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\title{Environmental DNA for biomonitoring}



\author[1]{Jan Pawlowski}%
\author[2]{Aurelie Bonin}%
\author[3]{Frédéric Boyer}%
\author[4]{Tristan Cordier}%
\author[5]{Pierre Taberlet}%
\affil[1]{University of Geneva}%
\affil[2]{Università degli Studi di Milano}%
\affil[3]{Universite Grenoble Alpes}%
\affil[4]{Nowegian Research Center}%
\affil[5]{Université Joseph Fourier}%


\vspace{-1em}



  
  \date{June 17, 2024}


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Environmental DNA for biomonitoring

Jan Pawlowski\textsuperscript{1,2,3}, Aur\selectlanguage{ngerman}élie Bonin\textsuperscript{4},
Frédéric Boyer\textsuperscript{5}, Tristan Cordier\textsuperscript{1,6},
Pierre Taberlet\textsuperscript{5,7}

\textsuperscript{1} Department of Genetics and Evolution, University of
Geneva, Geneva, Switzerland

\textsuperscript{2} Institute of Oceanology, Polish Academy of Sciences,
Sopot, Poland

\textsuperscript{3} ID-Gene Ecodiagnostics, Geneva, Switzerland

\textsuperscript{4} Department of Environmental Science and Policy,
Università degli Studi di Milano, Milano, Italy

\textsuperscript{5} Laboratoire d'Ecologie Alpine (LECA), CNRS,
Université Grenoble Alpes, Grenoble, France

\textsuperscript{6} NORCE Climate, NORCE Norwegian Research Centre AS,
Bjerknes Centre for Climate Research, Jahnebakken 5, 5007 Bergen, Norway

\textsuperscript{7} Tromsø Museum, UiT -- The Arctic University of
Norway, Tromsø, Norway

Corresponding author

Jan.Pawlowski@unige.ch

In 2012, Molecular Ecology published a special issue on environmental
DNA, which provided an overview of the field of eDNA research and
presented a selection of papers on eDNA studies (Taberlet, Coissac,
Hajibabaei, \& Rieseberg, 2012). This special issue also introduced the
concept of Biomonitoring 2.0, advocating for the use of DNA-based
identification of taxa in biodiversity surveys and ecosystem assessment
(Baird \& Hajibabaei, 2012). Since then, hundreds of papers have been
published covering various aspects of eDNA-based biomonitoring from
single-species detection to community studies and environmental impact
assessments. Numerous reviews have summarized these studies for both
freshwater and marine environments (e.g. Bohmann et al., 2014; Thomsen
\& Willerslev, 2015).

The progress made in the eDNA field during these last ten years has been
spectacular (Taberlet, Bonin, Zinger, \& Coissac, 2018). Although the
basic concepts and workflow of DNA barcoding and metabarcoding have not
changed, the technological advances in high-throughput sequencing have
greatly facilitated the access to eDNA data. It has become possible to
monitor biodiversity with unprecedented precision and depth. Massive
environmental genomic datasets have been rapidly generated at relatively
low cost. The analysis of these datasets using machine learning and
other taxonomy-free approaches opened wide the doors for using new
groups of bioindicators to infer ecological status (Cordier et al.,
2018; Cordier, Lanzén, Apothéloz-Perret-Gentil, Stoeck, \& Pawlowski,
2019; Pawlowski et al., 2018). At the same time, constant efforts to
fill gaps in barcoding reference databases considerably increased the
effectiveness of taxonomic identification of eDNA data (Weigand et al.,
2019).

Astonishingly, these rapid advances in eDNA-based technologies are
rather timidly implemented in routine biomonitoring (Hering et al.,
2018; Shackleton et al., 2021). Although the concept of Biomonitoring
2.0 is widely endorsed, its acceptance in practice is hampered for
various reasons. There is no consensus whether eDNA-based biomonitoring
should only apply to conventional bioindicators (Renovate) or should
also include new bioindicators (Rebuild) or new taxonomy-free approaches
(Revolutionize) (see Fig. 1). Moreover, three main steps on the roadmap
from eDNA to biomonitoring are not developed equally. The main attention
is given to the development and optimization of eDNA data generation and
analysis. The standardization of eDNA methods and their translation into
legislatory framework remain at a very early stage. One of the main
issues impeding the application of eDNA-based tools concerns the lack of
congruence between the results of traditional and molecular analyses
(Aylagas et al., 2020). It is expected that the new method is ``safe to
use'' only if it provides the same or almost same results as the
conventional one. However, obtaining such perfect congruence is often
impossible because the character of data is very different (e.g.,
abundance of individuals vs abundance of eDNA reads). Moreover, the eDNA
``ecology'' can hardly be translated directly into species ecology.
There are also numerous biological and technical biases that can affect
the generation and processing of eDNA data, impacting their
interpretation.

This special issue addresses some of these challenges by presenting the
latest advances in eDNA field and discussing their strengths and
limitations when applied to routine biomonitoring. The issue comprises
29 papers grouped into four sections and covering different aspects of
eDNA applications. It is accompanied by an opinion paper, which
clarifies the eDNA terminology in relation to its use in biomonitoring
(Pawlowski, Apothéloz-Perret-Gentil, \& Altermatt, 2020). The first
section comprises a series of studies using new analytical tools (e.g.
machine learning), new types of bioindicators and genomic data (e.g.
shotgun sequencing) for the assessment of ecological status. It is
followed by a section dedicated to fish eDNA, whose application in
biomonitoring is the most advanced. The third section comprises papers
dealing with various methodological aspects and the comparison between
conventional and molecular methods. The final section presents few
examples of eDNA applications for biodiversity surveys and population
genetics.

Novel approaches to monitor ecosystems

The development of environmental genomics enables monitoring of
microbial and meiofaunal communities that were previously inaccessible
when using conventional methods. However, our knowledge of the ecology
of these communities is very limited and therefore new analytic
approaches are necessary to integrate them into routine bioassessment.
This section begins with a review of implementation strategies for the
application of environmental genomics in ecological diagnostics (Cordier
et al., 2021). The authors introduce four broad categories of possible
strategies, including (1) DNA-based taxonomic identification of known
bioindicators, (2) taxonomy-free discovery of new bioindicators, (3)
structural community metrics, and (4) functional community metrics. Each
of these strategies is adapted to a particular type of data
(metabarcoding, metagenomics, metatranscriptomics) and rely on different
computational analyses in order to provide an assessment of the
ecological status.

Among the different analytical tools, machine learning seems to be the
most promising way to predict the ecological status (Cordier et al.,
2018, 2019). In this issue, its performance is tested in the case of the
benthic diatoms index widely used in the assessment of ecological
quality of rivers and streams (Apothéloz-Perret-Gentil et al., 2021).
This study shows that supervised machine learning performs better than
the taxonomic assignment, but its predictions are similar to those
obtained using a taxonomy-free molecular assignment approach. Moreover,
the efficiency of a taxonomic assignment method strongly depends on the
completeness of the reference database, highlighting the need to fill in
the existing gaps, particularly in the case of bioindicator taxa.

The ability of \emph{de novo} prokaryotic bioindicators to predict
multiple anthropogenic impacts on estuarine and coastal benthic
communities is demonstrated by Lanzén, Mendibil, Borja, \& Alonso-Sáez
(2021). The authors compare their results to the traditional
macrofauna-based indices and discuss various advantages of using
microbial bioindicators as they are more sensitive to different abiotic
pressures. Similar conclusions were reached in the case of environmental
impact assessment of marine aquaculture (Frühe et al., 2021) and the oil
and gas industry (Mauffrey et al., 2021). Both studies demonstrate the
effectiveness of machine learning and\emph{de novo} microbial
bioindicators and promote their use for benthic monitoring in marine
environments.

The last two papers in this series explore new directions for the
further development of ecogenomic diagnostics. Broman et al. (2021) use
environmental RNA (eRNA) shotgun sequencing to analyse the impact of
organic enrichment on benthic micro-eukaryotic communities. Compared to
eDNA metabarcoding that is used in the majority of studies, eRNA shotgun
data has the advantage to overcome the potential biases of PCR
amplification and to better capture the organismic response to
environmental pressures by targeting predominantly active cells. Ibrahim
et al. (2021) use historical eDNA metabarcoding data to analyze the
impact of eutrophication on lake phytoplankton in the
20\textsuperscript{th}century. This study demonstrates the potential of
paleo-metabarcoding to characterize past biodiversity and establish
reference conditions for future monitoring.

Refining fish eDNA surveys

The second series of papers concerns the use of eDNA to monitor fish
diversity. We focus on fish because they are among the most important
groups of bioindicators and also because their study from an eDNA
perspective is the most advanced (Pont et al., 2021). The barcoding
reference database of common fish species in some regions is close to
completeness (Knebelsberger, Dunz, Neumann, \& Geiger, 2015),
fish-specific markers are well defined (M. Miya et al., 2015; Valentini
et al., 2016; Zhang, Zhao, \& Yao, 2020) and protocols for fish eDNA
sampling and processing are well established (Masaki Miya, Gotoh, \&
Sado, 2020; Valentini et al., 2016). Currently, considerable efforts are
directed to solve the most challenging issue, which is related to
quantitative fish eDNA data and its application for inferring fish
indices in routine biomonitoring.

Two papers address this issue by proposing different approaches to
estimate fish abundance from eDNA data. Fukaya et al. (2021) use
numerical hydrodynamic models to simulate the spatial and temporal
distribution of fish eDNA in aquatic environments. By integrating the
models to the measures of eDNA concentration, the authors obtained
estimates of fish population abundance comparable to those obtained by
the quantitative echo sounder method. Yates et al. (2021) improve the
correlation between eDNA concentration and fish abundance by integrating
allometric scaling coefficients. Such coefficients can help adjust the
values of eDNA production taking in consideration density, biomass and
metabolic rates characteristic to a given taxon.

A better understanding of the ``ecology'' of fish eDNA, and particularly
how its temporal and spatial distribution is shaped by abiotic and
biotic factors, is the subject of the following papers. Littlefair et
al. (2021) tested how seasonal variations in thermal stratification
influence the distribution of fish eDNA in lakes. The authors show that
eDNA distribution follows lake stratification and the thermal niche of
the species, which in turn may affect its detection in certain seasons.
The distribution of fish and amphibian eDNA in a lentic system was
investigated experimentally by Brys et al. (2021). This study indicates
high eDNA decay rates and limited dispersal, reinforcing the accuracy of
eDNA-based monitoring for retrieving the spatiotemporal occupancy
patterns. The advantages of using eDNA for survey of fish populations
were also demonstrated by other papers in this section. McColl-Gausden
et al. (2021) showed that eDNA metabarcoding is generally more sensitive
than electrofishing for conducting fish surveys in freshwater streams,
while Aglieri et al. (2021) demonstrate strong complementarity of
eDNA-based analysis with visual and capture-based methods in the survey
of coastal fish communities.

Methodology and comparison with conventional methods

General acceptance of molecular methods in biomonitoring requires their
benchmarking against conventional morphotaxonomy-based approaches. This
is commonly achieved by processing the same samples in parallel using
different methods and by assessing how the molecular data fit to the
results of traditional approaches, considered as a ground truth. The
papers of this section compare the results of eDNA metabarcoding vs bulk
DNA metabarcoding vs different morphology-based approaches. They also
present and discuss the biases of molecular methods and propose
solutions to improve the outcomes of molecular data generation and
processing.

The section begins with the three comparative studies of marine
biomonitoring. Suter et al. (2021) evaluate the performance of water
eDNA and bulk DNA metabarcoding in assessing the biodiversity of
zooplankton in open ocean, currently monitored by using continuous
plankton recorders. The study shows that both methods recover more
species than morphological analyses, however, their efficiency depends
on the sampling method and selected marker. They conclude that eDNA
metabarcoding is very promising, but it still requires some refinement
and standardization before it can be routinely used for zooplankton
biomonitoring. Similar conclusions are drawn from the comparison of
sediment DNA metabarcoding and macrofauna surveys applied to monitor
benthic impacts of salmon farms (He et al., 2021). Although the authors
found a certain coherence in relative abundance of common macrofauna
bioindicators inferred from morphological and eDNA data, they observed
that the correlation with organic enrichment was much stronger for
meiofauna, which is not usually included in biomonitoring studies.
Significant differences were also found between water eDNA samples and
bulk DNA extracts from adjacent benthic communities (Antich et al.,
2021). The authors concluded that water eDNA is a poor proxy for the
analysis of benthic communities, although they do not exclude that the
use of taxon-specific markers could improve the congruence between eDNA
and bulk DNA metabarcoding data.

The importance of marker selection has also been emphasized in the case
of freshwater macrobenthos metabarcoding. The performance of different
markers, with focus on key insect orders (Ephemeroptera, Plecoptera and
Trichoptera) was tested by Ficetola et al. (2021). The authors
demonstrate the complexity of the marker selection process and advocate
for the use of multiple markers to cover the widest range of taxa.
Combining data from different markers was shown to considerably improve
the match between macrobenthic indices inferred from bulk DNA and
morphotaxonomic surveys (Meyer et al., 2021). A multimarker approach was
also recommended for the assessment of macroinvertebrate communities
from the bulk preservative (Martins et al., 2021). Despite the
importance of using multiple markers, the authors also demonstrate that
the presence of heavily sclerotized exoskeleton can act as a limiting
factor for the detection of some taxa.

The comparison of bulk DNA vs water eDNA metabarcoding has been reported
by two papers. Gleason et al. (2021) show that bulk DNA metabarcoding
more accurately represents the local stream macroinvertebrate community,
with water eDNA data being overwhelmed by non-metazoan sequences. The
same difference was observed when comparing bulk DNA to water eDNA and
morphological inventories of pond macroinvertebrates (Harper et al.,
2021). However, the authors consider both approaches as complementary
and suggest that they should be combined for comprehensive assessment of
the invertebrate community. The importance of bulk DNA metabarcoding as
a tool for the assessment of marine ecosystems is also highlighted by
van de Loos and Nijland (2021). The authors review various technical
biases affecting bulk DNA metabarcoding workflow and discuss possible
improvements that could help overcoming these biases in the future.

The analysis of water samples from five sites in the Brazilian Atlantic
forest and one adjacent site in Cerrado grasslands allowed Lopes et al.
(2021) to demonstrate that eDNA metabarcoding significantly improves
traditional monitoring methods, confirming the presence of frog species
undetected by traditional methods. For a few years, invertebrate-derived
DNA (iDNA) from leech blood-meal have been used to track mammalian
species (Schnell et al., 2012). Here, Drinkwater et al. (2021) apply
this approach to assess differences in mammalian diversity across a
gradient of forest degradation in Borneo. For monitoring elusive
mammals, the iDNA method complements the more traditional and widely
used camera trapping.

The last two papers in this section provide examples of metabarcoding
optimizations aiming at improving its effectiveness in biomonitoring
surveys. Guerrieri et al. (2021) show how soil preservation methods can
affect estimates of taxonomic richness and community composition. The
authors propose guidelines for optimizing soil preservation conditions
in agreement with the objectives and practical constraints of the
research project. On the other hand, Mächler et al. (2021) address the
optimization of data analysis, by investigating how stringency filtering
can affect eDNA diversity estimates. The authors conclude that the use
of Hill numbers can help in comparisons of eDNA datasets that strongly
differ in diversity.

Other perspectives for eDNA-based biomonitoring

The last three articles in this special issue present ground-breaking
approaches to monitoring biodiversity. Martel et al. (2021) clearly show
that eDNA surveys paired with occupancy modelling can uncover
metapopulation dynamics and their drivers. Such type of information is
important for monitoring endangered species distributed in
metapopulations and is quite difficult to obtain via traditional
inventories. Shum and Palumbi (2021) reanalyzed a published marine
metabarcoding dataset concerning cobble communities found within kelp
forest ecosystems. They focussed on diversity data at the intraspecific
level to infer population structure and demographic trends. This type of
approach greatly increases the scope and value of metabarcoding studies,
also opening the way towards metaphylogeography (Turon, Antich, Palacín,
Praebel, \& Wangensteen, 2020). Finally, Sigsgaard et al. (2021)
successfully tracked insects from cow dungs from different environments,
and showed that eDNA metabarcoding represents an efficient method for
assessing insect diversity, with potential for biomonitoring in relation
with the relatively easy standardization of such an approach.

Conclusion

As shown by the collection of papers published in this issue, potential
applications of eDNA in biomonitoring are highly diverse. Their scope
ranges from tracking endangered species to surveying biodiversity or
assessing environmental impact. Some papers focus on integrating eDNA
into existing bioindication systems, whereas others use eDNA to expand
the range of bioindicators and include inconspicuous, commonly
overlooked microbial and meiofaunal taxa. All these papers attest to
major efforts that have been done to improve eDNA methodology at every
step of the workflow from sampling to data analysis. They also
contribute to better understand the biological and technical factors
impacting the eDNA analyses. Yet, despite this huge new knowledge and
numerous practical advantages, the implementation of eDNA in routine
biomonitoring still has not taken off.

It is now high time to move on and to transform the eDNA field into a
truly applied science. The biodiversity crisis and global environmental
changes call for an urgent modernization of the tools to monitor
biodiversity and assess the ecological status of our environment. As
shown by the papers published here, the eDNA methodology achieved top
levels of technical and scientific excellence in many areas. Certainly,
there are some biases and limitations inherent to eDNA specificity, but
there is no reason to consider that the technology is less ``safe to
use'' than the conventional morpho-taxonomic approaches. There are also
actions to be taken to ensure the quality and to build confidence in
eDNA analyses through standardization of technical protocols and
intercalibration tests. However, in view of the substantial efforts that
have been made by the scientific community and illustrated by the
content of this special issue, it is reasonable to expect that the
implementation of eDNA-based tools in biomonitoring will not be long in
coming\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} Framework for an eDNA-based biomonitoring. The boxes
on the left represent the main current research avenues, that are mostly
focused on particular components of biological communities. The right
side represents the milestones to meet for the implementation of eDNA
for routine biomonitoring.

\textbf{Data Accessibility Statement}

Non applicable



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