# Environmental Matrices Need Consideration: Insights From Water and Biofilm Environmental DNA for Multi-Taxonomic Biomonitoring

Paula Gauvin, Isabelle Domaizon, Agnes Bouchez, Frédéric Rimet

<figure><img src="image_1.png" /></figure>

## To cite this version:

Paula Gauvin, Isabelle Domaizon, Agnes Bouchez, Frédéric Rimet. Environmental Matrices Need Consideration: Insights From Water and Biofilm Environmental DNA for Multi-Taxonomic Biomonitoring. Environmental DNA, 2025, 7 (2), pp.e70079. 10.1002/edn3.70079. hal-05150275

HAL Id: hal-05150275

https://hal.inrae.fr/hal-05150275v1

Submitted on 8 Jul 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
ORIGINAL ARTICLE

OPEN ACCESS

# Environmental Matrices Need Consideration: Insights From Water and Biofilm Environmental DNA for Multi-Taxonomic Biomonitoring

Paula Gauvin¹,² | Isabelle Domaizon¹,² | Agnes Bouchez¹,² | Frédéric Rimet¹,²

¹Université Savoie Mont-Blanc, INRAE, UMR CARRTEL, Thonon-les-Bains, France | ²INRAE, Pole R&D ECLA, Thonon-les-Bains, France

**Correspondence:** Paula Gauvin (paula.gauvin@inrae.fr)

**Received:** 8 March 2024 | **Revised:** 17 February 2025 | **Accepted:** 25 February 2025

**Funding:** This work was supported by Carnot Eau & Environnement Institute INRAE-OFB ECLA (ECosystèmes LAcustres).

**Keywords:** algae | biofilm | eDNA | fish | lake biomonitoring | macroinvertebrates

## ABSTRACT

Environmental DNA (eDNA) is revolutionizing biodiversity monitoring, offering a unique approach to assess multi-taxonomic diversity with various applications related to evaluation, protection, and restoration of aquatic ecosystems. However, there is still a lack of sufficient studies to assess the complementarity of various environmental matrices and their contribution to enhancing biodiversity detection. This study evaluates the impact of eDNA sampling in different matrices to measure biodiversity of different taxonomic groups. We set up a year-long eDNA sampling of water and biofilm matrices in a large lake littoral zone (Lake Geneva), focusing on microalgae, benthic macroinvertebrates, and fish. We first assessed primer specificity, which was high for microalgae (23S) and fish (12S) but was lower for macroinvertebrates (COI). We then evaluated the complementarity of eDNA signals in water and biofilms. For microalgae, communities from biofilm and water were highly different: water eDNA almost exclusively detected planktonic taxa while biofilm eDNA detected predominantly benthic taxa. For macroinvertebrates, communities in water and biofilms were also different, and biofilm eDNA could detect mostly Chironomidae. Finally, for fish, eDNA of both matrices enabled us to detect similar communities even if a few rare species were detected only in water. In the framework of the assessment of ecosystem quality or restoration actions success, we recommend diversifying matrices to collect eDNA to capture a complete picture of microalgae and macroinvertebrate communities. For fish, it is possible to sample water or biofilms, keeping in mind that water has become a standard practice for fish eDNA sampling.

## 1 | Introduction

Freshwater ecosystems are among the most endangered environments (Dudgeon et al. 2006), primarily due to climate change (Bates et al. 2008) and critical anthropogenic impacts such as eutrophication, toxic pollutants, overfishing, and exploitation (Meybeck 2003; Vörösmarty et al. 2010). Ecosystem biomonitoring is a crucial tool for management (Friberg et al. 2011), as it enables measuring human impact on the biota. High-quality biomonitoring data are fundamental for comprehending and addressing ecosystem degradation (Birk et al. 2012; Bohan

et al. 2017) as well as evaluating restoration programs effectiveness (Winking et al. 2014).

Freshwater biomonitoring relies on various ecological indicators, which assess the impact of different pressures, such as eutrophication or habitat degradation (Argillier et al. 2013; Hering et al. 2006). Routine biomonitoring programs rely on standardized methods and have been providing invaluable information for decades through the morphological identification of taxa (Serrana et al. 2019). However, morphological identifications present several limitations, including the

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2025 The Author(s). Environmental DNA published by John Wiley & Sons Ltd.</footnote>
need for extensive taxonomic expertise, high costs, and labor-
intensive efforts (Aylagas et al. 2014). These issues restrict
the ability of morphological methods to provide information
at high spatial and temporal coverage (Jones 2008). In partic-
ular for microalgae and freshwater invertebrates, many taxa
are misidentified or mistaken because of poor morphological
criteria (Haase et al. 2006). Regarding conventional fish mon-
itoring methods (i.e., capture methods using gillnets or elec-
trofishing), there are limitations in detecting rare and elusive
taxa (Mackenzie and Royle 2005; Smith et al. 2015) and these
methods are lethal (or damaging) making them challenging to
employ in conservation contexts (Wheeler et al. 2004). These
limitations often lead to an underestimation of biodiversity
(Jackson et al. 2014) and hamper our capacity to efficiently
monitor ecological patterns (Feckler et al. 2012; Gleason and
Rooney 2017).

The above-mentioned limitations contributed to the growing
popularity of molecular identification techniques utilizing
high-throughput sequencing (HTS), such as environmental
DNA (eDNA) metabarcoding (Taberlet et al. 2012). eDNA
metabarcoding enables the rapid and cost-effective taxon-
omy identification of community compositions (Hyvärinen
et al. 2021; Pawlowski et al. 2018), and it is well suited for
routine monitoring (Altermatt et al. 2020; Hering et al. 2018;
Leese et al. 2016) over large spatial and temporal scales
(Schenekar 2023). Moreover, it enables the detection of cryp-
tic species often overlooked with morphological identifica-
tion (e.g., Elbrecht and Leese 2015; Jackson et al. 2014; Rimet
et al. 2018). Consequently, this approach was successfully
used for monitoring a large range of freshwater and marine
habitats (Pawlowski et al. 2018; Ruppert et al. 2019). eDNA
metabarcoding is based on the sampling of DNA derived from
environmental samples (water, sediments, biofilm, etc.). This
DNA comprises both intra-and extracellular DNA from living
or dead organisms (Taberlet et al. 2018). Depending on the
type of bioindicator being studied, the type of eDNA targeted
will differ; microalgae will mostly be detected through high
quality/quantity of intracellular DNA. In contrast, fish will
mostly be detected through DNA released in the environment
(i.e., extracellular DNA or DNA in cells as gamete, skin; whose
persistence, degradation and transport in the water are influ-
enced by various factors) (Harrison et al. 2019).

When sampling eDNA, different environmental matrices can
be chosen (e.g., water, biofilm, sediment, etc.) and the sam-
pling strategy can differ (e.g., volume of filtered water) which
impacts the results of biodiversity assessment (Liu et al. 2019).
There are still too few studies available to evaluate the comple-
mentarity of different environmental matrices and to what ex-
tent they improve biodiversity detection. Understanding how
the matrices complement each other in terms of biodiversity
inventories is critical for the selection of sampling approaches.
For fish monitoring, eDNA is typically collected from water
(e.g., Hänfling et al. 2016; Hervé et al. 2022). Although this
matrix is commonly used to detect a large diversity of organ-
isms in aquatic environments (mammals, fish, frogs, insects,
microbes, etc.), its disadvantage is that water moves with
currents (in lakes and rivers), and it moves with the eDNA
it carries, so it is difficult to know the origin of water-based
eDNA (Nevers et al. 2020). Another commonly used matrix,

sediment, has been used successfully to detect species in fresh-
water environments (Ruppert et al. 2019). However, this ma-
trix is considered more challenging (Deiner et al. 2017; Rourke
et al. 2022), with issues related to PCR inhibitions (Buxton
et al. 2017) or spatial heterogeneity of the signal (Harrison
et al. 2019). Another matrix to consider in aquatic environ-
ments is biofilm, which is a complex community of bacteria,
algae, fungi, and protozoa encased in a matrix of extracellu-
lar polymeric substances (EPS). Depending on light availabil-
ity, these biofilms in the benthic zone can include eukaryotic
algae (such as diatoms and green algae) and cyanobacteria.
Biofilms also support top consumers like ciliates, flagellates,
nematodes, and young instar insects (Battin et al. 2016) and
host a microalgal diversity considered excellent ecological in-
dicators (e.g., Sabater et al. 2007). Numerous studies demon-
strated that aquatic biofilms capture particulate matter from
the water column, resulting in higher particle concentra-
tions in biofilms than in the surrounding water (e.g., Mora
et al. 2016; Roche et al. 2017). Based on this, some authors
hypothesized that biofilms could also capture extracellular
eDNA from water and concentrate it (Shogren et al. 2018).
First studies showed good results in assessing fish and macro-
invertebrates biodiversity from the total DNA extracted from
lake biofilms (Rivera et al. 2021, 2022, 2023). Biofilms offer
several advantages: they are ubiquitous in aquatic environ-
ments, remain stationary due to their attachment, accumulate
particles, and harbor a diverse community of organisms, mak-
ing them valuable for biomonitoring applications. In addition,
many near shore aquatic environments do not have sediments
available for sampling, such as rocky intertidal regions, lakes
with rocky substrates, or anthropized environments (harbor
basins, dykes, or artificial drains). This justifies the use of bio-
film to consider the biodiversity of the benthic compartment
when sampling broad biodiversity with eDNA. Indeed, even in
areas near the benthic layer, water eDNA has been shown to
poorly represent the nearby benthic taxa in freshwater ecosystems
(Duarte et al. 2021 and references therein).

Finally, numerous studies have demonstrated that a compre-
hensive understanding of the functioning of aquatic ecosystems,
as well as an accurate assessment of their ecological
status, requires the evaluation of the diversity of multiple or-
ganism groups. In freshwater environments, this typically in-
cludes microalgae, benthic macroinvertebrates, and fish (Birk
et al. 2012; Hering et al. 2006). However, sampling all these bi-
ological compartments using conventional analysis methods is
limited due to logistical, time, and cost constraints associated
with collecting representative samples for each taxonomic
group (Chon et al. 2013). With eDNA sampling, it is possi-
ble to sample a limited number of environmental matrices to
analyze these different organisms. However, only a limited
number of studies have investigated this possibility (Robinson
et al. 2022), leaving considerable scope for further exploration.
To address these objectives, we conducted a one-year moni-
toring study in Lake Geneva (France, Switzerland) to assess
the complementarity of eDNA signals found in water and in
biofilm for three bioindicators: microalgae, benthic macroin-
vertebrates, and fish. Sediments were not sampled since they
were not present in all sampling sites. We formulated the fol-
lowing hypotheses: (i) eDNA from water and biofilms detects
matrix-specific taxa for taxonomic groups that live in these
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** | Locations of biofilm and water sampling points (Map data IGN, RGD, Ersi France, France).</figcaption></figure>

matrices, and (ii) biofilms yield high quantities and quality of intracellular eDNA from benthic microalgae and benthic macroinvertebrates, while water yields intracellular eDNA from phytoplankton. Conversely, (iii) eDNA from water and biofilms detects more similar taxa for taxonomic groups releasing eDNA in the environment (such as fish). To this end, we first assess the specificity of the primers used for metabarcoding of each biological group: the 23S V5 marker was amplified with ECLA23S_F1/ECLA23S_R1 primers (Canino et al. 2023) for microalgae, the COI marker was targeted with fwhF2/fwhR2N primers (Vamos et al. 2017) for benthic macroinvertebrates, and the 12S marker was amplified with MiFish-U-F/MiFish-U-R for fish (Miya et al. 2015). Then, we compared alpha and beta diversity as well as ecological metrics (life traits) among matrices to evaluate their capacity to detect each bioindicator group. These findings provide insights related to sampling strategies, specifically the matrix/matrices to collect eDNA from for a particular bioindicator group.

# 2 | Materials and Methods

## 2.1 | Study Area and Field Sampling

The study was conducted in 2020–2021 on the shoreline of Anthysur-Léman (France), a small city located on the Lake Geneva shore. This portion of shoreline is constituted of rocks, due to the swell phenomena and the configuration of the shoreline which created intense and consecutive erosion zones over several hundred meters. Sampling was carried out monthly from October 2020 to September 2021 (08/10/2020, 10/11/2020, 08/12/2020, 19/01/2021, 04/02/2021, 02/03/2021, 26/04/2021, 19/05/2021, 03/06/2021, 05/07/2021, 27/07/2021, and 22/09/2021). Six sampling points were collected over 300 m on the shoreline (1 sampling point every 50 m). These six points (P1–P6) are considered to be field replicates (Figure 1).

## 2.2 | Biofilm Sampling

Biofilm sampling was based on the European diatom sampling protocol (EN 13946 2014), adapted to molecular biology analyses (CEN/TR 17245 2018) and precisely described in Chonova et al. (2019). A total of 73 biofilm samples were collected (6 sampling points × 12 dates and one field control). For each site, five biofilm-covered stones were randomly collected (water depth of 40–50 cm) and placed in a plastic basin. The excess of lake water in the plastic basin was discarded. A unique plastic basin was used per sampling point. They were cleaned according to the protocol of decontamination with hydrogen peroxide of eDNA dedicated material (dx.doi.org/10.17504/protocols.io.kxygx36ozg8j/v1) before the sampling process. Using a brand new toothbrush for each sampling point, the biofilm of the five stones was scrubbed directly in the plastic basin with Milli-Q water. 15 mL of this mixture (biofilm and Milli-Q water) was transferred to a 50 mL Falcon tube and preserved by adding 35 mL of ethanol (70%). The stones were thrown back in the water. For the field control sample collected on the 22/09/2021, the same protocol described above was applied with no stones to test possible contamination from the sampling gear. Back in the lab, these Falcon tubes were kept at 4°C at the end of each sampling month. After the one-year sampling campaign, 2 mL of ethanol-preserved biofilm from each 50 mL Falcon tube was transferred to a new tube. Then, these 2 mL tubes were centrifuged at 4°C for 30 min at 18000 g, and the supernatant was removed. The remaining pellet was frozen at –20°C until the DNA extraction step.

## 2.3 | Water Sampling and Filtering

Water sampling was based on the protocol established specifically for Sterivex filter filtration for fish eDNA developed by Vautier et al. (2021). A total of 73 water samples (6 sampling
points × 12 dates and one field control) were collected. Two liters of water were collected at approximately 10 cm under the surface at each site using a 2 L plastic bottle and then stored in a cooler waiting for the return to the laboratory. Each bottle used was decontaminated with hydrogen peroxide (see the protocol above) before the sampling process. In order to check for potential contamination from the equipment, the field control sample collected on 22/09/2021 consisted of a 2 L bottle of Milli-Q water transferred into a new 2 L bottle. Within 2 h after the water samples were collected, the two liters were filtered through a Sterivex filter (Sterivex GP pressure filter units, 0.45 µm; Millipore) according to the protocol of Vautier et al. (2021). Once filtration was complete, 2 mL of lysis buffer was added to the Sterivex filter with a micropipette; the Sterivex filter set was then stored at -20°C until the DNA extraction step.

## 2.4 | eDNA Extractions

For biofilm, DNA was extracted with the NucleoSpin Soil kit (MACHEHEREY-NAGEL, GmbH, Düren, Germany), following the adjustments made by Vautier et al. (2020), to obtain a final volume of 30 µL. After DNA extraction, the quality and quantity of extracted DNA were checked with a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific). For Sterivex filters, DNA was extracted with the NucleoSpin Soil kit (MACHEHEREY-NAGEL, GmbH, Düren, Germany), following the adjustments made by Vautier et al. (2020), as used in former published studies (Rivera et al. 2021). The quality and quantity of extracted DNA from Sterivex filters were verified using a Nanodrop spectrophotometer (Thermo Fisher Scientific).

## 2.5 | PCR Amplification and Sequencing

In this study, we targeted three different bioindicators: microalgae, benthic macroinvertebrates, and fish. We considered microalgae as all photosynthetic prokaryotic (cyanobacteria) and eukaryotic microorganisms (e.g., Chlorophyta, Ochrophyta, and Euglenophyta). For benthic macroinvertebrates, we aimed at targeting phyla that are classically considered freshwater benthic macroinvertebrates in France according to Tachet et al. (2000). Finally, for fish, we considered all taxa belonging to Teleostei.

### 2.5.1 | Microalgae (23S V5)

PCR amplification and amplicon library preparation followed a 2-step PCR process based on the protocol of PGTB (Plateforme Génome Transcriptome de Bordeaux, France) for the preparation of targeted metagenomic libraries on MiSeq Illumina and adapted for the primer pair used in this study (Canino et al. 2023) using a dual-index metagenomic library preparation workflow. The barcode (358 bp), belonging to the 23S ribosomal RNA (rRNA) gene, was amplified by a first PCR (PCR1) with the primer pair ECLA23S_F1/ECLA23S_R1. The target-specific primer pair (ECLA23S_F1: 5'-ACAGWAAGACCCTATGAAGCTT-3'; ECLA23S_R1: 5'-CCTGTTATCCCTAGAGTAACTT-3') was tagged with universal sequences that allowed, in a second PCR, the hybridization of Illumina adapters and sequencing indexes (Forward adapter: 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGAC AG-3'; Reverse adapter: 5'-GTCTCGTGGGCTCGGAGATGT GTATAAGAGACAG-3'). The PCR1 reaction mixture (initial concentrations stated), with a final volume of 25 µL, contained 12.5 µL of 2× KAPA HiFi HotStart ready mix, 5 µL of forward primer [1 µM], 5 µL of reverse primer [1 µM], and 2.5 µL of DNA [5 ng-µL]. Thermocycling conditions consisted of an initial denaturation at 95°C (3 min), then 30 cycles of denaturation at 95°C (30 s), primer annealing at 58°C (30 s) and extension at 72°C (30 s), and final extension at 72°C (5 min). Two negative PCR controls (adding milliQ instead of DNA template) were performed along with sample PCRs. Mock communities to test the specificity of these primers have already been used in Canino et al. (2023). Amplicons were checked via electrophoresis (1.5% agarose gel), and all controls (field controls and PCR controls) were negative. After PCR1, amplicons from all samples and controls were sent to the PGBT sequencing platform for further processing. First, the amplicons were purified with SPRI magnetic beads (1×), then a second PCR (PCR2) was performed in order to attach the sequencing indexes and adapters using Illumina's Nextera XT Dual Indexes (5 µM). The PCR2 reaction mixtures consisted of 4 µL of purified PCR1 products, 4 µL of Illumina Dual Index (5 µM), 4.5 µL of H₂O Milli-Q, and 12.5 µL of 2× KAPA HiFi HotStart Ready Mix (Roche). Thermocycling conditions consisted of an initial denaturation at 95°C (3 min), then 12 cycles of denaturation at 95°C (30 s), then primer annealing at 60°C (30 s) and extension at 72°C (30 s), and final extension at 72°C (5 min). These PCR2 amplicons were then purified with SPRI magnetic beads (1×) and quantified by a fluorometer using the Quant-it dsDNA HS assay kit (Life Technologies, UK). The amplicons were pooled equimolar (diluting them to the same final concentration), and the amplicon library was controlled on a Tapestation 4200 (Agilent Technologies) and quantified by qPCR (LightCycler 480) with the QIAseq_LibQuantAssay kit. Finally, 7 pM of the final library was loaded with 15% of PhiX on MiSeq (Illumina) with the v2 kit (500 cycles) for the sequencing process.

### 2.5.2 | Benthic Macroinvertebrates (COI)

PCR amplification and amplicon library preparation followed a 2-step PCR process based on the protocol of GeT-PlaGe (Génome & Transcriptome Plateforme Génomique, INRAE, Genotoul, France) for the preparation of targeted metagenomic libraries on NovaSeq and adapted for the primer pair used in this study (Miya et al. 2015; Vamos et al. 2017) using a single-index metagenomic library preparation workflow. For macroinvertebrate metabarcoding, the 205-bp barcode of the mitochondrial cytochrome c oxidase subunit I gene (COI) was amplified by PCR with the primer pair fwhF2-fwhR2N (fwhF2: 5'-GGDACWGGWTGAACWGTWTAYCCHCC-3'; fwhR2N: 5'-GTRATWGCHCCDGCTARWACWGG-3') (Vamos et al. 2017). Sequencing adapters (Forward Illumina adapter: 5'-CTTTCCCTACACGACGCTCTTCCGATCT-3'; Reverse Illumina adapter: 5'-GGAGTTCAGACGTGTGCTCTTCCG ATCT-3') were added to the target-specific primers. In PCR1, each DNA extract was amplified in PCR duplicates in order
to ensure detection for each sample, as these primers demonstrated moderate specificity for benthic macroinvertebrates, with 60% of the amplified DNA not belonging to benthic macroinvertebrates (see Leese et al. 2021). The PCR1 mix (initial concentrations stated) consisted of 15 µL of Multiplex PCR Master Mix QIAGEN Multiplex PCR Plus Kit (reference: 206152) [2×], 7.8 µL of H₂O, 0.6 µL of fwhF2 forward primer [0.2 µM], and 0.6 µL of fwhF2 reverse primer [0.20 µM], for a final volume of 30 µL including 6 µL of DNA. Thermocycling conditions were started by denaturation of DNA at 95°C for 5 min for both templates. Then, a PCR touchdown procedure was performed, with specificities for each matrix. A total of 35 cycles (for biofilm samples) and 38 cycles (for water samples) were performed under the following conditions: initial denaturation at 95°C (5 min), then 7 cycles of denaturation at 95°C (30 s), annealing at 65°C losing 1°C per cycle for 7 cycles (90 s), and extension at 72°C (30 s), then 28 cycles (for biofilm samples) and 31 cycles (for water samples) of denaturation at 95°C (30 s), annealing at 58°C (90 s), followed by extension at 72°C (30 s), and final extension at 68°C (10 min). In order to detect contamination, two negative PCRs (adding milliQ instead of DNA template) were performed along with samples. In addition, a mock community of macroinvertebrates was used in the form of three positive controls in order to estimate the quality of the amplification and sequencing steps. The mock community consisted of a DNA equimolar mixture of five macroinvertebrate individuals from five families of Arthropoda diluted to 25 ng/µL and then pooled together for a final volume of 100 µL (20 µL of each sample). DNA from each specimen was extracted and purified with the NucleoSpin Tissue Mini Kit (REF. 740952.50) for DNA from cells and tissue, according to the recommended instructions (Table S1). PCR duplicates of each sample were pooled together after amplification. The amplification of the COI gene barcode was verified by electrophoresis, using a 1.5% agarose gel. After PCR1, at the sequencing platform, PCR product quality and integrity were determined using capillary electrophoresis on a fragment analyzer using the HS NGS Fragment Kit (Agilent, DNF 474-0500). PCR product purification was carried out using Clean NGS DNA clean-up (Proteigene, CNGS-0500) with a bead/sample volume ratio of 1.25. Quantification was determined using the Nanodrop system ND8000. A second PCR (PCR2) enabled the addition of a homemade 6 bp index for sample multiplexing, which was added to the reverse primer using 12 cycles by the PCR2-F and PCR2-R primers (PCR2-F: 5'-AAT GATACGGCGACCACCGAGATCTACACTCTTTCCCTACAC GAC-3'; PCR2-R: 5'-CAAGCAGAAGACGGCATACGAGAT-in dex-GTGACTGGAGTTCAGACGTGT-3'). Thermocycling conditions of the PCR2 were as follows: initial denaturation at 94°C (1 min), then 12 cycles of denaturation at 94°C (60 s), annealing at 65°C (60 s) and extension at 72°C (60 s), and final extension at 72°C (10 min). PCR products were then purified using Clean NGS DNA clean-up with a bead/sample volume ratio of 1.25. Indexed amplicons were quantified on a Nanodrop system ND8000 spectrophotometer, and quality was determined using capillary electrophoresis on the fragment analyzer using the HS NGS Fragment Kit. Barcoded amplicons were mixed in equal quantities, and the pool was quantified using KAPA Library Quantification Kit (Roche, KK4824). The library (1 nM loaded) was sequenced on an Illumina NovaSeq 6000 SP lane (Illumina Inc., San Diego, CA, USA) using a paired-end read length of 2×150 pb with the Illumina NovaSeq 6000 Reagent

Kits. The quality of the run was checked internally using 30% PhiX, and then, each paired-end sequence read pair was assigned to its sample with the help of the previously integrated index (bcl2fastq software).

### 2.5.3 | Fish (12S)

For fish metabarcoding, a 170-bp region of the mitochondrial rRNA 12S gene was amplified with the primer pair MiFish-U-F/MiFish-U-R (MiFish-U-F: 5'-GTCGG TAAAACTCGTGCCAGC-3'; MiFish-U-R: 5'-CATAGTGGG GTATCTAATCCCAGTTTG-3') (Miya et al. 2015). Illumina adapters (MiFish-U-F_tail-GetPlage: 5'-CTTTCCCTACACG ACGCTCTTCCGATC-3'; MiFish-U-R_tail-GetPlage: 5'-GGA GTTCAGACGTGTGCTCTTCCGATCT-3') were added to the target-specific primers. In PCR1, each DNA extract (water and biofilm) was amplified twice in PCR triplicates in order to follow the recommendations to perform PCR replicates when the probability of taxa detection is low (Coutant et al. 2021; Ficetola et al. 2015). For some fish samples (water and biofilm), additional PCRs were performed when the number of three positive PCR replicates was not reached. The PCR1 mix (initial concentrations stated) was composed of 0.2 µL of AmpliTaq Gold (Applied Biosystems) [5 U/µL], 15.74 µL of H₂O, 2.5 µL of buffer [10×], 0.07 µL of dNTP [100 mM], 1 µL of BSA [10 mg/mL], 1.5 µL of MgCl₂ [25 mM], and 1 µL of Mifish-U-F and Mifish-U-R [10 µM], for a final volume of 25 µL including 2 µL of DNA template. PCR1 thermocycling conditions were as follows: denaturation of DNA at 95°C (10 min) followed by 40 cycles of denaturation at 95°C (30 s), annealing at 64.5°C (30 s), extension at 72°C (1 min), and final extension at 72°C (10 min). Two negative PCRs (adding milliQ instead of DNA template) were performed along with the samples. A mock community of fish DNA was added to the sequencing run (three positive controls). The composition of the fish mock community, as well as the steps for the mock construction, is detailed in Rivera et al. (2022). DNA extracts (water and biofilm) amplified twice in PCR triplicates were pooled (Pool 1 and Pool 2 each containing 3 PCR replicates). The amplification of the 12S gene barcode was checked by electrophoresis using a 1.5% agarose gel. For the PCR2 protocol, the procedure was the same as described for the benthic macroinvertebrates (COI).

## 2.6 | Taxonomic Reference Databases

### 2.6.1 | Microalgae (23S)

For microalgae, we extracted the rRNA 23S (LSU) from the reference barcoding library Phytool (Canino et al. 2021), available in the Shiny Web Application Phytool V2 (https://caninuzzo.shinyapps.io/phytool_v2 beta/). Phytool is a curated reference barcoding library dedicated to microalgae.

### 2.6.2 | Benthic Macroinvertebrates (COI)

The macroinvertebrates reference library that was used can be found at doi: 10.3897/arphapreprints.e138085. This library is a
selection of COI sequences available in the BOLD database for macroinvertebrate species present in French aquatic ecosystems.

### 2.6.3 | Fish (12S)

The fish reference barcoding library used in this study is the one from Rivera et al. (2022), constructed from fish mitochondrial genomes downloaded from the public reference library MitoFish (Sato et al. 2018) (https://mitofish.aori.u-tokyo.ac.jp/). One fish species (*Salariopsis fluviatilis*, Asso) was added to this database by retrieving sequences from the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/). All the 23 species present in Lake Geneva (Périat and Vonlanthen 2014) are barcoded in this reference library.

## 2.7 | Bioinformatics of High-Throughput Sequencing Data

Demultiplexed Illumina paired-end FASTQ files were processed with the DADA2 v1.24.0 package (Callahan et al. 2016) on R (R Core Team 2022). First, primers were removed with cutadapt 4.1 (Python) (Martin 2011) for all samples. Then, we followed the pipeline available at https://doi.org/10.57745/TCA5HA, adapted from the rbcL diatoms pipeline (https://github.com/fkeck/DADA2_diatoms_pipe). Taxonomic assignment of ASVs was performed with the taxonomic reference databases adapted to each biological group with the assignTaxonomy function based on the RDP naive Bayesian classifier method of Wang et al. (2007). The minimum bootstrap confidence for taxonomic level assignment was set at 70% for microalgae with seven taxonomic levels, 70% for benthic macroinvertebrates with six taxonomic levels, and 90% for fish with seven taxonomic levels to ensure that the assigned sequences belong to the targeted biological group. The taxonomic assignments of ASVs obtained with DADA2 and the local reference barcoding libraries were compared and refined with the assignments using the NCBI database (for microalgae), the BOLD database (for benthic macroinvertebrates), and the MitoFish database (for fish) for three main purposes: (1) to identify the taxonomic category of sequences not assigned to the phylum or kingdom level of the target organisms and assess the proportion of non-target organisms (bacteria, fungi, etc.) within each group, (2) to verify the veracity of the assignment made with the local reference databases, and (3) to refine, if possible, the taxonomic rank of sequences assigned to higher levels than species levels.

## 2.8 | Molecular Data Cleaning and Data Transformations

After recovering the sequence tables from dada2 for the three biological groups, a threshold was determined to eliminate sequences potentially considered as contaminations. The thresholds were based on the maximum number of reads of an ASV in control samples (37 reads for the PCR2 control for microalgae, 530 reads for the field control for benthic macroinvertebrates, and 135 reads for the field control for fish). Next, ASVs containing fewer reads than this threshold were eliminated, and samples with few total reads were discarded (none for algae and

benthic macroinvertebrates and two sample for fish—1030 and 156 total reads per sample). After this step, only the ASVs assigned to our target biological groups were kept. Then, for fish, only the ASVs present in the two PCR replicate batches (PoisA and PoisB) were retained. Afterward, a permutational multivariate analysis of variance (PERMANOVA) (calculated with the adonis2 function from the vegan package (version 2.6-4), with 9999 permutations on Jaccard and Bray-Curtis distances matrices) was performed to test the difference in community composition between the two PCR replicates (PoisA and PoisB) for fish. As there was a significant difference between the two PCR replicates ($F(1, 238) = 6.01, p < 0.01$) in terms of relative abundances (with Bray Curtis distance) but not in terms of taxonomic composition (with Jaccard distance) ($F(1, 238) = 1.06, p = 0.359$), the sample relative abundances of those two datasets were summed in order to smooth PCR disparities. Datasets were rarefied to the lowest read number, which was 41,977 for microalgae, 532 for benthic macroinvertebrates, and 161,399 reads for fish using the function rarefy from the package vegan (version 2.6-4) (Oksanen et al. 2022). In total, 136 out of 141 samples were retained in the microalgae dataset. One sample was excluded due to insufficient reads, and four samples were removed to maintain a balanced representation of water and biofilm samples within each sampling date. For benthic macroinvertebrates, 130 out of 140 samples were retained. Seven samples were excluded to ensure a balanced representation of water and biofilm samples within each sampling date, and three samples were removed because they did not contain any macroinvertebrate ASVs. For fish, 120 out of 133 samples were retained. Eleven samples were removed to maintain a balanced number of water and biofilm samples within each sampling date, and two samples were excluded due to insufficient reads. The details of these steps for data cleaning for each group are available in Table S2.

## 2.9 | Statistical Analysis

The statistical analysis was conducted using RStudio version 2023.06.0+421 (R Core Team 2022). For all the statistical analyses below, we used rarefied relative abundance data (see Section 2.8). To analyze the shared and exclusive ASVs between matrices, we used the ps_venn function from the package MicEco (version 0.9.19). To assess the alpha diversity within the community, we calculated species richness and Shannon indices in each sample based on ASV assemblages. These metrics were calculated using the plot Richness function from the package phyloseq (version 1.44.0) (McMurdie and Holmes 2013), and differences between water and biofilm alpha diversity were evaluated with a one-way ANOVA with the function aov from the package stats (version 3.6.2) and visualized with the function geom_signif (package ggsignif version 0.6.4) (Ahlmann-Eltze and Patil 2021) within ggplot2 (Wickham 2009). To explore the beta diversity, we used the Bray-Curtis distance based on ASV assemblages. The Bray-Curtis distance was calculated with the ordinate function from the package phyloseq (version 1.44.0) and then, the distance matrix was used for an NMDS analysis to explore the temporal dissimilarity between water and biofilm communities. Confidence ellipses were drawn with stat_ellipse from the package ggplot2, with the type "t" parameter and a confidence level of 95%. To determine the influence of each predictor on communities (matrix, date, and their interaction),
<figure><img src="image_4.png" /><figcaption>**FIGURE 2** | Comparison of the relative proportion of DNA concentrations (ng/µL) introduced in the mock for each taxa (order and species) to the relative proportion of DNA reads obtained after the sequencing of three replicates (R1, R2 and R3) for macroinvertebrates (fwhF2/fwhR2N primers) (a) and fish (MiFish-U-F/MiFish-U-R primers) (b).</figcaption></figure>

we performed a PERMANOVA (9999 permutaions) using Bray-
Curtis distance matrices. This analysis was conducted with
the adonis2 function from the package vegan (version 2.6-4)
(Oksanen et al. 2022) and the homogeneity of variance was
checked with the betadisper function from the same package.
Finally, we conducted an indicator species analysis (Dufrene
and Legendre 1997) using the indval function from the package
labdsv (version 2.1-0). This analysis enabled the identification
of ASVs that served as distinctive taxa within specific groups of
samples, namely biofilm and water. The indicator species anal-
ysis is based on the relative abundance and relative frequency of
occurrence of ASVs within these groups.

3 | Results

**3.1 | Sequencing Results**

Sequencing for microalgae yielded an average of 112,772 reads per sample. After primers removal with cutadapt, 1.5% of reads were eliminated, followed by a 13.2% loss of reads during the filtration step, 0.9% after denoising, 12.7% after merging, and finally, 12.5% during the removal of chimeric sequences. As a result, an average of 72,564 reads per sample and a total of 5526 unique ASVs were obtained, with a mean of 263 ASVs per sample. Concerning benthic macroinvertebrates, sequencing resulted in an average of 686,313 reads per sample. After primers removal, 41.9% of the reads were discarded with cutadapt. From the remaining 395,622 average reads per sample, 5.9% were lost during the filtration step, followed by 0.3% after denoising, 3.4% after merging, and finally, and 8.4% during the removal of chimeric sequences. This resulted in an average of 328,334 reads per sample grouped into a total of 17,660 unique ASVs with a mean of 731 ASVs per sample. Sequencing for fish resulted in averages of 687,473 and 1,167,283 reads per sample,

respectively. On average, 59.9% for replicate A and 49.6% for rep-
licate B of the sequences lacked fish-specific primers and were
eliminated with cutadapt. From the remaining 278,084 reads
(A) and 113,756 reads (B) per sample, 7.1% were lost during the
filtration step, followed by 0.1% after denoising, 4.9% (A) and
1.5% (B) after merging, and finally, 2.5% during the removal of
chimeric sequences. The resulting average number of reads per
sample was 239,678 (A) and 638,726 (B) grouped into a total of
1336 unique ASVs in both replicates, with means of 27.3 (A) and
34.1 (B) ASVs per sample.

**3.2 | Primer Specificity**

We looked at primer specificity, a parameter defined as the
primer's ability to amplify a target compared to the ampli-
fication of non-target organisms. Regarding microalgae se-
quenced with ECLA23S_F1-ECLA23S_R1, 91.8% of the 23S
ASVs were assigned to microalgae, while 8.2% was assigned
to non-photosynthetic bacteria and unidentified organisms
(Figure S1a). In the case of macroinvertebrates amplified with
fwhF2-fwhR2N, 45.7% of the COI ASVs were assigned to macro-
invertebrates, and the rest was mostly assigned to algae (17.5%),
fungi (1.6%), rotifers (10.4%), and unidentified organisms (24.8%)
(Figure S1b). For fish sequenced with MiFish-U-F-MiFish-U-R,
98.1% of the 12S ASVs were assigned to fish and only 1.9% to
other organisms (mammals and amphibian) (Figure S1c). Mock
communities were sequenced for macroinvertebrates and fish.
In the case of macroinvertebrates, all of the taxa whose DNA
was included in the mock samples were detected except for
gammarids (Amphipoda) (Figure 2a). In the case of fish, five
species (Silurus glanis, Gobio gobio, Esox lucius, Ameirus melas,
and Lota lota) out of 14 species were not detected from the mock
sample after the removal of species with read counts below the
contamination threshold (135 reads) (Figure 2b). In the initial
<figure><img src="image_5.png" /><figcaption>**FIGURE 3** | Comparison of the observed number of ASV (richness) and the Shannon diversity in water and biofilm for algae (a), macroinvertebrates (b) and fish (c). *p* values significance (t-test): ***<0.001, **<0.01.</figcaption></figure>

mock, these five species represented 2.2% of the total DNA con-
centration (Rivera et al. 2022).

**3.3 | Comparison of Alpha Diversity Metrics**
**Between Matrices**

For microalgae, there were more ASVs uniquely detected in biofilm
than in water (391 on average versus 273 unique ASV, 285 shared),
with 9 unique families and 14 unique genera detected in biofilm
and 15 unique families and 20 unique genera detected in water
(Figure S2). In biofilm, the richness (F(1, 134)=54, p<0.001) and
Shannon (F(1, 134)=11.53, p<0.001) diversity were significantly
higher than in water (Figure 3a). In terms of reads relative abun-
dance throughout the studied period, Bacillariophyta was the main
phylum detected, followed by Cyanobacteria and Chlorophyta in
the biofilm. In water, Cryptophyta and Cyanobacteria were the
most dominant taxa, except in April, when the Bacillariophyta
reads relative abundances peaked (Figure 4a,b). For benthic mac-
roinvertebrates, 288 ASVs were detected exclusively in biofilm
and 39 in water only (with 68 shared), with 8 unique genera and
18 unique species detected in the biofilm and 10 unique genera
and 7 unique species detected in water (Figure S2). In the bio-
film, the richness (F(1, 128)=53.55, p<0.001) and Shannon (F(1,
128)=34.7, p<0.001) diversity were significantly higher than in
water (Figure 3b). Arthropods were found to be the main phylum
detected in biofilm and water, followed by a lower proportion of
mollusks and annelids in biofilm, while cnidarians and poriferans
were mainly detected in water (Figure 4c). More precisely, chi-
ronomids within the order of Diptera were the dominant family
detected (with 26 species in total) in the biofilm at all dates and
from March to May in water (Figure 4). For fish, 137 ASVs were
common between the two matrices. 92 ASVs were exclusively de-
tected in water, while 14 ASVs were detected exclusively in the bio-
film. All the species detected in the biofilm were also detected in

water, but five species were only identified in water (Figure S2). In
the biofilm, the richness (F(1, 118)=206.1, p<0.001) and Shannon
(F(1, 118)=9.63, p<0.01) diversity were significantly lower than in
water (Figure 3c). The Salmonidae family dominated the biofilm
in November and February and then the Percidae and Blenniidae
from May to September (Figure 4f,e). In water, the most abundant
family was the Percidae (Figure 4e).

**3.4 | Comparison of Water and Biofilm Matrices**
**Based on Beta Diversity**

The objective of these analyses was to compare the commu-
nity compositions obtained in the biofilm to those obtained
in water, at a given site on a given date and for each biological
compartment. For microalgae, Bray-Curtis dissimilarities be-
tween water and biofilm communities were high (0.8-1), indi-
cating that algal communities of water and biofilm were very
different in terms of taxa composition throughout the year,
except in April (Figure 5A), where *Diatoma ehrenbergii* domi-
nated both water and biofilm inventories in terms of reads rel-
ative abundance. This pattern can be observed in the NMDS
analysis of the algal communities that showed two distinct el-
lipses, with a proximity of water and biofilm samples in April
(Figure 5B.a). PERMANOVA indicated that the type of envi-
ronmental matrix (water and biofilm) and the date were found
to be highly significant (F(1, 112)=136.16, *p* < 0.001, F(11,
112)=12.31, *p* < 0.001 respectively), explaining each around
30%, and the interaction of these factors significantly ex-
plained 15.7% of the total variability in the community struc-
ture (F(11, 112)=6.5, *p* < 0.001) (Figure 5C.a, Table S3). As for
benthic macroinvertebrates, Bray-Curtis dissimilarities were
high, ranging from 0.8 to 1 (Figure 5A), suggesting that the
benthic macroinvertebrates communities in water and biofilm
were consistently distinct throughout the year. The NMDS
<figure><img src="image_6.png" /><figcaption>**FIGURE 4** | Temporal dynamic of reads relative abundances in water and biofilm at phylum level and within Bacillariophyceae (a, b) for algae, at phylum and family levels within Diptera for macroinvertebrates (c, d), and at family level and within Salmonidae for fish (e, f).</figcaption></figure>

analysis for benthic macroinvertebrates showed a strong overlap between both confidence ellipses, with a wider dispersion for communities detected from water eDNA (Figure 5B.b). PERMANOVA demonstrated the significance of the date and matrix variables ($F(11, 106) = 3.77, p < 0.001$, $F(1, 106) = 15.16, p < 0.001$ respectively), highlighting their roles in characterizing benthic macroinvertebrates communities and accounting for 22.1% and 8.1% of the overall variability, respectively. The interaction significantly explained 13.5% of the total variability in the community structure ($F(11, 106) = 2.31, p < 0.001$) (Figure 5C.b, Table S3). For fish, Bray-Curtis dissimilarities were highly fluctuating within each date, and generally lower (from 0.3-0.8) compared to microalgae and benthic macroinvertebrates (Figure 5A). The NMDS analysis for fish showed that the ellipses of each sample matrix presented a complete overlap, with a wider dispersion within the biofilm

(Figure 5B.c). PERMANOVA revealed that the date was a highly significant predictor ($F(11, 96) = 4.7, p < 0.001$) explaining 30% of the total variability. However, the matrix ($F(1, 96) = 6.7, p < 0.001$) was not a powerful predictor, accounting only for 3.9% of the variance, and the interaction ($F(11, 96) = 1.7, p < 0.01$) significantly explained 10.8% of the total variability in community structure (Figure 5C.c) (Table S3).

## 3.5 | Indicator Taxa of Water and Biofilm Matrices Identified From IndVal Analyses

For microalgae, 123 indicator taxa were identified (Table S4). In biofilm, the majority of these taxa (32) were benthic, while a minority (16) were planktonic and 22 had an undetermined lifestyle (Figure 6). This latter category (undetermined) was
<figure><img src="image_7.png" /><figcaption>**FIGURE 5** | Beta-diversity based on ASV assemblages in water and biofilm for algae (a), macroinvertebrates (b), and fish (c). (A) Temporal dissimilarity between water and biofilm communities calculated with Bray-Curtis distances. (B) Nonmetric multidimensional scaling (NMDS) calculated with Bray-Curtis distances with 95% confidence ellipses. (C) Pie chart of variance proportions explained by significant predictor variables in the linear regression model (PERMANOVA) (*p* < 0.05).</figcaption></figure>

due to limitations in determining the specific habitat life-
style because of an imprecise taxonomic identification (e.g.,
"Oscillatoriales" can be either planktonic or benthic depend-
ing on the genera). In contrast, in water, a large majority of
the indicator taxa (34) were planktonic (Figure 6). For ben-
thic macroinvertebrates, 13 indicator taxa were identified
(Table S4). In biofilm, all of the taxa (10) displayed a benthic
lifestyle. In contrast, in water, one taxon was benthic while
one taxon had a mixed lifestyle and one taxon presented an
undetermined lifestyle (Figure 6). Finally, 11 fish species were
identified as indicators and were all attributed exclusively to
the water matrix (Table S4). Among these species, six were
found to have a direct association with the benthic habitat,
one species was pelagic, and four displayed a mixed lifestyle
(lifestyle depending of the life stage) (Figure 6).

4 | Discussion

4.1 | Primers Specificity: Highly Specific for Microalgae and Fish, but Some Overcoming Pitfalls for Macroinvertebrates

The choice of primers for eDNA-based monitoring is of utmost
importance (e.g., Elbrecht et al. 2017; Hajibabaei et al. 2019).
In our study, we demonstrate the high specificity of the prim-
ers selected for microalgae and fish (Figure S1). The algal
primers specifically target 23S to identify eukaryotic microal-
gae and cyanobacteria while minimizing the amplification of
heterotrophic bacteria (Canino et al. 2023). For fish, which
represent a well-defined taxonomic clade, we also benefited
from highly specific primers (Kelly et al. 2014; Valentini
<figure><img src="image_8.png" /><figcaption>**FIGURE 6** | Number of indicator taxa (assigned ASV) identified with an Indval analysis and their associated habitat lifestyle trait for algae (a), macroinvertebrates (b), and fish (c) in water and biofilm. Only significant indicator taxa (*p* < 0.01) are taken into account. Habitat lifestyle for algae is described as in Rimet et al. 2009, from Usseglio Polatera for macroinvertebrates and Keith et al. (2020) for fish. “Undetermined” refers to algal and macroinvertebrate taxa whose taxonomic identification could not be precise enough to determine their habitat lifestyle. “Mixed” refers to fish species whose habitat lifestyle directly depends on life stages.</figcaption></figure>

et al. 2016), enabling us to detect the majority of taxa down to the species level. Moreover, the use of mock communities for fish confirmed that the sequencing results with these primers mostly reflected the expected relative amounts of DNA introduced (Figure 2).

Selecting primers for macroinvertebrates proved to be more complex. One major challenge was that 54.2% of all taxa amplified in our samples were not assigned to macroinvertebrates (Figure S1). Several reasons may account for this. First, the high genetic variability within the diverse polyphyletic group of macroinvertebrates (Keck et al. 2022) led to the design of degenerated primers, causing amplification of non-target organisms. Second, the relatively low abundance of macroinvertebrates in aquatic environments compared to smaller non-metazoans, such as microalgae, could contribute to this outcome (Macher et al. 2018). This is the case in Lake Geneva where phytoplankton biomass is 1000 µg/L (Cipel 2023). Nevertheless, this percentage of non-specific amplification is lower than that in other studies targeting longer DNA barcodes (Gleason et al. 2021; Rivera et al. 2021), suggesting that targeting shorter barcodes, more adapted to the analysis of potentially degraded released DNA, has reduced this issue. Indeed, extracellular eDNA is subject to fragmentation into smaller sequences (e.g., Bylemans et al. 2018); therefore, amplifying smaller sequences may be more adapted to amplify extracellular eDNA. Furthermore, our mock community revealed another pitfall related to the selected primers. All introduced taxa were detected in the sequenced mock community, except the order of Amphipoda. This lack of detection was due to low PCR primer compatibility (Deagle et al. 2009; Zhou et al. 2013). Molecular studies on Amphipoda have demonstrated extensive species diversity and the existence of cryptic species complexes (Jaždžewska and Mamos 2019), indicating high genetic variability within species, including in the selected primer regions. This variability results in a lack of compatibility with primer pairs, suggesting that sequences in the macroinvertebrate mock community did not exhibit sufficient affinity for the primers used. To address these pitfalls concerning macroinvertebrates, some authors suggest simultaneously using multiple primers targeting the COI region or designing

primers customized to specific taxonomic groups (e.g., Mollusca, Annelida, and Arthropoda). This approach can help avoid biases associated with broad-coverage primers and meet the stringent taxonomic requirements for different macroinvertebrate phyla in aquatic ecosystems (Duarte et al. 2021). Additionally, the presence of numerous unassigned DNA sequences, especially in water samples, could likely be due to a lack of reference sequences in reference databases. Using a robust reference library is crucial, particularly when dealing with highly diverse groups like arthropods (Gleason et al. 2021).

## 4.2 | Exploring Diversity With Water and Biofilm eDNA: Complementary Views for Microalgae and Benthic Macroinvertebrates, Similar for Fish

Our findings showed that the microalgae communities revealed by eDNA displayed high specificity and distinctiveness between both sample matrices (Figures 4 and 5, Figure S2). The distinct habitat preferences of indicator species (Figure 6) suggest that biofilm eDNA mainly reveals the diversity of benthic living taxa, while water eDNA reflects mostly the composition of pelagic communities. Our data suggest that the molecular inventory of microalgae in biofilms mostly results from intracellular eDNA (originating from taxa living in the matrix), which is consistent with former studies (Keck et al. 2017). This is in line with the understanding that eDNA released in the environment tends to be rare and more degraded compared to intracellular eDNA derived from living cells or multicellular organisms (Taberlet et al. 2018). Algal communities in both the biofilm and water samples exhibited seasonal dynamics (Figure 3), which, consistent with former observations of pelagic planktonic and littoral benthic microalgae of Lake Geneva, are linked to abiotic and biotic factors (Anneville et al. 2018; Rimet et al. 2009). However, an intriguing similarity between communities in the two matrices was observed in April. Previous studies have indicated that eDNA release from stream bottom substrate is not constant but rather linked to turbulent events with sufficient energy occurring intermittently (Jerde et al. 2016). Storms, for example, can lead to turnover in beta-diversity by bringing in additional
allochthonous eDNA from outside the study (Bista et al. 2017). In our specific case, the strong winds on 26 April 2021 prior to sampling (https://www.alplakes.eawag.ch/) may have generated turbulence in the environment, leading to the resuspension of cells and their intracellular eDNA usually present in the biofilm. The dominance of *Diatoma ehrenbergii*, a benthic colonial diatom (Rimet and Bouchez 2012), which develops in the biofilm during spring (Rimet et al. 2015), whose DNA was dominant in the water samples in April, supports this hypothesis.

For benthic macroinvertebrates, the communities observed in each matrix also showed distinct compositions (Figures 4 and 5, Figure S2). The taxa detected in the biofilm were mainly from the family Chironomidae, whereas in water, the signal detected originated from a variety of phyla (Figure 4). This result is similar to other studies, which also found that chironomids are often the most abundant family in macroinvertebrate samples (e.g., Beermann et al. 2018). In biofilm samples, the indicator taxa were exclusively benthic, suggesting that the eDNA signal originates from organisms living within the biofilm, like the larvae of Diptera. This finding supports the hypothesis from Rivera et al. (2021) that the macroinvertebrate eDNA in biofilm originates mainly from intracellular eDNA, with a higher quantity and quality of DNA from organisms living in the substrate. This suggests that the two sampling methods likely capture different microhabitats, which has been demonstrated in other studies comparing bulk samples and eDNA from water (Cowart et al. 2015; Serrana et al. 2019; Shea 2023). For the water samples, the dominance of Diptera towards the spring season (Figure 4) and the increased similarity in communities during this period compared to other months (Figure 5) can be explained by several factors. First, the turbulence occurring in April may have re-suspended the biofilm and consequently homogenized the eDNA signal. A second explanation could be related to seasonal variations of several macroinvertebrate orders (e.g., Diptera) that become airbound during late spring and summer (Blackman et al. 2022). A third explanation might involve the presence of extracellular eDNA, as different organisms exhibit varying rates of DNA shedding (Andruszkiewicz Allan et al. 2021; Carraro et al. 2020). However, it is important to note that certain aquatic insect larvae possess sclerotized exoskeletons, and others like Trichopterans construct shelters from abiotic materials, potentially leading to a reduced release of DNA into the surrounding environment (Brantschen et al. 2022).

Regarding fish, we observed that the communities detected in both water and biofilm were similar (Figures 4 and 5, Figure S2). The amount of eDNA released by fish into water strongly depends on biological factors, such as reproductive behavior (Barnes and Turner 2016), and seasonality, which is in line with our observations (i.e., significance of date as explanatory factor of fish communities composition, see Figure 5 and Table S3). The read relative abundances obtained for fish species were influenced by the fish biological activity, as illustrated in winter (December) when the eDNA signal of whitefish (*Coregonus lavaretus* Linnaeus) dominated the Salmonidae family in the biofilm due to its spawning activity in coastal areas (Goulon et al. 2022) and the released eggs which settled on the biofilm. Moreover, our results show that eDNA in the biofilm may better reflect benthic species (Sakata et al. 2020; Zeng et al. 2022), such as *Salariopsis fluviatilis* (Blenniidae), a species living on

rocks and whose read abundance was higher in the biofilm in our study. All fish species identified in water were also detected in the biofilm, except five species for which eDNA was exclusively found in water samples. It could be explained by the fact that the eDNA signal for fish might not have been present in sufficient concentration to be detected in biofilm samples, possibly being degraded or transported before sedimentation (Collins et al. 2018). These five species were the least abundant in terms of number of reads in the water samples (representing 0.018% of the total read abundance) and exhibited sporadic signals. Three species, *Salvelinus fontinalis* Mitchill, *Phoxinus septimaniae* Kottelat, and *Barbatula barbatula* Linnaeus, are living in rivers (Keith et al. 2020) but their eDNA may have drifted to our study site. These findings are consistent with those reported in the study conducted by Rivera et al. (2021), where eDNA detection differences between the two matrices (biofilm, water) were also due to rare taxa.

## 4.3 | eDNA Sampling in Water and Biofilm: Benefits, Recommendations, and Perspectives

Sampling eDNA from the biofilm and water in lake littoral areas offers distinct benefits to assess community diversity. One of the primary advantages of using eDNA metabarcoding compared to classical morphological methods is the significantly improved taxonomic resolution achieved at the genus/species level for each bioindicator (Figure S2). However, the effectiveness of DNA metabarcoding relies on taxonomic expertise for validating assignments and is critically dependent on open, comprehensive, and accurate reference sequence libraries (e.g., Weigand et al. 2019) to ensure reliable species identification. While species identification is relatively straightforward for fish, it is challenging for microalgae and benthic macroinvertebrates. This is particularly important for benthic macroinvertebrates, as precise species resolution is essential in assessing water quality and degradation of microhabitats (Pereira-da-Conceicao et al. 2021 and references therein), which cannot be easily deciphered using traditional morphological methods (e.g., Rutschmann et al. 2014; Vitecek et al. 2017). For chironomids, a highly informative group for lake monitoring (Frossard et al. 2014; Verneaux et al. 2004), morphological identification at the genus level poses challenges (Pereira-da-Conceicao et al. 2021), as it involves mounting the buccal parts of each individual in a permanent resin and observing them under a microscope. eDNA metabarcoding has proven invaluable in identifying taxa diversity in our samples by identifying 26 species from the Chironomidae family. In comparison, the study which evaluated the hydro-biological situation of Lake Geneva in the same coastal zone found that only two taxa of Chironomidae could be identified down to the species level with morphology (Wagner and Raymond 2022). This increased taxonomic resolution was also observed in other studies in which Chironomids could be identified to the species level, demonstrating a significant advantage of the eDNA method for freshwater biomonitoring (Elbrecht et al. 2017). eDNA metabarcoding is also of significant interest for microalgae. Despite long-term investigation of microalgae in Lake Geneva since the 1960s (Rimet et al. 2020), our investigation revealed numerous genera that have never been identified before with morphological methods during annual monitoring. Without molecular identification, many of these algal taxa either require rare taxonomic expertise
(e.g., certain groups in the Chlorophyta, like *Sarcinofilum* or *Aegagropila* genera) or need to be cultured to observe their life cycle for accurate identification (Darienko and Pröschold 2017). Additionally, some algal species are too small to be identified using a microscope (e.g., picocyanobacteria of the genus *Cyanobium*, as shown in Nicolosi Gelis et al. 2024), while others necessitate the use of electron microscopy to observe specific features (e.g., Cryptophyceae to observe the periplaste plates, Kugrens and Clay 2003).

The second benefit is about the complementarity of the biofilm and water matrices in detecting biological communities. For microalgae, the use of different matrices in our study provided complementary information in terms of biodiversity inventories, as each matrix revealed the pelagic and benthic biodiversity. This highlights that the choice of the matrix for eDNA sampling has a major influence on the composition of the detected communities (Hermans et al. 2018). For benthic macroinvertebrates, the biofilm matrix revealed a greater diversity of arthropods, particularly chironomids, while the water revealed a greater diversity of benthic macroinvertebrates. Previous studies in rivers (Aylagas et al. 2016; Polanco et al. 2021) also highlighted that a substantial fraction of macroinvertebrate diversity was uniquely detectable in a specific matrix, such as sediment or water. However, particular macroinvertebrate groups, such as oligochaetes and nematodes, which are underrepresented in our study and recognized as ecological indicators (e.g., Bongers 1990; Moreno et al. 2011) are specifically sampled in sediments, and metabarcoding studies proved their efficacy to assess their diversity in sediments (Vivien et al. 2019). In the case of fish, the data obtained from both matrices exhibited similar communities, being able to draw up an exhaustive inventory of fish in the lake, indicating that these matrices are not complementary but rather redundant in this context.

Based on the benefits observed in our study, we propose the following recommendations for future eDNA sampling to assess the microalgae, benthic macroinvertebrate, and fish communities in the littoral zone of lakes. For fish, we found that both water and biofilm matrices yielded redundant results for the detection of fish species inventoried in Geneva lake. Therefore, the choice of the sampling matrix for fish can be left to the field operator, who can consider the easiest matrix to sample in the field. However, it should be acknowledged that fish amplification was overall more challenging with the biofilm than with water, as it is common in biofilm samples that fish eDNA is found in low abundance, affecting the success of fish amplification (Rivera et al. 2023). It should also be recognized that water eDNA sampling to measure fish diversity is becoming standard practice (e.g., Takahashi et al. 2023). In contrast, for microalgae and benthic macroinvertebrates, the use of different matrices was found to be complementary. To obtain a comprehensive view of the communities inhabiting the various habitats in the littoral zone of lakes, we strongly recommend sampling both water and biofilm matrices. This approach will provide additional and valuable information about the biodiversity of these groups in the littoral zone. Moreover, in the context of restoration actions often aiming to diversify habitats and which are key actions in national biodiversity legislations to answer COP15 agreements, considering eDNA sampling from different matrices becomes even more important (Port et al. 2016). Looking ahead, we

suggest that further diversification of the sampling strategy to compare the results with identifications based on traditional methods (Brantschen et al. 2022) could be beneficial. These additional sampling approaches may provide even more comprehensive insights into the ecological dynamics and biodiversity of the littoral zone of lakes.

## Author Contributions

Paula Gauvin, Frédéric Rimet, Agnès Bouchez, and Isabelle Domaizon conceived the study, laboratory, and bioinformatics treatment. Frédéric Rimet and Agnès Bouchez performed biofilm and water sampling. Paula Gauvin carried out DNA bioinformatics, data analysis, and wrote the manuscript with significant contributions from all authors. All authors gave their final approval for publication.

## Acknowledgments

This study was made possible thanks to a funding from the Carnot Eau & Environnement Institute, a funding from the INRAE department AQUA, and a funding from the pôle INRAE-OFB ECLA (ECosystèmes LAcustres). We thank Louis Jacas, who carried out the DNA extractions, PCR, and libraries. We thank the OFB (French Biodiversity Office, JC Raymond) and Thonon-Agglomeration (A Sahuc, M Gleize). Sequencing of the 23S sequences was performed at the PGTB (doi: 10.15454/1.5572 396583599417E12), with the help of Emilie Chencerel. We also thank Lidwine Trouilh and Adrien Castinel from the GeT-Biopuces and GeT-PlaGe platforms from the INSA Toulouse Biotechnology Institute.

## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

The Illumina and NovaSeq raw data (sequences), as well as the related metadata and bioinformatic R scripts (dada2) and scripts relating to cleaning data, can be found at: https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/TCA5HA.

## References

Ahlmann-Eltze, C., and I. Patil. 2021. "ggsignif: R Package for Displaying Significance Brackets for 'ggplot2'." https://doi.org/10.31234/osf.io/7awm6.

Altermatt, F., C. J. Little, E. Mächler, S. Wang, X. Zhang, and R. C. Blackman. 2020. "Uncovering the Complete Biodiversity Structure in Spatial Networks: The Example of Riverine Systems." *Oikos* 129, no. 5: 607-618. https://doi.org/10.1111/oik.06806.

Andruszkiewicz Allan, E., W. G. Zhang, A. Lavery, and A. Govindarajan. 2021. "Environmental DNA Shedding and Decay Rates From Diverse Animal Forms and Thermal Regimes." *Environmental DNA* 3, no. 2: 492-514. https://doi.org/10.1002/edn3.141.

Anneville, O., G. Dur, F. Rimet, and S. Souissi. 2018. "Plasticity in Phytoplankton Annual Periodicity: An Adaptation to Long-Term Environmental Changes." *Hydrobiologia* 824, no. 1: 121-141. https://doi.org/10.1007/s10750-017-3412-z.

Argillier, C., S. Caussé, M. Gevrey, et al. 2013. "Development of a Fish-Based Index to Assess the Eutrophication Status of European Lakes." *Hydrobiologia* 704, no. 1: 193-211. https://doi.org/10.1007/s10750-012-1282-y.

Aylagas, E., Á. Borja, X. Irigoien, and N. Rodríguez-Ezpeleta. 2016. "Benchmarking DNA Metabarcoding for Biodiversity-Based Monitoring and Assessment." *Frontiers in Marine Science* 3: 96. https://doi.org/10.3389/fmars.2016.00096.
Aylagas, E., Á. Borja, and N. Rodríguez-Ezpeleta. 2014. "Environmental Status Assessment Using DNA Metabarcoding: Towards a Genetics Based Marine Biotic Index (gAMBI)." *PLoS One* 9, no. 3: e90529. https://doi.org/10.1371/journal.pone.0090529.

Barnes, M. A., and C. R. Turner. 2016. "The Ecology of Environmental DNA and Implications for Conservation Genetics." *Conservation Genetics* 17, no. 1: 1–17. https://doi.org/10.1007/s10592-015-0775-4.

Bates, B. C., Z. W. Kundzewicz, S. Wu, and J. P. Palutikof, eds. 2008. *Climate Change and Water. Technical Paper of the Intergovernmental Panel on Climate Change*. IPCC Secretariat. 210.

Battin, T. J., K. Besemer, M. M. Bengtsson, A. M. Romani, and A. I. Packmann. 2016. "The Ecology and Biogeochemistry of Stream Biofilms." *Nature Reviews Microbiology* 14, no. 4: 251–263. https://doi.org/10.1038/nrmicro.2016.15.

Beermann, A. J., V. M. A. Zizka, V. Elbrecht, V. Baranov, and F. Leese. 2018. "DNA Metabarcoding Reveals the Complex and Hidden Responses of Chironomids to Multiple Stressors." *Environmental Sciences Europe* 30, no. 1: 26. https://doi.org/10.1186/s12302-018-0157-x.

Birk, S., W. Bonne, A. Borja, et al. 2012. "Three Hundred Ways to Assess Europe's Surface Waters: An Almost Complete Overview of Biological Methods to Implement the Water Framework Directive." *Ecological Indicators* 18: 31–41. https://doi.org/10.1016/j.ecolind.2011.10.009.

Bista, I., G. R. Carvalho, K. Walsh, et al. 2017. "Annual Time-Series Analysis of Aqueous eDNA Reveals Ecologically Relevant Dynamics of Lake Ecosystem Biodiversity." *Nature Communications* 8, no. 1: 14087. https://doi.org/10.1038/ncomms14087.

Blackman, R. C., H.-C. Ho, J.-C. Walser, and F. Altermatt. 2022. "Spatio-Temporal Patterns of Multi-Trophic Biodiversity and Food-Web Characteristics Uncovered Across a River Catchment Using Environmental DNA." *Communications Biology* 5, no. 1: 259. https://doi.org/10.1038/s42003-022-03216-z.

Bohan, D. A., C. Vacher, A. Tamaddoni-Nezhad, A. Raybould, A. J. Dumbrell, and G. Woodward. 2017. "Next-Generation Global Biomonitoring: Large-Scale, Automated Reconstruction of Ecological Networks." *Trends in Ecology & Evolution* 32, no. 7: 477–487. https://doi.org/10.1016/j.tree.2017.03.001.

Bongers, T. 1990. "The Maturity Index: An Ecological Measure of Environmental Disturbance Based on Nematode Species Composition." *Oecologia* 83, no. 1: 14–19. https://doi.org/10.1007/BF00324627.

Brantschen, J., L. Pellissier, J. Walser, and F. Altermatt. 2022. "Evaluation of Primer Pairs for eDNA-Based Assessment of Ephemeroptera, Plecoptera, and Trichoptera Across a Biogeographically Diverse Region." *Environmental DNA* 4, no. 6: 1356–1368. https://doi.org/10.1002/edn3.342.

Buxton, A. S., J. J. Groombridge, and R. A. Griffiths. 2017. "Is the Detection of Aquatic Environmental DNA Influenced by Substrate Type?" *PLoS One* 12, no. 8: e0183371. https://doi.org/10.1371/journal.pone.0183371.

Bylemans, J., E. M. Furlan, D. M. Gleeson, C. M. Hardy, and R. P. Duncan. 2018. "Does Size Matter? An Experimental Evaluation of the Relative Abundance and Decay Rates of Aquatic Environmental DNA." *Environmental Science & Technology* 52, no. 11: 6408–6416. https://doi.org/10.1021/acs.est.8b01071.

Callahan, B. J., P. J. McMurdie, M. J. Rosen, A. W. Han, A. J. A. Johnson, and S. P. Holmes. 2016. "DADA2: High-Resolution Sample Inference From Illumina Amplicon Data." *Nature Methods* 13, no. 7: 581–583. https://doi.org/10.1038/nmeth.3869.

Canino, A., A. Bouchez, C. Laplace-Treyure, I. Domaizon, and F. Rimet. 2021. "Phytool, a ShinyApp to Homogenise Taxonomy of Freshwater Microalgae From DNA Barcodes and Microscopic Observations." *Metabarcoding and Metagenomics* 5: e74096. https://doi.org/10.3897/mbmg.5.74096.

Canino, A., C. Lemonnier, B. Alric, et al. 2023. "Which Barcode to Decipher Freshwater Microalgal Assemblages? Tests on Mock Communities." *International Journal of Limnology* 59: 8. https://doi.org/10.1051/limn/2023008.

Carraro, L., E. Mächler, R. Wüthrich, and F. Altermatt. 2020. "Environmental DNA Allows Upscaling Spatial Patterns of Biodiversity in Freshwater Ecosystems." *Nature Communications* 11, no. 1: 3585. https://doi.org/10.1038/s41467-020-17337-8.

CEN/TR 17245:2018. 2018. "Water Quality—Technical Report for the Routine Sampling of Benthic Diatoms From Rivers and Lakes Adapted for Metabarcoding Analyses." https://standards.iteh.ai/catalog/standards/cen/31e578ee-1135-49d8-9446-94b56d9c267e/cen-tr-17245-2018.

Chon, T.-S., X. Qu, W.-S. Cho, et al. 2013. "Evaluation of Stream Ecosystem Health and Species Association Based on Multi-Taxa (Benthic Macroinvertebrates, Algae, and Microorganisms) Patterning With Different Levels of Pollution." *Ecological Informatics* 17: 58–72. https://doi.org/10.1016/j.ecoinf.2013.06.004.

Chonova, T., R. Kurmayer, F. Rimet, et al. 2019. "Benthic Diatom Communities in an Alpine River Impacted by Waste Water Treatment Effluents as Revealed Using DNA Metabarcoding." *Frontiers in Microbiology* 10: 653. https://doi.org/10.3389/fmicb.2019.00653.

Cipel. 2023. "Rapports Sur Les Études Et Recherches Entreprises Dans Le Bassin Lémanique." Commission internationale pour la protection des eaux du Léman contre la pollution—CIPEL.

Collins, R. A., O. S. Wangensteen, E. J. O'Gorman, S. Mariani, D. W. Sims, and M. J. Genner. 2018. "Persistence of Environmental DNA in Marine Systems." *Communications Biology* 1, no. 1: 185. https://doi.org/10.1038/s42003-018-0192-6.

Coutant, O., I. Cantera, K. Cilleros, et al. 2021. "Detecting Fish Assemblages With Environmental DNA: Does Protocol Matter? Testing eDNA Metabarcoding Method Robustness." *Environmental DNA* 3, no. 3: 619–630. https://doi.org/10.1002/edn3.158.

Cowart, D. A., M. Pinheiro, O. Mouchel, et al. 2015. "Metabarcoding Is Powerful Yet Still Blind: A Comparative Analysis of Morphological and Molecular Surveys of Seagrass Communities." *PLoS One* 10, no. 2: e0117562. https://doi.org/10.1371/journal.pone.0117562.

Darienko, T., and T. Pröschold. 2017. "Toward a Monograph of Non-Marine Ulvophyceae Using an Integrative Approach (Molecular Phylogeny and Systematics of Terrestrial Ulvophyceae II.)." *Phytotaxa* 324, no. 1: 1. https://doi.org/10.11646/phytotaxa.324.1.1.

Deagle, B. E., R. Kirkwood, and S. N. Jarman. 2009. "Analysis of Australian fur Seal Diet by Pyrosequencing Prey DNA in Faeces." *Molecular Ecology* 18, no. 9: 2022–2038. https://doi.org/10.1111/j.1365-294X.2009.04158.x.

Deiner, K., H. M. Bik, E. Mächler, et al. 2017. "Environmental DNA Metabarcoding: Transforming How We Survey Animal and Plant Communities." *Molecular Ecology* 26, no. 21: 5872–5895. https://doi.org/10.1111/mec.14350.

Duarte, S., B. Leite, M. Feio, F. Costa, and A. Filipe. 2021. "Integration of DNA-Based Approaches in Aquatic Ecological Assessment Using Benthic Macroinvertebrates." *Water* 13, no. 3: 331. https://doi.org/10.3390/w13030331.

Dudgeon, D., A. H. Arthington, M. O. Gessner, et al. 2006. "Freshwater Biodiversity: Importance, Threats, Status and Conservation Challenges." *Biological Reviews* 81, no. 2: 163–182. https://doi.org/10.1017/S1464793105006950.

Dufrene, M., and P. Legendre. 1997. "Species Assemblages and Indicator Species: The Need for a Flexible Asymmetrical Approach." *Ecological Monographs* 67, no. 3: 345. https://doi.org/10.2307/2963459.

Elbrecht, V., and F. Leese. 2015. "Can DNA-Based Ecosystem Assessments Quantify Species Abundance? Testing Primer Bias and Biomass—Sequence Relationships With an Innovative Metabarcoding
Protocol.” *PLoS One* 10, no. 7: e0130324. https://doi.org/10.1371/journal.pone.0130324.

Elbrecht, V., E. E. Vamos, K. Meissner, J. Aroviita, and F. Leese. 2017. "Assessing Strengths and Weaknesses of DNA Metabarcoding-Based Macroinvertebrate Identification for Routine Stream Monitoring." *Methods in Ecology and Evolution* 8, no. 10: 1265-1275. https://doi.org/10.1111/2041-210X.12789.

EN 13946. 2014. "Water quality—Guidance for the Routine Sampling and Preparation of Benthic Diatoms From Rivers and Lakes." iTeh Standards. Accessed January 24, 2024. https://standards.iteh.ai/catalog/standards/cen/7a0de152-2689-4d1b-b012-46e980cb045a/en/13946-2014.

Feckler, A., A. Thielsch, K. Schwenk, R. Schulz, and M. Bundschuh. 2012. "Differences in the Sensitivity Among Cryptic Lineages of the Gammarus Fossarum Complex." *Science of the Total Environment* 439: 158-164. https://doi.org/10.1016/j.scitotenv.2012.09.003.

Ficetola, G. F., J. Pansu, A. Bonin, et al. 2015. "Replication Levels, False Presences and the Estimation of the Presence/Absence From eDNA Metabarcoding Data." *Molecular Ecology Resources* 15, no. 3: 543-556. https://doi.org/10.1111/1755-0998.12338.

Friberg, N., N. Bonada, D. C. Bradley, et al. 2011. "Biomonitoring of Human Impacts in Freshwater Ecosystems." *Advances in Ecological Research* 44: 1-68. https://doi.org/10.1016/B978-0-12-374794-5.00001-8.

Frossard, V., L. Millet, V. Verneaux, et al. 2014. "Depth-Specific Responses of a Chironomid Assemblage to Contrasting Anthropogenic Pressures: A Palaeoimmological Perspective From the Last 150 Years." *Freshwater Biology* 59, no. 1: 26-40. https://doi.org/10.1111/fwb.12243.

Gleason, J. E., V. Elbrecht, T. W. A. Braukmann, R. H. Hanner, and K. Cottenie. 2021. "Assessment of Stream Macroinvertebrate Communities With eDNA Is Not Congruent With Tissue-Based Metabarcoding." *Molecular Ecology* 30, no. 13: 3239-3251. https://doi.org/10.1111/mec.15597.

Gleason, J. E., and R. C. Rooney. 2017. "Aquatic Macroinvertebrates Are Poor Indicators of Agricultural Activity in Northern Prairie Pothole Wetlands." *Ecological Indicators* 81: 333-339. https://doi.org/10.1016/j.ecolind.2017.06.013.

Goulon, C., M. Vautier, C. Rautureau, I. Domaizon, and J. Guillard. 2022. "Frai Du Corégone (Coregonus SP.) ET DE LA Perche (Perca Fluviatilis) Dans LE Léman - Campagne 2021." INRAE, 15. https://hal-04839989.

Haase, P., J. Murray-Bligh, S. Lohse, et al. 2006. "Assessing the Impact of Errors in Sorting and Identifying Macroinvertebrate Samples." *Hydrobiologia* 566, no. 1: 505-521. https://doi.org/10.1007/s10750-006-0075-6.

Hajibabaei, M., T. M. Porter, C. V. Robinson, D. J. Baird, S. Shokralla, and M. T. G. Wright. 2019. "Watered-Down Biodiversity? A Comparison of Metabarcoding Results From DNA Extracted From Matched Water and Bulk Tissue Biomonitoring Samples." *PLoS One* 14, no. 12: e0225409. https://doi.org/10.1371/journal.pone.0225409.

Hänfling, B., L. Lawson Handley, D. S. Read, et al. 2016. "Environmental DNA Metabarcoding of Lake Fish Communities Reflects Long-Term Data From Established Survey Methods." *Molecular Ecology* 25, no. 13: 3101-3119. https://doi.org/10.1111/mec.13660.

Harrison, J. B., J. M. Sunday, and S. M. Rogers. 2019. "Predicting the Fate of eDNA in the Environment and Implications for Studying Biodiversity." *Proceedings of the Royal Society B: Biological Sciences* 286, no. 1915: 20191409. https://doi.org/10.1098/rspb.2019.1409.

Hering, D., A. Borja, J. I. Jones, et al. 2018. "Implementation Options for DNA-Based Identification Into Ecological Status Assessment Under the European Water Framework Directive." *Water Research* 138: 192-205. https://doi.org/10.1016/j.watres.2018.03.003.

Hering, D., R. K. Johnson, S. Kramm, S. Schmutz, K. Szoszkiewicz, and P. F. M. Verdonschot. 2006. "Assessment of European Streams With Diatoms, Macrophytes, Macroinvertebrates and Fish: A Comparative

Metric-Based Analysis of Organism Response to Stress." *Freshwater Biology* 51, no. 9: 1757-1785. https://doi.org/10.1111/j.1365-2427.2006.01610.x.

Hermans, S. M., H. L. Buckley, and G. Lear. 2018. "Optimal Extraction Methods for the Simultaneous Analysis of DNA From Diverse Organisms and Sample Types." *Molecular Ecology Resources* 18, no. 3: 557-569. https://doi.org/10.1111/1755-0998.12762.

Hervé, A., I. Domaizon, J.-M. Baudoin, et al. 2022. "Spatio-Temporal Variability of eDNA Signal and Its Implication for Fish Monitoring in Lakes." *PLoS One* 17, no. 8: e0272660. https://doi.org/10.1371/journal.pone.0272660.

Hyvärinen, H., A. Skyttä, S. Jernberg, K. Meissner, H. Kuosa, and L. Uusitalo. 2021. "Cost-Efficiency Assessments of Marine Monitoring Methods Lack Rigor—A Systematic Mapping of Literature and an End-User View on Optimal Cost-Efficiency Analysis." *Environmental Monitoring and Assessment* 193, no. 7: 400. https://doi.org/10.1007/s10661-021-09159-y.

Jackson, J. K., J. M. Battle, B. P. White, et al. 2014. "Cryptic Biodiversity in Streams: A Comparison of Macroinvertebrate Communities Based on Morphological and DNA Barcode Identifications." *Freshwater Science* 33, no. 1: 312-324. https://doi.org/10.1086/675225.

Jaźdźewska, A. M., and T. Mamos. 2019. "High Species Richness of Northwest Pacific Deep-Sea Amphipods Revealed Through DNA Barcoding." *Progress in Oceanography* 178: 102184. https://doi.org/10.1016/j.pocean.2019.102184.

Jerde, C. L., B. P. Olds, A. J. Shogren, et al. 2016. "Influence of Stream Bottom Substrate on Retention and Transport of Vertebrate Environmental DNA." *Environmental Science & Technology* 50, no. 16: 8770-8779. https://doi.org/10.1021/acs.est.6b01761.

Jones, F. C. 2008. "Taxonomic Sufficiency: The Influence of Taxonomic Resolution on Freshwater Bioassessments Using Benthic Macroinvertebrates." *Environmental Reviews* 16: 45-69. https://doi.org/10.1139/A07-010.

Keck, F., R. C. Blackman, R. Bossart, et al. 2022. "Meta-Analysis Shows Both Congruence and Complementarity of DNA and eDNA Metabarcoding to Traditional Methods for Biological Community Assessment." *Molecular Ecology* 31, no. 6: 1820-1835. https://doi.org/10.1111/mec.16364.

Keck, F., V. Vasselon, K. Tapolczai, F. Rimet, and A. Bouchez. 2017. "Freshwater Biomonitoring in the Information Age." *Frontiers in Ecology and the Environment* 15, no. 5: 266-274. https://doi.org/10.1002/fee.1490.

Keith, P., N. Poulet, G. Denys, T. Changeux, É. Feunteun, and H. Persat. (coord.) 2020. "Les Poissons d'eau douce de France." In *Deuxième édition. Muséum national d'Histoire naturelle*, 704. Biotope, Mêze. (Inventaires & biodiversité 18).

Kelly, R. P., J. A. Port, K. M. Yamahara, and L. B. Crowder. 2014. "Using Environmental DNA to Census Marine Fishes in a Large Mesocosm." *PLoS One* 9, no. 1: e86175. https://doi.org/10.1371/journal.pone.0086175.

Kugrens, P., and B. L. Clay. 2003. "21—Cryptomonads." In *Freshwater Algae of North America*, edited by J. D. Wehr and R. G. Sheath, 715-755. Academic Press. https://doi.org/10.1016/B978-012741550-5/50022-2.

Leese, F., F. Altermatt, A. Bouchez, et al. 2016. "DNAqua-Net: Developing New Genetic Tools for Bioassessment and Monitoring of Aquatic Ecosystems in Europe." *Research Ideas & Outcomes* 2: e11321. https://doi.org/10.3897/rio.2.e11321.

Leese, F., M. Sander, D. Buchner, V. Elbrecht, P. Haase, and V. M. A. Zizka. 2021. "Improved Freshwater Macroinvertebrate Detection From Environmental DNA Through Minimized Nontarget Amplification." *Environmental DNA* 3, no. 1: 261-276. https://doi.org/10.1002/edn3.177.

Liu, Y., G. H. Wikfors, J. M. Rose, R. S. McBride, L. M. Milke, and R. Mercialdo-Allen. 2019. "Application of Environmental DNA Metabarcoding to Spatiotemporal Finfish Community Assessment in a
Temperate Embayment.” *Frontiers in Marine Science* 6: 674. https://doi.org/10.3389/fmar.2019.00674.

Macher, J.-N., A. Vivancos, J. J. Piggott, F. C. Centeno, C. D. Matthaei, and F. Leese. 2018. "Comparison of Environmental DNA and Bulk-Sample Metabarcoding Using Highly Degenerate COI Primers." *Molecular Ecology Resources* 18, no. 6: 1456–1468. https://doi.org/10.1111/1755-0998.12940.

Mackenzie, D. I., and J. A. Royle. 2005. "Designing Occupancy Studies: General Advice and Allocating Survey Effort: Designing Occupancy Studies." *Journal of Applied Ecology* 42, no. 6: 1105–1114. https://doi.org/10.1111/j.1365-2664.2005.01098.x.

Martin, M. 2011. "Cutadapt Removes Adapter Sequences From High-Throughput Sequencing Reads."

McMurdie, P. J., and S. Holmes. 2013. "Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data." *PLoS One* 8, no. 4: e61217. https://doi.org/10.1371/journal.pone.0061217.

Meybeck, M. 2003. "Global Analysis of River Systems: From Earth System Controls to Anthropocene Syndromes." *Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences* 358, no. 1440: 1935–1955. https://doi.org/10.1098/rstb.2003.1379.

Miya, M., Y. Sato, T. Fukunaga, et al. 2015. "MiFish, a Set of Universal PCR Primers for Metabarcoding Environmental DNA From Fishes: Detection of More Than 230 Subtropical Marine Species." *Royal Society Open Science* 2, no. 7: 150088. https://doi.org/10.1098/rsos.150088.

Mora, J., A. Freixa, N. Perujo, and L. Barral-Fraga. 2016. Limits of the Biofilm Concept and Types of Aquatic Biofilms, p. 24. https://doi.org/10.21775/9781910190173.01.

Moreno, M., F. Semprucci, L. Vezzulli, M. Balsamo, M. Fabiano, and G. Albertelli. 2011. "The Use of Nematodes in Assessing Ecological Quality Status in the Mediterranean Coastal Ecosystems." *Ecological Indicators* 11, no. 2: 328–336. https://doi.org/10.1016/j.ecolind.2010.05.011.

Nevers, M. B., K. Przybyla-Kelly, D. Shively, C. C. Morris, J. Dickey, and M. N. Byappanahalli. 2020. "Influence of Sediment and Stream Transport on Detecting a Source of Environmental DNA." *PLoS One* 15, no. 12: e0244086. https://doi.org/10.1371/journal.pone.0244086.

Nicolosi Gelis, M. M., A. Canino, A. Bouchez, et al. 2024. "Assessing the Relevance of DNA Metabarcoding Compared to Morphological Identification for Lake Phytoplankton Monitoring." *Science of the Total Environment* 914: 169774. https://doi.org/10.1016/j.scitotenv.2023.169774.

Oksanen, J., G. L. Simpson, F. G. Blanchet, et al. 2022. "vegan: Community Ecology Package."

Pawlowski, J., M. Kelly-Quinn, F. Altermatt, et al. 2018. "The Future of Biotic Indices in the Ecogenomic Era: Integrating (e)DNA Metabarcoding in Biological Assessment of Aquatic Ecosystems." *Science of the Total Environment* 637–638: 1295–1310. https://doi.org/10.1016/j.scitotenv.2018.05.002.

Pereira-da-Conceicao, L., V. Elbrecht, A. Hall, A. Briscoe, H. Barber-James, and B. Price. 2021. "Metabarcoding Unsorted Kick-Samples Facilitates Macroinvertebrate-Based Biomonitoring With Increased Taxonomic Resolution, While Outperforming Environmental DNA." *Environmental DNA* 3, no. 2: 353–371. https://doi.org/10.1002/edn3.116.

Périat, G., and P. Vonlanthen. 2014. "Etude du peuplement pisciaire du Lac Léman."

Polanco, F. A., M. Mutis Martinezguerra, V. Marques, et al. 2021. "Detecting Aquatic and Terrestrial Biodiversity in a Tropical Estuary Using Environmental DNA." *Biotropica* 53, no. 6: 1606–1619. https://doi.org/10.1111/btp.13009.

Port, J. A., J. L. O'Donnell, O. C. Romero-Maraccini, et al. 2016. "Assessing Vertebrate Biodiversity in a Kelp Forest Ecosystem Using Environmental DNA." *Molecular Ecology* 25, no. 2: 527–541. https://doi.org/10.1111/mec.13481.

R Core Team. 2022. *R: A Language and Environment for Statistical Computing*. R Foundation for Statistical Computing. https://www.r-project.org/.

Rimet, F., O. Anneville, D. Barbet, et al. 2020. "The Observatory on LAkes (OLA) Database: Sixty Years of Environmental Data Accessible to the Public: The Observatory on LAkes (OLA) Database." *Journal of Limnology* 79, no. 2: 164–178. https://doi.org/10.4081/jlimnol.2020.1944.

Rimet, F., and A. Bouchez. 2012. "Life-Forms, Cell-Sizes and Ecological Guilds of Diatoms in European Rivers." *Knowledge and Management of Aquatic Ecosystems* 406: 01. https://doi.org/10.1051/kmae/2012018.

Rimet, F., A. Bouchez, and B. Montuelle. 2015. "Benthic Diatoms and Phytoplankton to Assess Nutrients in a Large Lake: Complementarity of Their Use in Lake Geneva (France–Switzerland)." *Ecological Indicators* 53: 231–239. https://doi.org/10.1016/j.ecolind.2015.02.008.

Rimet, F., J.-C. Druart, and O. Anneville. 2009. "Exploring the Dynamics of Plankton Diatom Communities in Lake Geneva Using Emergent Self-Organizing Maps (1974–2007)." *Ecological Informatics* 4, no. 2: 99–110. https://doi.org/10.1016/j.ecoinf.2009.01.006.

Rimet, F., V. Vasselon, A.-B. Keszte, and A. Bouchez. 2018. "Do We Similarly Assess Diversity With Microscopy and High-Throughput Sequencing? Case of Microalgae in Lakes." *Organisms Diversity & Evolution* 18, no. 1: 51–62. https://doi.org/10.1007/s13127-018-0359-5.

Rivera, S. F., F. Rimet, V. Vasselon, M. Vautier, I. Domaizon, and A. Bouchez. 2022. "Fish eDNA Metabarcoding From Aquatic Biofilm Samples: Methodological Aspects." *Molecular Ecology Resources* 22, no. 4: 1440–1453. https://doi.org/10.1111/1755-0998.13568.

Rivera, S. F., V. Vasselon, A. Bouchez, and F. Rimet. 2023. "eDNA Metabarcoding From Aquatic Biofilms Allows Studying Spatial and Temporal Fluctuations of Fish Communities From Lake Geneva." *Environmental DNA* 5, no. 3: 570–581. https://doi.org/10.1002/edn3.413.

Rivera, S. F., V. Vasselon, N. Mary, O. Monnier, F. Rimet, and A. Bouchez. 2021. "Exploring the Capacity of Aquatic Biofilms to Act as Environmental DNA Samplers: Test on Macroinvertebrate Communities in Rivers." *Science of the Total Environment* 763: 144208. https://doi.org/10.1016/j.scitotenv.2020.144208.

Robinson, C. V., T. M. Porter, V. C. Maitland, M. T. G. Wright, and M. Hajibabaei. 2022. "Multi-Marker Metabarcoding Resolves Subtle Variations in Freshwater Condition: Bioindicators, Ecological Traits, and Trophic Interactions." *Ecological Indicators* 145: 109603. https://doi.org/10.1016/j.ecolind.2022.109603.

Roche, K. R., J. D. Drummond, F. Boano, A. I. Packman, T. J. Battin, and W. R. Hunter. 2017. "Benthic Biofilm Controls on Fine Particle Dynamics in Streams: Biofilm-Particle Dynamics." *Water Resources Research* 53, no. 1: 222–236. https://doi.org/10.1002/2016WR019041.

Rourke, M. L., A. M. Fowler, J. M. Hughes, et al. 2022. "Environmental DNA (eDNA) as a Tool for Assessing Fish Biomass: A Review of Approaches and Future Considerations for Resource Surveys." *Environmental DNA* 4, no. 1: 9–33. https://doi.org/10.1002/edn3.185.

Ruppert, K. M., R. J. Kline, and M. S. Rahman. 2019. "Past, Present, and Future Perspectives of Environmental DNA (eDNA) Metabarcoding: A Systematic Review in Methods, Monitoring, and Applications of Global eDNA." *Global Ecology and Conservation* 17: e00547. https://doi.org/10.1016/j.gecco.2019.e00547.

Rutschmann, S., J.-L. Gattolliat, S. J. Hughes, M. Báez, M. Sartori, and M. T. Monaghan. 2014. "Evolution and Island Endemism of Morphologically Cryptic Baetis and Cloeon Species (Ephemeroptera, Baetidae) on the Canary Islands and Madeira." *Freshwater Biology* 59, no. 12: 2516–2527. https://doi.org/10.1111/fwb.12450.

Sabater, S., H. Guasch, M. Ricart, et al. 2007. "Monitoring the Effect of Chemicals on Biological Communities. The Biofilm as an Interface." *Analytical and Bioanalytical Chemistry* 387, no. 4: 1425–1434.
Sakata, M. K., S. Yamamoto, R. O. Gotoh, M. Miya, H. Yamanaka, and T. Minamoto. 2020. "Sedimentary eDNA Provides Different Information on Timescale and Fish Species Composition Compared With Aqueous eDNA." *Environmental DNA* 2, no. 4: 505-518. https://doi.org/10.1002/edn3.75.

Sato, Y., M. Miya, T. Fukunaga, T. Sado, and W. Iwasaki. 2018. "MitoFish and MiFish Pipeline: A Mitochondrial Genome Database of Fish With an Analysis Pipeline for Environmental DNA Metabarcoding." *Molecular Biology and Evolution* 35, no. 6: 1553-1555. https://doi.org/10.1093/molbev/msy074.

Schenekar, T. 2023. "The Current State of eDNA Research in Freshwater Ecosystems: Are We Shifting From the Developmental Phase to Standard Application in Biomonitoring?" *Hydrobiologia* 850, no. 6: 1263-1282. https://doi.org/10.1007/s10750-022-04891-z.

Serrana, J. M., Y. Miyake, M. Gamboa, and K. Watanabe. 2019. "Comparison of DNA Metabarcoding and Morphological Identification for Stream Macroinvertebrate Biodiversity Assessment and Monitoring." *Ecological Indicators* 101: 963-972. https://doi.org/10.1016/j.ecolind.2019.02.008.

Shea, M. M. 2023. Environmental DNA (eDNA) Metabarcoding Differentiates Between micro-Habitats Within the Rocky Intertidal.

Shogren, A. J., J. L. Tank, S. P. Egan, et al. 2018. "Water Flow and Biofilm Cover Influence Environmental DNA Detection in Recirculating Streams." *Environmental Science & Technology* 52, no. 15: 8530-8537. https://doi.org/10.1021/acs.est.8b01822.

Smith, C. D., M. C. Quist, and R. S. Hardy. 2015. "Detection Probabilities of Electrofishing, Hoop Nets, and Benthic Trawls for Fishes in Two Western North American Rivers." *Journal of Fish and Wildlife Management* 6, no. 2: 371-391. https://doi.org/10.3996/022015-JFWM-011.

Taberlet, P., A. Bonin, L. Zinger, and E. Coissac. 2018. *Environmental DNA: For Biodiversity Research and Monitoring*. Oxford University Press. https://doi.org/10.1093/oso/9780198767220.001.0001.

Taberlet, P., E. Coissac, M. Hajibabaei, and L. H. Rieseberg. 2012. "Environmental DNA: Environmental DNA." *Molecular Ecology* 21, no. 8: 1789-1793. https://doi.org/10.1111/j.1365-294X.2012.05542.x.

Tachet, H., P. Richoux, M. Bournaud, and P. Usseglio-Polatera. 2000. "Invertébrés d'eau douce—Systématique, biologie, écologie—CNRS Editions (CNRS)." https://www.cnrseditions.fr/catalogue/ecologie-environnement-sciences-de-la-terre/invertebres-d-eau-douce-henri-tachet/.

Takahashi, M., M. Saccò, J. H. Kestel, et al. 2023. "Aquatic Environmental DNA: A Review of the Macro-Organismal Biomonitoring Revolution." *Science of The Total Environment* 873: 162322. https://doi.org/10.1016/j.scitotenv.2023.162322.

Valentini, A., P. Taberlet, C. Miaud, et al. 2016. "Next-Generation Monitoring of Aquatic Biodiversity Using Environmental DNA Metabarcoding." *Molecular Ecology* 25, no. 4: 929-942. https://doi.org/10.1111/mec.13428.

Vamos, E., V. Elbrecht, and F. Leese. 2017. "Short COI Markers for Freshwater Macroinvertebrate Metabarcoding." *Metabarcoding and Metagenomics* 1: e14625. https://doi.org/10.3897/mbmg.1.14625.

Vautier, M., C. Chardon, and I. Domaizon. 2021. "Fish eDNA: Water Sampling and Filtrationthrough Sterivex Filter Unit v1 [Preprint]." https://doi.org/10.17504/protocols.io.br5rm856.

Vautier, M., V. Vasselon, C. Chardon, F. Rimet, A. Bouchez, and I. Domaizon. 2020. "DNA Extraction From Environmental Biofilm Using the NucleoSpin® Soil Kit (MACHEERY-NAGEL) v1 [Preprint]." https://doi.org/10.17504/protocols.io.bd52i88e.

Verneaux, V., J. Verneaux, A. Schmitt, C. Lovy, and J. C. Lambert. 2004. "The Lake Biotic Index (LBI): An Applied Method for Assessing the Biological Quality of Lakes Using Macrobenthos; the Lake Châlain

(French Jura) as an Example." *Annales de Limnologie—International Journal of Limnology* 40, no. 1: 1-9. https://doi.org/10.1051/limn/2004003.

Vitecek, S., G. Vinçon, W. Graf, and S. Pauls. 2017. "High Cryptic Diversity in Aquatic Insects: An Integrative Approach to Study the Enigmatic *Leuctra inermis* Species Group (Plecoptera)." *Arthropod Systematics & Phylogeny* 75, no. 3: 497-521. https://doi.org/10.3897/asp.75.e31919.

Vivien, R., L. Apothéloz-Perret-Gentil, J. Pawlowski, I. Werner, and B. J. D. Ferrari. 2019. "Testing Different (e)DNA Metabarcoding Approaches to Assess Aquatic Oligochaete Diversity and the Biological Quality of Sediments." *Ecological Indicators* 106: 105453. https://doi.org/10.1016/j.ecolind.2019.105453.

Vörösmarty, C. J., P. B. McIntyre, M. O. Gessner, et al. 2010. "Global Threats to Human Water Security and River Biodiversity." *Nature* 467, no. 7315: 555-561. https://doi.org/10.1038/nature09440.

Wagner, M., and J.-C. Raymond. 2022. *Etat initial avant travaux*, 40. Office Française de la Biodiversité.

Wang, Q., G. M. Garrity, J. M. Tiedje, and J. R. Cole. 2007. "Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences Into the New Bacterial Taxonomy." *Applied and Environmental Microbiology* 73, no. 16: 5261-5267. https://doi.org/10.1128/AEM.00062-07.

Weigand, H., A. J. Beermann, F. Čiampor, et al. 2019. "DNA Barcode Reference Libraries for the Monitoring of Aquatic Biota in Europe: Gap-Analysis and Recommendations for Future Work." *Science of the Total Environment* 678: 499-524. https://doi.org/10.1016/j.scitotenv.2019.04.247.

Wheeler, Q. D., P. H. Raven, and E. O. Wilson. 2004. "Taxonomy: Impediment or Expedient?" *Science* 303, no. 5656: 285. https://doi.org/10.1126/science.303.5656.285.

Wickham, H. 2009. *ggplot2: Elegant Graphics for Data Analysis*. Springer. https://doi.org/10.1007/978-0-387-98141-3.

Winking, C., A. W. Lorenz, B. Sures, and D. Hering. 2014. "Recolonisation Patterns of Benthic Invertebrates: A Field Investigation of Restored Former Sewage Channels." *Freshwater Biology* 59, no. 9: 1932-1944. https://doi.org/10.1111/fwb.12397.

Zeng, C., Y. Wen, X. Liu, J. Yu, B. Jin, and D. Li. 2022. "Impact of Anthropogenic Activities on Changes of Ichthyofauna in the Middle and Lower Xiang River." *Aquaculture and Fisheries* 7, no. 6: 693-702. https://doi.org/10.1016/j.aaf.2021.06.007.

Zhou, X., Y. Li, S. Liu, et al. 2013. "Ultra-Deep Sequencing Enables High-Fidelity Recovery of Biodiversity for Bulk Arthropod Samples Without PCR Amplification." *GigaScience* 2, no. 1: 4. https://doi.org/10.1186/2047-217X-2-4.

## Supporting Information

Additional supporting information can be found online in the Supporting Information section.