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\begin{document}

\title{Koalas, friends, and foes -- the application of airborne eDNA for the
biomonitoring of threatened species}



\author[1]{Celine Frere}%
\author[2]{Nicola Jackson}%
\author[2]{Alejandro Oliveros Sandino}%
\author[2]{Sarah Ball}%
\author[2]{Daniel Powell}%
\affil[1]{The University of Queensland}%
\affil[2]{The University of Queensland - Saint Lucia Campus}%


\vspace{-1em}



  
  \date{May 19, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Perched high up in a Eucalyptus tree, swaying from side to side, lies a
sleepy koala unaware of the means spent each year trying to obtain
accurate baseline information about its presence. We have thrown all we
could at it, from wildlife surveys to night spotting, bioacoustics,
detection dogs, and drones equipped with thermal cameras. Yet, whilst
critical to its conservation and management efforts, finding a koala
remains an ambitious, time-consuming, and costly endeavour often
producing insufficient results. However, little did we know that traces
of koalas' presence and that of its predators along with other native,
domesticated, and invasive species, float in the air and can be detected
using metagenomics. This study and despite high levels of co-sampled
non-target DNA (e.g. humans and domesticated animals) confirms that
koalas, species belonging to the wallaby and possum family and threats
such as domestic dogs (a major predator contributing to koala population
declines) can successfully be detected by sampling airborne particles.
Together, it demonstrates the potential of airborne eDNA for the
detection of terrestrial wildlife under natural conditions and presents
achievable optimisation steps to increase its field applicability and
validity.%
\end{abstract}%



\sloppy


\textbf{Koalas, friends, and foes -- the application of airborne eDNA
for the biomonitoring of threatened species}

Fr\selectlanguage{ngerman}ère, C.H.\textsuperscript{1}; Jackson, N. \textsuperscript{1};
Oliveros, A. \textsuperscript{1}; Ball, S.J. ; Moreno,
J.\textsuperscript{1}; Powell, D. \textsuperscript{1}

School of Biological Sciences, University of Queensland, St Lucia, QLD,
Australia.

Corresponding author: c.frere@uq.edu.au

\textbf{Abstract}

Perched high up in a Eucalyptus tree, swaying from side to side, lies a
sleepy koala unaware of the means spent each year trying to obtain
accurate baseline information about its presence. We have thrown all we
could at it, from wildlife surveys to night spotting, bioacoustics,
detection dogs, and drones equipped with thermal cameras. Yet, whilst
critical to its conservation and management efforts, finding a koala
remains an ambitious, time-consuming, and costly endeavour often
producing insufficient results. However, little did we know that traces
of koalas' presence and that of its predators along with other native,
domesticated, and invasive species, float in the air and can be detected
using metagenomics. This study and despite high levels of co-sampled
non-target DNA (e.g. humans and domesticated animals) confirms that
koalas, species belonging to the wallaby and possum family and threats
such as domestic dogs (a major predator contributing to koala population
declines) can successfully be detected by sampling airborne particles.
Together, it demonstrates the potential of airborne eDNA for the
detection of terrestrial wildlife under natural conditions and presents
achievable optimisation steps to increase its field applicability and
validity.





\textbf{Introduction}

Curbing worldwide wildlife population declines will necessitate the
protection of their habitat (Tilman et al. 2017). Aside from political
willingness, targeted habitat protection requires robust baseline
information about wildlife that use and occupy it. Collating such
information, however, remains a challenging and costly endeavour. To
tackle these problems, scientists have been working hard developing
innovative technologies that improve the cost-effectiveness and
scalability of data collection on wildlife assemblages, including
employing thermal drones, detection dogs, radars, camera traps
and~bioacoustics devices (Cristescu et al. 2015, Hagens et al. 2018,
Hüppop et al. 2019, Beaver et al. 2020, Law et al. 2020). Whilst these
technologies have greatly expanded our capabilities to collect more
accurate baseline information on wildlife presence, the collation of
data on wildlife assemblages still comes at a significant technological
and human cost. To seek to identify the presence of multiple wildlife
species, ecologists must deploy a multitude of methods in synchrony,
which can include bioacoustics arrays, baited traits, camera traps and
human-led fauna transects (Mena et al. 2021). Hence, affordable and
scalable technology for the detection and monitoring of wildlife
assemblages across the landscape is a goal we should seek to achieve as
it would improve our ability to detect and track changes in wildlife
biodiversity patterns (e.g. species richness and community composition),
identify wildlife species or habitat of high conservation priority, and
underpin the development of successful management strategies.

Environmental DNA, known as eDNA is a promising technology to address
this issue.~ eDNA refers to the DNA that is shed into the environment as
wildlife move throughout the landscape, leaving previously unbeknownst
traces of their presence (Thomsen and Willerslev 2015). Already heavily
deployed in aquatic systems for at least two decades, the practicability
of utilising eDNA to detect species presence and assemblages is clearly
established (Hinz et al. 2022). For example, in 2016, aquatic eDNA
technology was first employed as part of environmental impact
assessments, demonstrating its capacity to meet regulatory standards and
obligations outlined by the International Association for Impact
Assessment (Hinz et al. 2022). However, to date, there is no such
established terrestrial analogue although recent studies have provided
promising results. Terrestrial wildlife's eDNA has, for instance, been
detected from samples collected from spider webs, permafrost, blood,
snow, soil, honey and aerosol spray runoff (Andersen et al. 2012,
Folloni et al. 2012, Schnell et al. 2012, Valentin et al. 2018, Ribani
et al. 2020, Gregori\selectlanguage{polish}č \selectlanguage{english}et al. 2022). Two most recent studies have also
demonstrated that airborne eDNA can be used to reassemble zoological
communities (Clare et al. 2022, Lynggaard et al. 2022). Together,
evidence suggest that airborne eDNA could be a promising avenue for the
identification of terrestrial wildlife assemblages as it is i)
non-invasive, ii) scalable, and iii) comparatively cost-effective given
it holds the potential to target species assemblages compared to single
species targeted approaches.~The feasibility of using airborne eDNA to
identify wildlife assemblages under natural conditions remains yet to be
tested.

Here, we sought to test the applicability of airborne eDNA particles for
the detection of an endangered Australian species, the koala
(\emph{Phascolarctos cinereus} ), and its co-occurring terrestrial
mammalian community in a natural setting. We demonstrate its successful
application to detecting koalas as well as other mammalian species in a
natural setting and discuss future steps for its continued improvement
and optimisation.

\textbf{Methods}

\textbf{Development and deployment of samplers}

Location of sampling was selected for its confirmed long-term koala
presence (Cardno 2020). In parallel to the deployment of the airborne
eDNA filters, targeted fauna surveys were undertaken to identify koala
presence and record their GPS location(s). We designed two types of air
filtering sample collection systems which were fitted with sterile
cheesecloth for the collection of airborne eDNA. The first air sampling
system was composed of a vertical air sampler which allowed for a flow
rate of 0.8m/min powered by a waterproof lithium battery pack. This
first system was then stationed elevated at approximately 60 cm above
ground by securing it to star pickets. The second air sampling system
was simply composed of a sterile cheesecloth filter set between sterile
stainless-steel plates and attached to the start pickets facing upright
in a horizontal position. These two sampling systems were deployed
across 11 sites for 72 hours. All equipment used in the deployment and
collection of samples was either purchased gamma sterilised and for
single use (e.g., storage tubes) or washed with a bleach solution and
autoclaved prior to use (e.g., stainless steel plates). Samples were
collected under the animal ethics number 2022/AE000765 from the
University of Queensland.

\textbf{DNA extraction and amplification.}

Cheesecloth filters were transferred to sterile 50 ml centrifuge tubes
with 10 ml of molecular grade PBS (Gibco) and were rocked gently using a
platform shaker for 30 mins at room temperature to dislodge particulate
matter. The cheesecloth filters were then removed and the PBS solution
centrifuged at 8,000 xg for 10 mins. The supernatant was removed, and
the pellet resuspended in 180 \selectlanguage{greek}µ\selectlanguage{english}l of Buffer ATL before transfer to a new
clean microfuge tube. For lysis, 20 \selectlanguage{greek}µ\selectlanguage{english}l of Proteinase K was added and
vortexed briefly to mix before being heated at 56\textsuperscript{o}C
for 1 hour.~ DNA was purified using a DNeasy Blood \& Tissue kit
(Qiagen) following manufacturer's protocol from step 3. DNA was eluted
in a total of 100 \selectlanguage{greek}µ\selectlanguage{english}l by passing 2 x 50 \selectlanguage{greek}µ\selectlanguage{english}l of Buffer AE through the spin
column and then stored at -20\textsuperscript{o}C.

Primers selected for this study targeting mitochondrial DNA were
previously employed by two recent studies and were validated for use in
similar applications (Clare et al. 2022, Lynggaard et al. 2022). DNA was
amplified with the primers 16Smam1 forward (5'-CGGTTGGGGTGACCTCGGA-3')
and 16Smam2 reverse (5'-GCTGTTATCCC-TAGGGTAACT-3') (Taylor 1996) to
produce a fragment of approximately 100 base pairs long (bp). PCR was
initially performed in 20 \selectlanguage{greek}µ\selectlanguage{english}L reactions consisting of, 10 \selectlanguage{greek}µ\selectlanguage{english}l AmpliTaq 360
mastermix (Thermo Scientific); 0.6 \selectlanguage{greek}µ\selectlanguage{english}M each of forward and reverse primer
and 2 \selectlanguage{greek}µ\selectlanguage{english}L DNA template. To reduce the presence of human contamination,
the human blocker (5'--3' GCGACCTCGGAGCAGAACCC--spacerC3) as reported by
(Vestheim and Jarman 2008a), was included in each reaction. The thermal
cycling profile was 95\textsuperscript{o}C for 10 min, followed by 40
cycles of 95\textsuperscript{o}C for 12 s, 59\textsuperscript{o}C for 30
s, and 70\textsuperscript{o}C for 25 s, with a final extension of
72\textsuperscript{o}C for 7 mins.~The PCR reactions were visualised on
a 1.5\% agarose gel alongside non-template controls and koala DNA
positive controls. Successfully amplified DNA fragments were selected
and subjected to a second round of PCR using primers that included a
sequencing tag to enable longer product for sequencing.
TSP1\_16Smam\_forward 5'-TCTACACTCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCGGTTG
GGGTGACCTCGGA-3' and TSP2\_16Smam\_reverse: 5'-GTCTCGTGGGCTCG
GAGATGTGTATAAGAGACAGGCTGTTATCCCTAGGGTAACT-3'. For the second-step PCR,
conditions were identical to above but for using 1 \selectlanguage{greek}µ\selectlanguage{english}l of the PCR product
from the first reaction and a lower annealing temperature of
55\textsuperscript{o}C. Samples were sent for amplicon sequencing using
the Illumina MiSeq Platform at the Australian Genome Research Facility.

An addition sample of DNA isolated from tissue collected from a skin
biopsy an Indo-Pacific bottlenose dolphin (\emph{Tursiops aduncus)} was
used as a positive control to check for cross-contamination from
demultiplexing and laboratory processing for high-throughput sequencing.

\textbf{Data analysis}

The two airborne eDNA samples collected from each site were combined and
treated as one sample to maximise the recovery of cellular material from
each site. The raw reads were firstly demultiplexed using QIIME2
(version 2022.8.0) (Estaki et al. 2020) based on the Illumina barcodes,
creating separate samples for each. FastQC (version 0.11.9)
(\emph{https://www.bioinformatics.babraham.ac.uk/projects/fastqc/}) was
used on the demultiplexed reads to assess the overall data quality
before and after adapter removal. Adapters were removed using Cutadapt
(Martin 2011) (version 2.8-2). Bowtie2 (Langmead and Salzberg 2012)
(version 2.2.5) and Samtools (Danecek et al. 2021) (version 1.10) were
used to align the reads to a reference human mitochondrial genome
sequence (accession number KX456569.1) and identify and remove
human/primate-associated sequences from the dataset. In some instances,
samples contained 99\% human DNA and hence were removed from all further
analyses (Supplementary Table 1).

To minimise the introduction of potentially erroneous ASVs, we used our
positive control sample (known DNA source; \emph{Tursiops aduncus} ) to
optimise the filtering and denoising parameters used in the DADA2
package (Callahan et al. 2016) in R (version 1.26.0). We did this by
varying the maximum expected errors from 1 to 2 and the truncation
quality score parameters from 2 to 30. Filtering for the maximum
expected error of 1 and truncation quality score of 30 significantly
decreased the abundance of all reads but did not remove erroneous ASVs.
Filtering and denoising parameters were therefore selected to maximise
data retention by using default settings: Trimming left of 19 bp for
forward reads and 21 bp for reverse reads, truncation length of 130 bp,
maximum number of ambiguous bases (N's) of 0, maximum expected errors of
2, truncation quality score of 2, removal of PhiX contamination set to
TRUE. DADA2 was then used to remove chimeras from the sequence data
using the removeBimeraDenovo function with the pooled method. We then
produced two datasets for downstream analyses, one including all forward
filtered and denoised reads (to maximise data retention) and the merged
forward and reverse filtered and denoised reads. We found that the use
of merged reads dramatically reduced the presence of detected
contaminant DNA (Figure S1).

The merged, filtered and denoised reads were then used to produce
amplicon sequence variants (ASVs) (Table S1), which clusters unique
sequence variants based on a 100\% sequence similarity. We then used
BLAST to match ASVs to mammalian mitochondrial DNA sequences filtered
from the NCBI's GenBank non-redundant (nr) database and made species
level taxonomic assignments to BLAST hits using the R package taxonomizr
(version 0.9.3). Taxonomic assignment was performed using a combination
of consensus between the 10 BLAST matches for each ASV and the
percentage of identity for each match to produce a trust score. If the
percentage identity of one or more matches was higher than the mean of
the 10 blast hits, only those higher matches were used to establish a
consensus for the assignment of the final taxonomy. The trust score was
transformed into a 0-1 score using the formula: trust score =
(percentage\_identify - 80) / (100 - 80). Only ASVs that returned a
mammalian mitochondrial DNA sequence BLAST match, had at least three
reads across all samples and did not match to primate mitochondrial
sequences were used for subsequent analyses.

A phylogenetic tree was constructed using the ASVs obtained after
taxonomic assignment and included relative abundance calculations,
normalized by library size. Reference sequences of relevant native and
introduced species identified by BLAST matches were also included in the
tree construction. These sequences and their accession numbers can be
found in Supplementary Table 2. The alignment of ASVs was carried out
with the MAAFT (Katoh and Standley 2013) algorithm, and a tree was
produced with FastTree (Price et al. 2010) (version 2.1.11). The
resulting set of ASVs were manually clustered based on three types of
evidence, 1) presence at the same site, 2) the best match to the
taxonomic reference, and 3) the normalised abundance. Where multiple,
highly similar ([?] 97\%) ASVs were present at the same site, the most
abundant ASV also had the highest similarity to the taxonomic reference
and was therefore chosen as the most accurate representative. This
process of manual curation removed the ambiguity of erroneous ASVs from
the dataset and aimed to ensure accurate representation of the species
present at the sampling sites. A dendrogram was then constructed using
this set of curated ASVs and relative abundance was calculated by
summing the read counts from the cluster, normalized by library size.
Reference sequences of relevant native and introduced animals were
included in the tree construction. The tree was then visualized and
refined in the Interactive Tree Of Life (iTOL) (Letunic and Bork 2007)
tool (version 4.4.0). Additional graphics were applied using Adobe
Illustrator.

\textbf{Results and Discussion}

In this study, we have demonstrated that the presence of our targeted
threatened species, the koala, and its co-occurring terrestrial
mammalian community can be detected from the collection of airborne eDNA
under natural conditions. All 11 sampled sites detected the presence of
terrestrial mammals (Figure 2) and using a custom taxonomic assignment
strategy, we identified the presence of nine taxonomic families, most of
which, apart from the Macropodidae and Phalangeridae
(kangaroos/wallabies and possums, respectively), were assigned to the
species level, including our primary target, the koala. Moreover, we
show that airborne eDNA enables the detection of both native and
introduced species simultaneously, highlighting the utility of this
untargeted sampling approach for the identification of potential
biodiversity threats. Dogs, for instance, are a known threat to koalas
(Beyer et al. 2018) and were detected from a high abundance of reads
across our 11 sampled sites (Figure 2). We also identified common, but
problematic, invasive species including the black rat (\emph{Rattus
rattus} ) and the hare (\emph{Lepus europaeus} ) (Barney et al. 2021,
Finlayson et al. 2022). We discuss below how continued methodological
optimisation will enable the resolution of some teething problems.

First, and most problematic, is the presence of high level of
non-informative co-sampled DNA (e.g. human and domesticated animals).
Even with taking the utmost care and using published human DNA blocking
primers (Vestheim and Jarman 2008b), we found that many reads were
identified as human (Supplementary Table 3). We found that 53\% of
reads, on average, were lost per sample as they were identified as human
DNA and a total of 5 samples were lost as only human DNA was amplified
(99\% human reads; Supplementary Table 1). While we know that human
co-sampling and contamination is a common re-occurring problem when
using eDNA (Harper et al. 2019, Leempoel et al. 2020), we also found
that, under natural conditions, precious sequencing reads are further
lost to non-informative domesticated species, such as cows and horses
(Figure 2). This is despite these animals not being identified in faunal
transects or prior human led field surveys at the sampling location. The
presence of such high abundance of non-informative co-sampled airborne
DNA is a challenge we ought to tackle as it likely outcompetes the lower
abundance of airborne DNA particles shed by and therefore collected from
lower-density species of interest including threatened, endangered, or
cryptic elusive species. Our high level of non-informative co-sampled
DNA might be one of the reasons why our only sample which detected the
presence of koala DNA (Site 11. Figure 2) was the filter located
directly beneath a tree occupied by a koala while two additional
sampling sites (Site 5 and 2) positioned 50 - 30 meters away from koalas
failed to detect their DNA. Whilst this high level of sensitivity in
detection range allows us to confirm koala presence with a high degree
of confidence, the use of alternative human DNA blocking primers
(Boessenkool et al. 2012) coupled with targeted primers to enrich the
DNA abundance of low density or low biomass target species prior to
Illumina sequencing may help increase our rate and range of detection of
low-density, low-biomass species like the koala. A targeted qPCR assay
approach will require additional investment in development but when used
in concert with an untargeted approach will allow for a more inclusive
representation of the occupancy of less abundant species.

Second, we found that our ability to assign species level taxonomic rank
was limited by the small target sequence length. While small sequence
length is often best when dealing with degraded DNA, a characteristic of
eDNA (Beng and Corlett 2020), we identified it imposed some limitations
to the taxonomic assignment of our ASVs. For instance, the lack of
genetic variation between the mountain and common brushtail possum (1 bp
difference) made it difficult for us to assign our ASV to either species
with high certainty even though our ASV was 100\% similar to the
mountain brushtail possum. This is because 1 bp difference between
sequences could easily fall within the margin of sequencing error
(Stoler and Nekrutenko 2021). Similarly, we were not able to disentangle
ASV 19 from a red-neck wallaby or a grey kangaroo because these
reference sequences differed by 1 bp. In contrast, we found that some of
our ASVs (ASV 14 and ASV 23) contained levels of genetic variation high
enough to make taxonomic assignments challenging. The Macropididae ASV
\#14, for instance, differed from both the Red-Necked and the Swamp
Wallaby reference sequence by 5 bp. It, however, phylogenetically
clustered with the swamp wallaby (Figure 1) because of a shared
conserved region separating them both from the red-necked wallaby. A
similar trend was identified for our detected ring-tailed possum ASV
(ASV \#23) which, while it clustered with our reference ring-tailed
possum sequence, nonetheless differed from it by 6 bp. Ecological
surveys of our sampling site detected the presence of those three
species indicating that, while imperfect at this stage, our taxonomic
assignments are likely correct. To continue to improve the robustness of
taxonomic assignment, we recommend considering the geographical
provenance of reference sequences because of expected geographic
patterns of genetic diversity. The ring-tailed possum 16S reference
sequence publicly available, for instance, came from an animal located
in Western Australia. We therefore propose that prior to deploying
airborne eDNA for detection of a suite of target terrestrial species, it
is critical to develop a relevant mitochondrial genomic database to
ensure accurate taxonomic resolution can be reached. This will include
assessing if the targeted sequence region(s) contain sufficient
variation to disentangle closely related species and are of geographic
relevance to your sampling location. Challenges to obtain material and
data from sensitive species groups can be overcome by engagement with
stakeholders, researchers, museums and the community. This is
particularly important to the deployment of airborne eDNA technology in
natural settings where, unlike zoological facilities (Clare et al.
2022), many closely related species overlap with each other and are only
differentiable by a few base pairs resulting in less refined taxonomic
resolution or potential misattributed taxonomic assignment.

Last and similar to other eDNA studies (Lusk 2014, Xing et al. 2022), we
demonstrate the importance of filtering stringency in the management of
DNA contamination (see Figure S1). DNA extracted from a skin biopsy of
the Indo-Pacific bottlenose dolphin (\emph{Tursiops aduncus} ) was used
to estimate and control for DNA contamination in our downstream
bioinformatic pipeline, as this species is not found at or near the
sampling location. Like many other studies, we did identify a
significant amount of DNA cross-contamination which was present in our
dolphin sample when we only used forward reads in our bioinformatic
pipeline. We, however, found that the merging of ASVs got rid of all DNA
cross-contamination which we underline as a necessary step in any future
eDNA studies (Supplementary Figure 1).

\textbf{Conclusion}

The sequencing of airborne eDNA using high-throughput technology is a
promising tool to survey the terrestrial ecosystem in a cost-effective
manner. We demonstrate that it is feasible to use filtered air samples
for the recovery of DNA from endangered and hard to find species, such
as the koala in its natural environment. Further, we report the
detection of 9 taxonomic groups across the surveyed landscape including
a range of endemic and introduced species. We propose that in order to
increase the representation of species of interest, a targeted approach
could be used in concert with a generic assay design with limited
additional effort. Nonetheless, this approach has the potential to
revolutionise ecosystem wildlife surveys to maximise habitat protection
efforts.

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\par\null

Data Accessibility and Benefit-Sharing.

All ASV produced are included in the supplement information. All raw
reads have been uploaded in GeneBank number PRJNA949380.

Authors contribution.

CHF: study design, analyses, data collection, manuscript write up and
editing, funding, supervision.

NJ: study design, analyses, data collection, manuscript write up and
editing.

OA: analyses, manuscript write up and editing.

SJB: data collection, manuscript editing.

JM: analyses, manuscript write up and editing.

DP: study design, analyses, manuscript write up and editing,
supervision.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1. Phylogenetic tree of ASVs after taxonomic assignment
at the Birkdale site.} Curated ASVs are visualized with a trust score of
the taxonomic assignment (represented by red bars next to the taxonomic
assignment). Reference sequences are represented by purple dots and the
ASV index (blue number) is next to each taxonomic assignment.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 2. Representation of the Presence/Absence of ASVs after
Taxonomic Assignment at Scattered Locations around the Birkdale
Site.}Curated ASVs are visualized with their total relative abundance in
reads per million (represented by red bars), and their presence/absence
at 11 different locations around the site (absent = blue cells, present
= orange cells). Two macropods, likely a Swamp Wallaby (\^{}
Macropodidae) and a Red-Necked Wallaby (*Macropodidae), were identified.

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

