however, the different iDNA sources
detected different communities of species at each site, and the same
sites were typically distant in ordination space. Although carrion flies
were the most effective sampler for detecting gamma diversity, we found
carrion flies were less redundant with mosquitos and sandflies for
detecting local biodiversity and carrion flies missed 85\% and 67\% of
the species occurrences from across all sites in the sandfly data and in
the mosquito data respectively, although most of these misses
corresponded to the dominant species in either the sandfly or mosquito
data as shown in Appendix S1: Fig. S4. This points to needing a
substantially larger sampling effort at the site level for iDNA methods,
particularly carrion flies, to saturate species detections.

Aside from the effect of sampling effort on diverging alpha diversity
profiles, some of the mismatch is explained by the differing feeding
ecologies of each insect group. We detected substantial taxonomic biases
in vertebrate detections among iDNA sources that were likely driven by
differences in the host preferences of the common mosquito and sandfly
species. Aside from domestic dog, we found evidence of contrasting
feeding preferences for vector taxa that were consistent with to their
known relationships with host species. Sandflies overwhelmingly targeted
armadillos (\emph{Dasypus novemcinctus} and \emph{Dasypus kappleri} )
which aligns with our understanding of both the relationship between
armadillos and sandflies (Akhoundi et al., 2016; Alexander, 2000;
Lainson et al., 1979) as well as armadillos as an important host species
for the life cycle of \emph{Leishmania} parasites (Lainson et al., 1979;
Lainson \& Shaw, 1989), which are transmitted by female sandfly vectors.
Kocher et al. (2017) reported the first direct evidence of
sandfly-armadillo interaction using metabarcoding of sandflies, and our
findings overwhelmingly support this finding. Mosquitos also exhibited
strong host preferences for humans, which even after rigorous quality
control to eliminate contamination was the dominant iDNA data source
representing 80\% of the total reads. While many species of mosquito are
opportunistic feeders, there are a number of species that target humans
including \emph{Aedes aegypti} which is the main vector for dengue,
yellow fever, and chikungunya and is found throughout tropical and
sub-tropical regions worldwide (McBride, 2016; Zwiebel \& Takken, 2004).
Interestingly, our metabarcoding data is corroborated by previous
studies spanning the Amazon region that show upwards of 80\% of \emph{A.
aegypti} bloodmeals contain human DNA (McBride, 2016). Compared to the
targeted feeding behavior of sandflies and mosquitos (as well as leeches
shown by Drinkwater et al. 2019), the feeding ecology of carrion flies
may offer the least biased view of the diversity and relative biomass of
species across a landscape because their feeding behavior is likely
driven by both the presence of carrion and scat (Calvignac-Spencer et
al., 2013; Rodgers et al., 2017). Given these findings, the feeding
ecologies of each insect group should be an important consideration
because choice of iDNA sampler can lead to mismatches in the vertebrate
diversity profiles, especially with smaller sampling sizes.

By exploring the biases associated with each sampling type, we found
evidence that carrion flies are significantly more effective in
describing landscape level biodiversity when compared to diversity found
with camera traps and other iDNA samplers, even with the least sampling
effort and smallest sample size. We found that camera traps predictably
were biased towards larger-bodied mammals and away from arboreal and
smaller-bodied species, thus missing much of the biodiversity described
with iDNA samplers, but also had the highest species richness at the
site-level. Sandflies and mosquitos show feeding behavior targeted at
armadillos and humans, respectively. Although sandflies and mosquitos
were inferior samplers for large-scale biodiversity surveillance
compared to carrion flies, they add value to existing
host-vector-pathogen surveillance efforts. This is particularly relevant
in forest frontiers where vectors maintain enzootic sylvatic cycles
which can cause disease spillover into humans and/or domestic animals if
they are bitten by these infected vectors (Figueiredo, 2007; Vasconcelos
et al., 2001). Additionally, our findings support the known ecological
relationships between these vector taxa and their hosts thus further
validating metabarcoding results within the larger realm of ecological
research. With the advances in high-throughput sequencing and the
steadily decreasing expense of metabarcoding, iDNA should be used as an
effective and efficient method for biomonitoring.

\textbf{Acknowledgements}

Support for this research was funded by The British Council Department
of Business, Innovation and Skills through their Global Innovation
Initiative and through Oregon State University. We thank everyone who
assisted with establishing sites, with data collection, and with sample
processing including C.J.S.P. Vieira, J.R. Kubiszeski, C. Benishek, S.
Schmidt, and A. Pepper. We would also like to acknowledge the landowners
across Sinop, Mato Grosso who allowed us access to their properties for
insect collection and camera trapping. The Center for Genome Research
and Biocomputing (CGRB) at Oregon State University provided access to
the Illumina HiSeq 3000 for all metabarcoding. We also thank the Levi
lab members for reviewing early versions of this manuscript.

\textbf{References}

Akhoundi, M., Kuhls, K., Cannet, A., Votypka, J., Marty, P., Delaunay,
P., \& Sereno, D. (2016). A historical overview of the classification,
evolution, and dispersion of Leishmania parasites and
sandflies.\emph{PLoS Neglected Tropical Diseases} , \emph{10} (3),
e0004349.

Alexander, B. (2000). Sampling methods for phlebotomine
sandflies.\emph{Medical and Veterinary Entomology} , \emph{14} (2),
109--122.

Batovska, J., Lynch, S. E., Cogan, N. O. I., Brown, K., Darbro, J. M.,
Kho, E. A., \& Blacket, M. J. (2018). Effective mosquito and arbovirus
surveillance using metabarcoding. \emph{Molecular Ecology Resources}
,\emph{18} (1), 32--40.

Bell, K. L., Burgess, K. S., Botsch, J. C., Dobbs, E. K., Read, T. D.,
\& Brosi, B. J. (2019). Quantitative and qualitative assessment of
pollen DNA metabarcoding using constructed species
mixtures.\emph{Molecular Ecology} , \emph{28} (2), 431--455.

Bohmann, K., Evans, A., Gilbert, M. T. P., Carvalho, G. R., Creer, S.,
Knapp, M., Douglas, W. Y., \& De Bruyn, M. (2014). Environmental DNA for
wildlife biology and biodiversity monitoring. \emph{Trends in Ecology
and Evolution} , \emph{29} (6), 358--367.

Burton, A. C., Neilson, E., Moreira, D., Ladle, A., Steenweg, R.,
Fisher, J. T., Bayne, E., \& Boutin, S. (2015). Wildlife camera
trapping: A review and recommendations for linking surveys to ecological
processes. \emph{Journal of Applied Ecology} , \emph{52} (3), 675--685.

Calvignac-Spencer, S., Merkel, K., Kutzner, N., Kuhl, H., Boesch, C.,
Kappeler, P. M., Metzger, S., Schubert, G., \& Leendertz, F. H. (2013).
Carrion fly-derived DNA as a tool for comprehensive and cost-effective
assessment of mammalian biodiversity. \emph{Molecular Ecology}
,\emph{22} (4), 915--924.

Chao, A., Gotelli, N. J., Hsieh, T. C., Sander, E. L., Ma, K. H.,
Colwell, R. K., \& Ellison, A. M. (2014). Rarefaction and extrapolation
with Hill numbers: A framework for sampling and estimation in species
diversity studies. \emph{Ecological Monographs} , \emph{84} (1), 45--67.

De Barba, M., Miquel, C., Boyer, F., Mercier, C., Rioux, D., Coissac,
E., \& Taberlet, P. (2014). DNA metabarcoding multiplexing and
validation of data accuracy for diet assessment: Application to
omnivorous diet. \emph{Molecular Ecology Resources} , \emph{14} (2),
306--323.

Drinkwater, R., Jucker, T., Potter, J. H. T., Swinfield, T., Coomes, D.
A., Slade, E. M., Gilbert, M. T. P., Lewis, O. T., Bernard, H.,
Struebig, M. J., Clare, E. L., \& Rossiter, S. J. (2020). Leech
blood-meal iDNA reveals differences in Bornean mammal diversity across
habitats. \emph{Molecular Ecology} .

Drinkwater, R., Schnell, I. B., Bohmann, K., Bernard, H., Veron, G.,
Clare, E. L., Gilbert, M. T. P., \& Rossiter, S. J. (2019). Using
metabarcoding to compare the suitability of two blood-feeding leech
species for sampling mammalian diversity in North Borneo.\emph{Molecular
Ecology Resources} , \emph{19} (1), 105--117.

Figueiredo, L. T. M. (2007). Emergent arboviruses in
Brazil.\emph{Revista Da Sociedade Brasileira de Medicina Tropical}
,\emph{40} (2), 224--229.

Gogarten, J. F., Hoffmann, C., Arandjelovic, M., Sachse, A., Merkel, K.,
Dieguez, P., Agbor, A., Angedakin, S., Brazzola, G., Jones, S.,
Langergraber, K. E., Lee, K., Marrocoli, S., Murai, M., Sommer, V.,
Kuhl, H., Leendertz, F. H., \& Calvignac-Spencer, S. (2020). Fly-derived
DNA and camera traps are complementary tools for assessing mammalian
biodiversity. \emph{Environmental DNA} , \emph{2} (1), 63--76.

Griffiths, R. I., Whiteley, A. S., O'Donnell, A. G., \& Bailey, M. J.
(2000). Rapid method for coextraction of DNA and RNA from natural
environments for analysis of ribosomal DNA-and rRNA-based microbial
community composition. \emph{Applied and Environmental Microbiology}
,\emph{66} (12), 5488--5491.

Hsieh, T. C., Ma, K. H., \& Chao, A. (2016). INEXT: an R package for
rarefaction and extrapolation of species diversity (Hill
numbers).\emph{Methods in Ecology and Evolution} , \emph{7} (12),
1451--1456.

Kocher, A., de Thoisy, B., Catzeflis, F., Valiere, S., Banuls, A.-L., \&
Murienne, J. (2017). iDNA screening: Disease vectors as vertebrate
samplers. \emph{Molecular Ecology} , \emph{26} (22), 6478--6486.

Kraaijeveld, K., De Weger, L. A., Ventayol Garcia, M., Buermans, H.,
Frank, J., Hiemstra, P. S., \& Den Dunnen, J. T. (2015). Efficient and
sensitive identification and quantification of airborne pollen using
next-generation DNA sequencing. \emph{Molecular Ecology Resources}
,\emph{15} (1), 8--16.

Lainson, R., \& Shaw, J. J. (1989). Leishmania (Viannia) naiffi sp. N.,
a parasite of the armadillo, Dasypus novemcinctus (L.) in Amazonia
Brazil. \emph{Annales de Parasitologie Humaine et Comparee} ,\emph{64}
(1), 3--9.

Lainson, R., Shaw, J. J., Ward, R. D., Ready, P. D., \& Naiff, R. D.
(1979). Leishmaniasis in Brazil XIII: isolation of Leishmania from
armadillos (Dasypus novemcinctua), and observations on the epidemiology
of cutaneous leishmaniasis in north Para State. \emph{Transactions of
the Royal Society of Tropical Medicine and Hygiene} , \emph{73} (2),
239--242.

Lee, P. S., Gan, H. M., Clements, G. R., \& Wilson, J. J. (2016). Field
calibration of blowfly-derived DNA against traditional methods for
assessing mammal diversity in tropical forests. \emph{Genome} ,\emph{59}
(11), 1008--1022.

Lynggaard, C., Nielsen, M., Santos-Bay, L., Gastauer, M., Oliveira, G.,
\& Bohmann, K. (2019). Vertebrate diversity revealed by metabarcoding of
bulk arthropod samples from tropical forests. \emph{Environmental DNA}
,\emph{1} (4), 329--341.

Massey, A. L., Roffler, G. H., Vermuel, T., Allen, J., \& Levi, T. (in
press). Comparison of mechanical sorting and DNA metabarcoding for diet
analysis with fresh and degraded wolf scats. \emph{Ecosphere} .

McBride, C. S. (2016). Genes and odors underlying the recent evolution
of mosquito preference for humans. \emph{Current Biology} , \emph{26}
(1), R41--R46.

Mittermeier, R. A., Mittermeier, C. G., Brooks, T. M., Pilgrim, J. D.,
Konstant, W. R., Da Fonseca, G. A., \& Kormos, C. (2003). Wilderness and
biodiversity conservation. \emph{Proceedings of the National Academy of
Sciences} , \emph{100} (18), 10309--10313.

O'Brien, T. G., \& Kinnaird, M. F. (2011). Estimation of species
richness of large vertebrates using camera traps: An example from an
Indonesian rainforest. In \emph{Camera Traps in Animal Ecology} (pp.
233--252). Springer.

Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R.,
O'hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Wagner,
H., \& Oksanen, M. J. (2013). Package ``vegan.'' \emph{Community Ecology
Package, Version,} \emph{2} (9), 1--295.

Riaz, T., Shehzad, W., Viari, A., Pompanon, F., Taberlet, P., \&
Coissac, E. (2011). ecoPrimers: Inference of new DNA barcode markers
from whole genome sequence analysis. \emph{Nucleic Acids Research}
,\emph{39} (21), e145--e145.

Rodgers, T. W., Xu, C. C., Giacalone, J., Kapheim, K. M., Saltonstall,
K., Vargas, M., Yu, D. W., Somervuo, P., McMillan, W. O., \& Jansen, P.
A. (2017). Carrion fly-derived DNA metabarcoding is an effective tool
for mammal surveys: Evidence from a known tropical mammal
community.\emph{Molecular Ecology Resources} , \emph{17} (6),
e133--e145.

Roffler, G. H., Allen, J., Massey, A. L., \& Levi, T. (2021).
Metabarcoding of fecal DNA shows dietary diversification in wolves
substitutes for ungulates in an island archipelago. \emph{Ecosphere}
,\emph{12} (1), e03297.

Schnell, I. B., Bohmann, K., Schultze, S. E., Richter, S. R., Murray, D.
C., Sinding, M. H. S., Bass, D., Cadle, J. E., Campbell, M. J., Dolch,
R., \& Edwards, D. P. (2018). Debugging diversity---A pan-continental
exploration of the potential of terrestrial blood-feeding leeches as a
vertebrate monitoring tool. \emph{Molecular Ecology Resources}
,\emph{18} (6), 1282--1298.

Schnell, I. B., Thomsen, P. F., Wilkinson, N., Rasmussen, M., Jensen, L.
R., Willerslev, E., Bertelsen, M. F., \& Gilbert, M. T. (2012).
Screening mammal biodiversity using DNA from leeches. \emph{Current
Biology} , \emph{22} (8), R262--R263.

Shaw, J. L., Clarke, L. J., Wedderburn, S. D., Barnes, T. C., Weyrich,
L. S., \& Cooper, A. (2016). Comparison of environmental DNA
metabarcoding and conventional fish survey methods in a river
system.\emph{Biological Conservation} , \emph{197} , 131--138.

Simister, R. L., Schmitt, S., \& Taylor, M. W. (2011). Evaluating
methods for the preservation and extraction of DNA and RNA for analysis
of microbial communities in marine sponges. \emph{Journal of
Experimental Marine Biology and Ecology} , \emph{397} (1), 38--43.

Stat, M., Huggett, M. J., Bernasconi, R., DiBattista, J. D., Berry, T.
E., Newman, S. J., Harvey, E. S., \& Bunce, M. (2017). Ecosystem
biomonitoring with eDNA: metabarcoding across the tree of life in a
tropical marine environment. \emph{Scientific Reports} , \emph{7} (1),
1--11.

Valentini, A., Taberlet, P., Miaud, C., Civade, R., Herder, J., Thomsen,
P., Bellemain, E., Coissac, E., Boyer, F., \& Gaboriaud, C. (2016).
Next-generation monitoring of aquatic biodiversity using environmental
DNA metabarcoding. \emph{Molecular Ecology} , \emph{25} (4), 929--942.

van der Heyde, M., Bunce, M., Wardell-Johnson, G., Fernandes, K., White,
N. E., \& Nevill, P. (2020). Testing multiple substrates for terrestrial
biodiversity monitoring using environmental DNA
metabarcoding.\emph{Molecular Ecology Resources} , \emph{20} (3),
732--745.

Varotto, C., Pindo, M., Bertoni, E., Casarotto, C., Camin, F., Girardi,
M., Maggi, V., \& Cristofori, A. (2021). A pilot study of eDNA
metabarcoding to estimate plant biodiversity by an alpine glacier core
(Adamello glacier, North Italy). \emph{Scientific Reports} ,\emph{11}
(1), 1--3.

Vasconcelos, P. F., Travassos da Rosa, A., Rodrigues, S. G., Travassos
da Rosa, E. S., Degallier, N., \& Travassos da Rosa, J. F. (2001).
Inadequate management of natural ecosystem in the Brazilian Amazon
region results in the emergence and reemergence of
arboviruses.\emph{Cadernos de Saude Publica} , \emph{17} , S155--S164.

Weiskopf, S. R., McCarthy, K. P., Tessler, M., Rahman, H. A., McCarthy,
J. L., Hersch, R., Faisal, M. M., \& Siddall, M. E. (2018). Using
terrestrial haematophagous leeches to enhance tropical biodiversity
monitoring programmes in Bangladesh. \emph{Journal of Applied Ecology}
,\emph{55} (4), 2071--2081.

Yu, D. W., Ji, Y., Emerson, B. C., Wang, X., Ye, C., Yang, C., \& Ding,
Z. (2012). Biodiversity soup: Metabarcoding of arthropods for rapid
biodiversity assessment and biomonitoring. \emph{Methods in Ecology and
Evolution} , \emph{3} (4), 613--623.

Zhang, J., Kobert, K., Flouri, T., \& Stamatakis, A. (2014). PEAR: a
fast and accurate Illumina Paired-End reAd mergeR.\emph{Bioinformatics}
, \emph{30} (5), 614--620.

Zwiebel, L. J., \& Takken, W. (2004). Olfactory regulation of
mosquito-host interactions. \emph{Insect Biochemistry and Molecular
Biology} , \emph{34} (7), 645--652.

\textbf{Data Accessibility}

All metabarcoding data collated for analyses will be available as
spreadsheets in the Supplemental Information.

\textbf{Author Contributions}

ALM led project design, fieldwork, laboratory work, analysis, and
writing. RB was involved with project conceptualization and fieldwork
and laboratory work design. DJFS was instrumental in assisting with
fieldwork and data collection. JA helped with early laboratory protocols
and library preparation for metabarcoding. PRL and CSSB carried out
camera trapping and image processing and provided raw camera trap data.
MSF and GRC helped with project conceptualization and design. CP and TL
were involved with project conceptualization and project design and
provided substantial feedback on the manuscript.

\textbf{Table 1:} Efficiency of each insect taxa as an iDNA sampler used
to profile vertebrate diversity from the blood (sandflies and mosquitos)
or carrion meals (carrion flies). Human DNA is excluded.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
carrion flies\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
sandflies\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
mosquitos\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Total number of individuals captured (and processed)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
1,759 (1,759)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
56,774 (48,686)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
4,776 (4,776)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Total number of pooled samples processed for metabarcoding\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
370\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
976\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
321\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Total number of amplified samples used for analysis post quality control
measures\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
265\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
354\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
78\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Percentage of usable samples\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
71.6\%\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
36.3\%\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
24.2\%\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2:} Vertebrate diversity found using iDNA samplers and
camera trapping methods. Values show a species' RAI in a given dataset.
An ``X'' indicates that a species was present at a RAI value less than
1\% (a rare species).\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& & \textbf{iDNA: carrion fly} & \textbf{iDNA: sandfly} & \textbf{iDNA:
mosquito} & \textbf{Camera traps}\tabularnewline
\midrule
\endhead
\textbf{Mammals} & \textbf{Mammals} & \textbf{Mammals} &
\textbf{Mammals} & \textbf{Mammals} & \textbf{Mammals}\tabularnewline
\textbf{Artiodactyla} & \textbf{Artiodactyla} & \textbf{Artiodactyla} &
\textbf{Artiodactyla} & \textbf{Artiodactyla} &
\textbf{Artiodactyla}\tabularnewline
Bos taurus & cattle & \textbf{0.36} & \textbf{0.02} & \textbf{0.03}
&\tabularnewline
Mazama americana & red brocket & \textbf{0.01} & \textbf{0.01} & &
\textbf{X}\tabularnewline
Mazama gouazoubira & brown brocket & & & & \textbf{0.03}\tabularnewline
Mazama nemorivaga & Amazonian brown brocket & \textbf{0.01} & &
\textbf{X} &\tabularnewline
Mazama spp. & deer & & \textbf{0.01} & & \textbf{0.01}\tabularnewline
Ovis aries & sheep & \textbf{0.03} & & &\tabularnewline
Pecari tajacu & collared peccary & \textbf{0.04} & \textbf{0.01} &
\textbf{0.01} & \textbf{0.03}\tabularnewline
Tayassu pecari & white-lipped peccary & \textbf{0.04} & \textbf{X} & &
\textbf{0.01}\tabularnewline
\textbf{Carnivora} & \textbf{Carnivora} & \textbf{Carnivora} &
\textbf{Carnivora} & \textbf{Carnivora} &
\textbf{Carnivora}\tabularnewline
Canis lupus familiaris & domestic dog & \textbf{0.23} & \textbf{0.16} &
\textbf{0.15} & \textbf{X}\tabularnewline
Cerdocyon thous & crab-eating fox & & & & \textbf{0.01}\tabularnewline
Eira barbara & tayra & \textbf{0.01} & & & \textbf{X}\tabularnewline
Felis catus & domestic cat & \textbf{0.01} & \textbf{0.01} &
\textbf{0.01} & \textbf{X}\tabularnewline
Leopardus pardalis & ocelot & & & & \textbf{0.01}\tabularnewline
Nasua nasua & coati & \textbf{0.03} & \textbf{X} & \textbf{X} &
\textbf{0.01}\tabularnewline
Panthera onca & jaguar & & & & \textbf{X}\tabularnewline
Puma concolor & cougar & & \textbf{0.01} & & \textbf{X}\tabularnewline
Puma spp. & cougar & & \textbf{0.02} & &\tabularnewline
Puma yagouaroundi & jaguarundi & & & & \textbf{X}\tabularnewline
Speothos venaticus & bush dog & \textbf{X} & \textbf{X} &
&\tabularnewline
\textbf{Chiroptera} & \textbf{Chiroptera} & \textbf{Chiroptera} &
\textbf{Chiroptera} & \textbf{Chiroptera} &
\textbf{Chiroptera}\tabularnewline
Micronycteris hirsuta & hairy big-eared bat & & \textbf{X} &
&\tabularnewline
Phyllostomus hastatus & greater spear-nosed bat & \textbf{X} &
\textbf{X} & &\tabularnewline
Rhogeessa spp. & bat & & & \textbf{X} &\tabularnewline
\textbf{Dasypodidae} & \textbf{Dasypodidae} & \textbf{Dasypodidae} &
\textbf{Dasypodidae} & \textbf{Dasypodidae} &
\textbf{Dasypodidae}\tabularnewline
Cabassous unicinctus & southern naked-tailed armadillo & \textbf{0.01} &
& & \textbf{X}\tabularnewline
Dasypus kappleri & greater long-nosed armadillo & & \textbf{0.03} &
&\tabularnewline
Dasypus novemcinctus & nine-banded armadillo & \textbf{0.01} &
\textbf{0.17} & \textbf{0.01} & \textbf{0.04}\tabularnewline
Dasypus septemcinctus & seven-banded armadillo & & \textbf{0.01} &
&\tabularnewline
Euphractus sexcinctus & six-banded armadillo & \textbf{0.01} &
\textbf{0.01} & & \textbf{X}\tabularnewline
Myrmecophaga tridactyla & giant anteater & & \textbf{X} & \textbf{X} &
\textbf{X}\tabularnewline
Priodontes maximus & giant armadillo & & & & \textbf{X}\tabularnewline
\textbf{Didelphimorphia} & \textbf{Didelphimorphia} &
\textbf{Didelphimorphia} & \textbf{Didelphimorphia} &
\textbf{Didelphimorphia} & \textbf{Didelphimorphia}\tabularnewline
Caluromys lanatus & brown-eared woolly opossum & \textbf{0.04} &
\textbf{X} & &\tabularnewline
Caluromys philander & bare-tailed woolly opossum & \textbf{X} & &
&\tabularnewline
Didelphis marsupialis & common opossum & \textbf{0.01} & \textbf{0.01} &
&\tabularnewline
Gracilinanus spp. & opossum & \textbf{0.01} & & &\tabularnewline
Marmosa murina & common mouse opossum & \textbf{0.01} & &
&\tabularnewline
Metachirus nudicaudatus & brown four-eyed opossum & \textbf{0.01} &
\textbf{X} & &\tabularnewline
Micoureus demerarae & woolly mouse opossum & & \textbf{X} &
&\tabularnewline
Philander opossum & gray four-eyed opossum & & \textbf{X} &
&\tabularnewline
\textbf{Lagomorpha} & \textbf{Lagomorpha} & \textbf{Lagomorpha} &
\textbf{Lagomorpha} & \textbf{Lagomorpha} &
\textbf{Lagomorpha}\tabularnewline
Sylvilagus spp. & rabbit & & \textbf{0.01} & &\tabularnewline
\textbf{Perissodactyla} & \textbf{Perissodactyla} &
\textbf{Perissodactyla} & \textbf{Perissodactyla} &
\textbf{Perissodactyla} & \textbf{Perissodactyla}\tabularnewline
Tapirus terrestris & Brazilian tapir & \textbf{0.09} & \textbf{0.03} &
\textbf{0.01} & \textbf{0.09}\tabularnewline
\textbf{Pilosa} & \textbf{Pilosa} & \textbf{Pilosa} & \textbf{Pilosa} &
\textbf{Pilosa} & \textbf{Pilosa}\tabularnewline
Tamandua tetradactyla & southern anteater & \textbf{X} & \textbf{0.01} &
& \textbf{X}\tabularnewline
\textbf{Primates} & \textbf{Primates} & \textbf{Primates} &
\textbf{Primates} & \textbf{Primates} & \textbf{Primates}\tabularnewline
Alouatta spp. & howler monkey & \textbf{0.02} & & &\tabularnewline
Ateles spp. & spider monkey & \textbf{0.02} & \textbf{X} &
&\tabularnewline
Cebus spp. & capuchin & \textbf{0.11} & \textbf{0.01} & \textbf{0.01} &
\textbf{0.01}\tabularnewline
Chiropotes albinasus & white-nosed saki & & \textbf{X} &
&\tabularnewline
Plecturocebus cupreus & coppery titi & \textbf{X} & \textbf{X} &
&\tabularnewline
Plecturocebus vieirai & Vieira's titi & & & & \textbf{X}\tabularnewline
\textbf{Rodentia} & \textbf{Rodentia} & \textbf{Rodentia} &
\textbf{Rodentia} & \textbf{Rodentia} & \textbf{Rodentia}\tabularnewline
Coendou melanurus & black-tailed hairy dwarf porcupine & \textbf{X} &
\textbf{X} & &\tabularnewline
Coendou prehensilis & Brazilian porcupine & \textbf{0.01} & \textbf{X} &
& \textbf{X}\tabularnewline
Cuniculus paca & lowland paca & \textbf{X} & \textbf{X} & &
\textbf{0.04}\tabularnewline
Dactylomys dactylinus & Amazon bamboo rat & \textbf{X} & &
&\tabularnewline
Dasyprocta azarae & Azara's agouti & \textbf{0.01} & & \textbf{0.01} &
\textbf{0.06}\tabularnewline
Hydrochoerus hydrochaeris & capybara & \textbf{0.03} & \textbf{X} & &
\textbf{X}\tabularnewline
Makalata didelphoides & Brazilian spiny tree-rat & \textbf{X} & &
&\tabularnewline
Mus musculus & house mouse & \textbf{0.01} & \textbf{0.01} &
&\tabularnewline
Oecomys concolor & unicolored rice rat & & \textbf{X} & &\tabularnewline
Potos flavus & kinkajou & \textbf{0.01} & & &\tabularnewline
Proechimys spp. & spiny rat & & \textbf{X} & \textbf{X} &\tabularnewline
Pseudoryzomys simplex & Brazilian false rice rat & \textbf{X} & &
&\tabularnewline
Rattus spp. & rat & \textbf{0.01} & \textbf{X} & &\tabularnewline
Sciurus aestuans & Guianan squirrel & & & & \textbf{X}\tabularnewline
\textbf{Reptiles} & \textbf{Reptiles} & \textbf{Reptiles} &
\textbf{Reptiles} & \textbf{Reptiles} & \textbf{Reptiles}\tabularnewline
Paleosuchus trigonatus & smooth-fronted caiman & \textbf{0.01} & &
&\tabularnewline
\textbf{Amphibians} & \textbf{Amphibians} & \textbf{Amphibians} &
\textbf{Amphibians} & \textbf{Amphibians} &
\textbf{Amphibians}\tabularnewline
Leptodactylus pentadactylus & smoky jungle frog & & \textbf{X} &
&\tabularnewline
\textbf{Fish} & \textbf{Fish} & \textbf{Fish} & \textbf{Fish} &
\textbf{Fish} & \textbf{Fish}\tabularnewline
Hemibrycon jabonero & & \textbf{X} & & &\tabularnewline
Leporinus copelandii & & \textbf{X} & & &\tabularnewline
\textbf{Birds} & \textbf{Birds} & \textbf{Birds} & \textbf{Birds} &
\textbf{Birds} & \textbf{Birds}\tabularnewline
Amazona ochrocephala & yellow-crowned parrot & \textbf{X} & &
&\tabularnewline
bird spp. & & \textbf{X} & & &\tabularnewline
Buteo spp. & hawk & \textbf{0.01} & & &\tabularnewline
Cairina moschata & Muscovy duck & & \textbf{X} & &\tabularnewline
Cathartes aura & turkey vulture & \textbf{X} & \textbf{X} &
&\tabularnewline
Coccyzus erythropthalmus & black-billed cuckoo & \textbf{0.02} &
\textbf{X} & &\tabularnewline
Coragyps atratus & black vulture & \textbf{0.01} & \textbf{X} &
&\tabularnewline
Crotophaga ani & smooth-billed ani & \textbf{X} & & &\tabularnewline
Crypturellus tataupa & Tataupa tinamou & \textbf{0.01} & &
&\tabularnewline
Crypturellus undulatus & undulated tinamou & \textbf{0.02} & \textbf{X}
& &\tabularnewline
Fringilla spp. & finch & \textbf{X} & & &\tabularnewline
Gallus gallus & red junglefowl & \textbf{0.01} & \textbf{X} &
&\tabularnewline
Lepidocolaptes angustirostris & narrow-billed woodcreeper &
\textbf{0.01} & & &\tabularnewline
Lepidothrix coronata & blue-crowned manakin & & & \textbf{X}
&\tabularnewline
Meleagris gallopavo & turkey & \textbf{0.02} & \textbf{X} &
\textbf{0.07} &\tabularnewline
Nyctiphrynus spp. & poorwill & \textbf{X} & & &\tabularnewline
Oreopholus ruficollis & tawny-throated dotterel & & & \textbf{X}
&\tabularnewline
Piprites chloris & wing-barred piprites & \textbf{X} & &
&\tabularnewline
Psophia viridis & dark-winged trumpeter & & \textbf{X} &
&\tabularnewline
Ramphastos tucanus & white-throated tucan & & \textbf{X} &
&\tabularnewline
Rhea americana & greater rhea & \textbf{0.02} & & &\tabularnewline
Sakesphorus luctuosus & glossy antshrike & \textbf{0.01} & \textbf{X} &
&\tabularnewline
tanager spp. & tanager & \textbf{0.02} & & &\tabularnewline
Tiaris olivaceus & yellow-faced grassquit & \textbf{0.01} & &
&\tabularnewline
tinamou spp. & tinamou & \textbf{0.01} & & &\tabularnewline
Trogon viridis & green-backed trogon & \textbf{X} & & &\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure 1:} Map of the study region centered at Sinop, Mato
Grosso, Brazil (shown in purple). The area shaded in green is designated
as forest with our study sites labeled as pink circles with an example
of the typical landscape at a forested site shown in the photo. We
sampled a total of 39 sites using UV LED CDC light traps (for sandflies
and mosquitos) and 3 carrion fly traps and at 33 of these sites we also
used camera traps. The inset map of Brazil shows the Amazonia biome
highlighted in green with our study area highlighted in pink.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/StudyMap-Biomas-final/StudyMap-Biomas-final}

\end{center}
\end{figure}

\textbf{Figure 2:} Species accumulation curves based on the species
diversity found with either an iDNA sampler or with camera traps. Solid
lines indicate interpolation while dashed lines indicate extrapolation.
For the insect samples, extrapolation parameters were set to 1000 pooled
samples to reflect the high number of total sandfly pooled samples. A
pooled sample of insects is equal to either 50 sandflies, 15 mosquitos,
or 5 carrion flies that were used for DNA extraction and metabarcoding.
A camera day is equal to one 24-hour period for an individual camera.
The shaded region surrounding each line shows the 95\% confidence
intervals.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig2SppAccum/Fig2SppAccum}

\end{center}
\end{figure}

\textbf{Figure 3:} Relative abundance index (RAI) for each species
present within either an iDNA sampler dataset or the camera trapping
dataset. Species are divided into respective groups describing their
taxa or a major life history trait to compare the effectiveness of each
sampling method for describing vertebrate diversity.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/Fig3TaxaRAI/Fig3TaxaRAI}

\end{center}
\end{figure}

\textbf{Figure 4:} The median number of species found at each site for
each sampling type. Boxplots are bounded by the first and third
quantiles.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig4SppSite/Fig4SppSite}

\end{center}
\end{figure}

\textbf{Figure 5:} NMDS ordination showing overlap in vertebrate species
communities as revealed by each iDNA source. Both panels display the
same ordination with (A) showing the site labels colored by the iDNA
sampler and (B) showing significant species (p-value \textless{} 0.001)
as vectors.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/Fig5NMDS/Fig5NMDS}

\end{center}
\end{figure}

\textbf{Figure 6:} Proportion of dog, armadillo, cattle, and human
positive samples measured at each site for each iDNA source. Data points
show the proportion of total pooled samples at a site that were positive
for the respective species.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig6Feedings/Fig6Feedings}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

