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\begin{document}

\title{Environmental DNA metabarcoding uncovers environmental correlates of
fish communities in spatially heterogeneous freshwater habitats}



\author[1]{Petr Blabolil}%
\author[2]{Lynsey Harper}%
\author[1]{Stepanka Ricanova}%
\author[2]{Graham Sellers}%
\author[2]{Cristina  Di Muri}%
\author[1]{Tomáš Jůza}%
\author[1]{Mojmír Vašek}%
\author[1]{Zuzana Sajdlová}%
\author[1]{Pavel Rychtecký}%
\author[1]{Petr Znachor}%
\author[1]{Josef Hejzlar}%
\author[1]{Jiří Peterka}%
\author[2]{Bernd Haenfling}%
\affil[1]{Biology Centre CAS, Institute of Hydrobiology}%
\affil[2]{University of Hull}%


\vspace{-1em}



  
  \date{June 11, 2024}


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\selectlanguage{english}
\begin{abstract}
Biomonitoring of complex heterogeneous environments is highly
challenging. Fish in deep water bodies occupy different habitats,
therefore a combination of survey methods has traditionally been used.
Environmental DNA (eDNA) metabarcoding is a novel monitoring tool that
can overcome spatial heterogeneity in a highly sensitive and entirely
non-invasive manner. However, taxon detection probability is dependent
on real-time environmental variables. In this study, three reservoirs
were sampled in two seasons using a spatiotemporally distributed
sampling design covering major environmental gradients. In all sampling
campaigns, 31 fish taxa were detected which exceeded expectations. Data
reliability was confirmed by a tight positive correlation between
individual taxon scores derived from gillnet sampling and eDNA site
occupancy. Analyses confirmed anticipated trends, such as the highest
number of taxa were observed in the largest water body, and more taxa
were detected in inflows and littoral regions compared to open water.
The most important factors for fish distribution were temperature, age
and trophic status (expressed as total Chlorophyll a concentration) of
water bodies. Taxon detection reflected ecological niches of individual
species, e.g. warm water wels catfish (Silurus glanis) and cold water
salmonids. This study provides further evidence that eDNA metabarcoding
is suitable for ecological study in heterogeneous environments and may
substitute conventional fish sampling techniques.%
\end{abstract}%



\sloppy


\textbf{Introduction}

High quality water sources are essential for ecosystem service
provisioning under global change, including climate and pollution (Baron
et al., 2002). One effective way to maintain high quality water sources
is water storage in reservoirs that have been created for drinking,
agriculture and industrial purposes, flood protection, recreation, and
hydropower generation among others (Labadie 2004). Despite the key
importance of reservoirs, research has typically focused on natural
water bodies whereas artificial or heavily modified water bodies are
often neglected (Stra\selectlanguage{polish}š\selectlanguage{english}kraba 2005). Deep reservoirs are prime, widespread
examples of aquatic heterogeneous ecosystems with hydrological
characteristics that are intermediate between lakes and rivers, and
include lacustrine, transitional and riverine components (Wetzel 2001).

Deep temperate reservoirs are hydrologically complex environments where
seasonal variation in temperature and flow conditions affects
stratification and mixing patterns of the water column. River inflows
along with an intricate outflow dam system further modify reservoir
hydrodynamics and often result in currents and intrusion of water to
deeper strata (Ford, 1990, Stra\selectlanguage{polish}š\selectlanguage{english}krabov\selectlanguage{ngerman}á et al., 1994). In summer, stable
thermal stratification occurs with higher temperature (\textgreater{}20
°C) in the epilimnion and lower temperature (4 °C) associated with
higher water density in the hypolimnion. Summer stratification is
followed by autumn mixing of the entire water column, with gradual
cooling to temperatures around or below 4 °C. During winter, the
hypolimnion contains water of the highest density with temperature close
to 4 °C, and surface water has lower density with possible ice coverage.
The stability of the water column in winter depends on weather
conditions (e.g. ice cover, wind, precipitation) and water level
manipulations. In canyon-shaped reservoirs, spatial heterogeneity is
also associated with longitudinal gradients (Thornton et al., 1990).
Nutrient rich tributaries support phytoplankton production, which
decreases towards the dam (Rychtecký \& Znachor, 2011). A transition
between the littoral and pelagic zones represents an additional source
of reservoir spatial heterogeneity as the littoral zone differs in many
aspects from more homogeneous pelagic habitats (\selectlanguage{polish}Ř\selectlanguage{ngerman}íha et al., 2015).
Littoral habitats can be complex with macrophyte, rock and root
configurations, or simple (e.g. sandy or muddy beaches), creating
diverse environments affecting species composition, abundance and
spatial distribution of the whole trophic web, from phyto- and
zooplankton to fish (Prchalová et al., 2009a).

Fish have both direct and indirect effects on all trophic levels and are
well-adapted to the various environmental conditions of heterogeneous
temperate freshwaters. Therefore, they are often used as ecological
indicators (EC, 2000). The long lifespan of many species also means that
fish communities reflect changes over decadal timescales (Blabolil, et
al., 2017b). Reliable assessment of fish community composition in
reservoirs has typically required a combination of various sampling
methods. However, established sampling methods are relatively laborious,
expensive, sometimes destructive, taxonomically biased, and their
application is case-specific, thus these limitations often result in
poor accuracy and precision (Kube\selectlanguage{polish}č\selectlanguage{english}ka et al., 2009). In Europe,
gillnetting is commonly used to assess fish populations, but this type
of fish survey is extremely invasive (Blabolil, et al., 2017a).
Therefore, efforts should be invested in the implementation of
less-invasive and more universal monitoring strategies. Traditionally,
visual census and hydroacoustics have been used for non-invasive
surveys, but both have limitations. The accuracy of visual census
depends on water clarity (Holubov\selectlanguage{ngerman}á et al., 2019), whereas hydroacoustics
application is constrained by water depth and results do not provide
species composition (Baran et al., 2017). Recently, environmental DNA
(eDNA) metabarcoding has come to the fore as an innovative method for
aquatic biodiversity biomonitoring (Taberlet et al., 2018).

eDNA refers to genetic material released by organisms into their
environment, which can be captured and analyzed to enable non-invasive,
highly sensitive species detection (Taberlet et al., 2018). Aquatic
ecosystems are particularly suited to eDNA analysis as water is an
effective medium for deposition and transport of intracellular or
extracellular organismal DNA from aquatic, semi-aquatic and terrestrial
organisms (Deiner et al., 2016; Harper et al., 2019b). All water bodies
can be easily sampled to collect eDNA, including temporary pools
(Bylemans et al., 2019), rivers (Deiner et al., 2016), ponds (Harper et
al., 2019a), large heterogeneous lakes (Lawson Handley et al., 2019) and
marine environments (Djurhuus et al., 2020). eDNA metabarcoding is an
especially powerful and cost-efficient approach for biomonitoring as it
allows whole communities to be screened (Hering et al., 2018). During
metabarcoding, eDNA is PCR-amplified using broad-range primers and
sequenced on a high-throughput sequencing platform (Deiner et al.,
2017). However, species composition and distribution inferred from eDNA
metabarcoding must account for environmental conditions that influence
eDNA dynamics. Various factors affect eDNA persistence and degradation,
such as temperature, ultraviolet (UV) light intensity, and oxygen
concentration, that in turn determine microbial action and metabolic
activity of aquatic organisms (Barnes et al., 2014). The spatial
distribution of eDNA in a given water body can be influenced by species'
habitat preferences and activity (de Souza et al., 2016), oxygen and
trophic conditions (Lawson Handley et al., 2019), or water movements
caused by inflows and outflows, precipitation, wind, convective currents
etc. (Jeunen et al., 2019). Background data characterizing environmental
conditions and their possible links to eDNA dynamics are desperately
needed, but are often not provided in published studies (Nicholson et
al., 2020; Ruppert et al., 2019).

The aim of our study was to use eDNA metabarcoding to assess the fish
communities in three Czech Republic reservoirs across two seasons. We
compared species composition at multiple sites characterizing the
spatial gradient in each of the reservoirs. We also tested the
relationship between eDNA site occupancy and scores derived from
conventional sampling for each taxon. Finally, we analyzed the
relationship between species diversity and reservoir spatial
heterogeneity.

\textbf{Materials and Methods}

\emph{Study sites}

We selected three reservoirs in the Czech Republic of different trophic
status (from eutrophic \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov to oligotrophic Klí\selectlanguage{polish}č\selectlanguage{english}ava), size and
hydrodynamics (Table 1). All reservoirs possess canyon-shape morphology
with one main inflow (Figure 1), resulting in longitudinal gradients of
nutrients and water quality from the inflow to the dam. In summer, the
water column is thermally stratified followed by water mixing in late
autumn. All studied reservoirs serve as drinking water storages and are
therefore restricted to public access. Fish communities are dominated by
cyprinids (mainly bleak {[}\emph{Alburnus alburnus} {]}, freshwater
bream {[}\emph{Abramis brama} {]}, roach {[}\emph{Rutilus rutilus} {]})
in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov and \selectlanguage{polish}Ž\selectlanguage{english}lutice, and by European perch (\emph{Perca fluviatilis} )
in Kl\selectlanguage{ngerman}í\selectlanguage{polish}č\selectlanguage{english}ava despite regular stocking of piscivorous species, i.e. asp
(\emph{Leuciscus aspius} ), Northern pike (\emph{Esox lucius} ),
pikeperch (\emph{Sander lucioperca} ), wels catfish (\emph{Silurus
glanis} ) (Va\selectlanguage{polish}š\selectlanguage{english}ek et al., 2013).

\emph{eDNA sampling and environmental parameters}

In order to cover the expected spatial heterogeneity within reservoirs,
samples were collected at intervals of approximately 1 km along the
river channel, hereafter referred to as ``localities''. Based on the
length of the river basin, five localities were sampled in Kl\selectlanguage{ngerman}í\selectlanguage{polish}č\selectlanguage{english}ava and
\selectlanguage{polish}Ž\selectlanguage{english}lutice, and eight localities were sampled in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov. In each reservoir,
one more locality was sampled in a side basin (Figure 1). At each
locality, samples were collected at both banks in the littoral zone and
from the surface layer of the pelagic zone, approximately halfway
between the two littoral samples. Additional deep water samples were
collected along a vertical transect 5 m, 10 m and 20 m at all pelagic
sites in Klí\selectlanguage{polish}č\selectlanguage{english}ava and \selectlanguage{polish}Ž\selectlanguage{english}lutice, and at localities 1, 4, 6 and the side
basin in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov. The number of pelagic samples taken depended on water
depth at each site (Figure 1). A single sample each was collected from
the inflows to the main reservoirs and the side basins respectively.
Littoral, pelagic (depth separated), and inflow samples are hereafter
referred to as ``habitats''. Each site was sampled twice, with sampling
campaigns in the summer and late autumn of 2018 (Table 2). During the
Klí\selectlanguage{polish}č\selectlanguage{english}ava and \selectlanguage{polish}Ž\selectlanguage{english}lutice summer campaigns, samples from 5 m depth in the bay
were not collected. During the late autumn campaign in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov, locality 8
(closest to the main tributary) was covered with ice preventing water
sample collection. The total number of samples collected in summer/late
autumn was 29/30 in Klí\selectlanguage{polish}č\selectlanguage{english}ava, 38/35 in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov and 28/29 in \selectlanguage{polish}Ž\selectlanguage{english}lutice.

At each locality, 2 L of water was sampled by pooling five 400 mL
subsamples taken within 50 m of the locality. A Friedinger sampler
(Karel \selectlanguage{polish}Š\selectlanguage{english}ramhauser -- Plastick\selectlanguage{ngerman}é hmoty, CZ) was used for water collection.
To avoid excessive seston, the water samples were pre-filtered in the
field with a 40 \selectlanguage{greek}µ\selectlanguage{english}m sterile plankton net into sterile plastic bottles
that were placed in an isolated box with ice for storage at
approximately 4 \selectlanguage{ngerman}°C until further processing. Sampling equipment was
sterilized by soaking for 10 min in 10\% commercial bleach solution
(containing 5\% sodium hypochlorite), followed by soaking for 10 min in
10\% microsol detergent solution and rinsing with purified water between
localities. The sampler was then rinsed again in reservoir water at the
next locality. After water sample collection, field blanks (0.5 L
purified water per sampling campaign) were passed through the sampler,
then passed through a sterile plankton net back into the storage bottle.

Meteorological conditions (i.e. air temperature, wind speed and cloud
cover), water inflow and water level were measured during each sampling
event by a floating tower (Aquaread, UK) with multiparametric sensors
(Fiedler, CZ) in the dam section of each reservoir. At each pelagic
locality, vertical profiles of water temperature and concentration of
dissolved oxygen (DO) were measured using a multiparameter probe YSI EXO
II (YSI, USA). Chlorophyll \emph{a} (ChlA) concentrations of four
phytoplankton groups (i.e. green algae, diatoms, cyanobacteria and
cryptomonads) were measured by the bbe Moldaenke FluoroProbe GmbH.

The field sampling and experimental protocols were performed in
accordance with the guidelines and permission from the Experimental
Animal Welfare Commission under the Ministry of Agriculture of the Czech
Republic (Ref. No. CZ 01679) and the experimental project was approved
by the Ministry of Environment of the Czech Republic (Ref. No.
MZP/2019/630/16). All methods were approved by the Experimental Animal
Welfare Commission of Biology Centre of the Czech Academy of Sciences.

\emph{eDNA capture and extraction}

All samples were filtered within 24 hours of collection in a
decontaminated laboratory (bleached floors and surfaces) that is not
used for handling fish specimens or DNA. We filtered 1 L of each sample
(2 x 0.5 L) through two sterile mixed cellulose acetate and cellulose
nitrate filters (47 mm diameter, 0.45 \selectlanguage{greek}µ\selectlanguage{english}m porosity, Whatman, UK) using
Nalgene filtration units in combination with a vacuum pump. The filters
were folded using sterile tweezers, placed in 1.5 mL Eppendorf tubes
filled with 96\% ethanol, and stored in the dark at 4 \selectlanguage{ngerman}°C. Filtration
equipment was sterilized in 10\% commercial bleach solution for 10 min,
followed by soaking for 10 min in 10\% microsol solution, and rinsed
with purified water after each filtration round. Field blanks also
served as filtration blanks, that were run before the first filtration
round, during filtration, and after the last filtration round to monitor
potential contamination.

In March 2019, the filters in ethanol were transported at room
temperature (six days) to a dedicated eDNA facility at the University of
Hull. In a UV and bleach sterilized laminar flow hood, filters were
removed from ethanol onto individual sterile Petri dishes to dry for
approximately 30 min. Dry filters from the same sample were transferred
to a 5 mL screw cap tube (Axygen\selectlanguage{english}, Fisher Scientific, UK). The storage
ethanol was transferred to a sterile 1.5 mL Eppendorf tube and
centrifuged for 40 min at 11,000 x g. The supernatant was discarded and
the dry pellet dissolved in 50 \selectlanguage{greek}µ\selectlanguage{english}L molecular grade water. The eluate was
added to the appropriate 5 mL screw cap tube containing dry filters. For
each sampling campaign, two sterile filters washed with 99\% ethanol
were processed alongside sample filters to act as evaporation blanks and
monitor possible contamination. All dried filters were stored at -20 \selectlanguage{ngerman}°C
for one week until DNA extraction.

DNA was extracted following the Mu-DNA water protocol (Sellers et al.,
2018). A subset of samples were initially extracted to test extraction
protocol efficacy. Based on relatively low DNA concentrations (average
7.7 ng/\selectlanguage{greek}µ\selectlanguage{english}L) measured by NanoDrop 1000 Spectrophotometer, the original
protocol was modified to increase DNA yield. The volumes of lysis
solution and water lysis additive were increased to 900 \selectlanguage{greek}µ\selectlanguage{english}L and 300 \selectlanguage{greek}µ\selectlanguage{english}L
respectively. To enhance lysate yield, the centrifugation step was
extended to 2 min, and all lysate was transferred to a sterile tube with
flocculant solution for the inhibitor removal step. Finally, the eluate
was transferred back onto the spin column membrane, incubated at room
temperature, and centrifuged to increase DNA yield. To minimize
contamination risk between reservoirs, DNA extraction was performed
separately for each sampling campaign. DNA was extracted from all
filters, field/filtration blanks, evaporation blanks, and extraction
blanks (i.e. extraction buffers only) for each sampling campaign. An
extraction blank was included for each batch of extractions (two per
sampling campaign), that were subsequently pooled to provide one
extraction blank for each sampling campaign. Extracted DNA was stored at
-20 \selectlanguage{ngerman}°C for one week until PCR amplification.

\emph{eDNA metabarcoding}

The sequencing library was generated from PCR amplicons produced using
the primer pair 12S-V5-F (5´-ACTGGGATTAGATACCCC-3´) and 12S-V5-R
(5´-TAGAACAGGCTCCTCTAG-3´), which amplifies a 73-110 bp fragment of the
mitochondrial 12S ribosomal RNA (rRNA) gene in vertebrates (Riaz et al.,
2011), and a \textasciitilde{}106 bp fragment in European fishes
(Hänfling et al., 2016). All PCRs were set up using eight-strip PCR
tubes in a UV and bleach sterilized laminar flow hood in the eDNA
laboratory to minimize contamination risk. To detect possible PCR
contamination within each sub-library, we included PCR negative controls
(molecular grade water) and PCR positive controls. The latter used
tissue DNA (0.05 ng/\selectlanguage{greek}µ\selectlanguage{english}L) from Zebra mbuna (\emph{Maylandia zebra} ), a
cichlid from Lake Malawi which is not present in natural waters in the
Czech Republic. The locus primers were attached to 24 unique index
combinations and heterogeneity spacers for the first PCR. The first PCR
for each sample, field/filtration blank, evaporation blank, extraction
blank, PCR negative control and PCR positive control was performed in 25
\selectlanguage{greek}µ\selectlanguage{english}L reaction volumes, including 12.5 \selectlanguage{greek}µ\selectlanguage{english}L
Q5\selectlanguage{ngerman}\textsuperscript{®[?]}\selectlanguage{english}High-Fidelity 2X Master Mix (New England
Biolabs\textsuperscript{(r)}Inc., MA, USA), 1.5 \selectlanguage{greek}µ\selectlanguage{english}L of each primer (10 \selectlanguage{greek}µ\selectlanguage{english}M,
Integrated DNA Technologies), 0.5 \selectlanguage{greek}µ\selectlanguage{english}L Thermo Scientific Bovine Serum
Albumin (Fisher Scientific, UK), 5 \selectlanguage{greek}µ\selectlanguage{english}L DNA, and 4 \selectlanguage{greek}µ\selectlanguage{english}L molecular grade
water. PCR conditions were initial denaturation for 5 min at 98 \selectlanguage{ngerman}°C, 35
cycles of 10 s at 98 °C, 20 s at 58 °C and 30 s at 72 °C, and final
extension for 7 min at 72 °C. Three technical replicates were performed
for each sample and then pooled to minimize PCR stochasticity. Pooled
PCR products were visualized on a 2\% agarose gel stained with GelRed
(Cambridge Bioscience, UK). PCR products were normalized into 12
sub-libraries (two per sampling campaign) based on the band strength
observed for each sample (very bright = 5 \selectlanguage{greek}µ\selectlanguage{english}L, bright = 10 \selectlanguage{greek}µ\selectlanguage{english}L, faint = 15
\selectlanguage{greek}µ\selectlanguage{english}L, and very faint/no band = 20 \selectlanguage{greek}µ\selectlanguage{english}L) together with 10 \selectlanguage{greek}µ\selectlanguage{english}L of the
filtration blanks, evaporation blanks, extraction blanks, PCR negative
controls and 1 \selectlanguage{greek}µ\selectlanguage{english}L of the PCR positive controls (Alberdi et al., 2018).
The normalized sub-libraries were purified using a double size selection
protocol with Mag-Bind\selectlanguage{ngerman}® TotalPure NGS magnetic beads (Omega Bio-tek,
USA) to remove primer dimers and large secondary products (Bronner et
al., 2014). Ratios of 0.9× and 0.15× magnetic beads to 100 \selectlanguage{greek}μ\selectlanguage{english}L of each
sub-library were used.

The 12 purified sub-libraries were used as template DNA for a second PCR
attaching Illumina indices. The second PCR was performed in 50 \selectlanguage{greek}µ\selectlanguage{english}L
reaction volumes, consisting of 25 \selectlanguage{greek}µ\selectlanguage{english}L
Q5\selectlanguage{ngerman}\textsuperscript{®[?]}\selectlanguage{english}High-Fidelity 2X Master Mix, 3 \selectlanguage{greek}µ\selectlanguage{english}L of each primer
(10 \selectlanguage{greek}µ\selectlanguage{english}M), 4 \selectlanguage{greek}µ\selectlanguage{english}L purified product, and 15 \selectlanguage{greek}µ\selectlanguage{english}L molecular grade water. PCR
conditions were initial denaturation for 3 min at 95 \selectlanguage{ngerman}°C, 10 cycles of 20
s at 98 °C and 1 min at 72 °C, and final extension for 5 min at 72 °C.
PCR was performed for sub-libraries in duplicate and replicates were
pooled before visualization on a 2\% agarose gel stained with GelRed.
The sub-libraries were purified using a double size selection protocol
with magnetic beads to remove secondary products (Bronner et al., 2014).
Ratios of 0.7× and 0.15× magnetic beads to 50 \selectlanguage{greek}μ\selectlanguage{english}L of each sub-library
were used. The sub-libraries were normalized and pooled according to the
number of samples each contained (without blanks and PCR negative
controls) and their DNA concentration. The pooled library was purified
again using the same volumes and ratios as the previous purification,
and quantified by qPCR using the NEBNext\selectlanguage{ngerman}\textsuperscript{®} Library
Quant Kit for Illumina\textsuperscript{®} (New England
Biolabs\textsuperscript{®} Inc., MA, USA) on a StepOnePlus\selectlanguage{english} Real-Time
PCR System (Thermo Fisher Scientific, UK) following manufacturer's
guidelines. Removal of secondary product was verified using an Agilent
2200 TapeStation and High Sensitivity D1000 ScreenTape (Agilent
Technologies, CA, USA). Libraries were run at 13 pM with 10\% PhiX
Control v3 on an Illumina MiSeq\textsuperscript{(r)} using a MiSeq Reagent
Kit v3 (600-cycle) (Illumina, Inc., CA, USA).

\emph{Bioinformatics analysis}

Raw sequence reads from the Illumina MiSeq were evaluated using FASTQC
(http:// www.bioinformatics.babraham.ac.uk/projects/fastqc). Raw
sequence reads were demultiplexed using a custom Python script before
bioinformatic processing using metaBEAT v0.97.11
(https://github.com/HullUnibioinformatics/metaBEAT), which is a custom
pipeline that incorporates commonly used open source software. The
program Trimmomatic 0.32 (Bolger et al., 2014) was used for quality
trimming and removal of locus primers from the raw sequence reads.
Average read quality was assessed in 5-bp sliding windows starting from
the 3' of the read, and reads were clipped until the average quality per
window was above phred 30. All reads shorter than a defined minimum 90
bp read length were discarded. Sequence pairs were subsequently merged
into single high-quality reads using the program FLASH 1.2.11 (Mago\selectlanguage{polish}č \selectlanguage{english}\&
Salzberg, 2011). Reads surviving quality filtering and trimming were
screened for chimeric sequences against a custom, curated reference
database using the \emph{uchime\_ref} function implemented in vsearch
1.1 (https://github.com/torognes/vsearch). The reference database was
developed at the University of Hull (H\selectlanguage{ngerman}änfling et al., 2016) and
supplemented with asp (GenBank accession numbers: MT163435, MT163450,
MT163449) and marena whitefish (\emph{Coregonus maraena} ) (GenBank
accession numbers: MT163451, MT163458, MT163460) to represent all fish
species in the study catchment. Sequences were clustered at 100\%
identity using VSEARCH v1.1. Clusters represented by less than three
sequences were considered sequencing error and were omitted from further
analysis. Nonredundant sets of query sequences were then compared to the
reference database using BLAST (Zhang et al., 2000). BLAST output was
interpreted using a custom python function, which implements a lowest
common ancestor approach for taxonomic assignment, similar to the
strategy used by MEGAN 5.10.6 (Huson et al., 2007). BLAST hits were only
considered if they possessed a minimum identity of 99\% and 90\% query
coverage.

\emph{Data analysis}

A false positive sequence threshold (0.1\%) was applied to the
metabarcoding inventory, where taxa recorded within each sample were
discarded if their read frequencies fell below this threshold (Hänfling
et al., 2016; Lawson Handley at al., 2019). The metabarcoding inventory
was then checked for spurious taxa (i.e. taxa that do not occur in the
Czech waters or occurred in very few samples), and taxonomic assignments
were corrected. Capelin (\emph{Mallotus villosus} ) was found in one
reservoir sample and thus removed from downstream analyses. A number of
species level taxonomic assignments lacked credibility and were
reassigned to genus level. Sturgeon (\emph{Acipenser sturio} ) was
changed to \emph{Acipenser} spp. as Siberian sturgeon (\emph{A. baerii}
) and Danube sturgeon (\emph{A. gueldenstaedtii} ) were previously
captured by conventional methods but these species were missing from our
reference database. Vendace (\emph{Coregonus albula} ) was corrected
to\emph{Coregonus} spp. as maraena whitefish and peled (\emph{C. peled}
) are more often stocked in Czech waters. Similarly, Arctic char
(\emph{Salvelinus alpinus} ) was corrected to \emph{Salvelinus} spp. as
the closely related brook trout (\emph{S. fontinalis} ) is more often
stocked in Czech waters, and these species cannot be distinguished using
the target 12S rRNA fragment. Silver carp (\emph{Hypophthalmichthys
molitrix} ) and bighead carp (\emph{H. nobilis} ) were merged
to\emph{Hypophthalmichthys} spp. as both species are artificially
selected, resulting in hybrids, and share the same ecological niches.
Finally, river lamprey (\emph{Lampetra fluviatilis} ) was corrected
to\emph{Lampetra} spp. because river lamprey is not present in the
studied catchments and sequences likely belong to European brook lamprey
(\emph{L. planeri} ) which does not possess reference sequences. Four
species that occur in the studied reservoirs were assigned to family
level as they could not be distinguished using the target 12S rRNA
fragment. Asp and rudd (\emph{Scardinius erythrophthalmus} ) reference
sequences were identical and assignments therefore corrected to
Cyprinidae, and European perch and pikeperch were identical and
corrected to Percidae. One species, Burbot (\emph{Lota lota} ), was
completely missed by eDNA metabarcoding having been previously detected
in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov with gillnetting. Excluding Cyprinidae and Percidae, higher
taxonomic assignments (i.e. families, including Salmonidae, or higher
ranks) were excluded from downstream analyses due to uncertainty and
differing species' ecologies.

Remaining taxa were classified as non-native or according to the
International Union for Conservation of Nature (IUCN) Red List
categories (Lusk et al., 2017; Pergl et al., 2016). For each reservoir,
five abundance categories were created (Table 3) based on previous
ichthyological surveys using benthic and pelagic gillnets during the
last 15 years (e.g. Blabolil, et al., 2017a). Species presence was also
assessed by seining, electrofishing, fyke-nets, trawling (J\selectlanguage{polish}ů\selectlanguage{english}za et al.,
2015; \selectlanguage{polish}Ř\selectlanguage{ngerman}íha et al., 2015), and expert judgment of FishEcU members
(www.fishecu.cz).

Data on inflow, meteorological conditions and reservoir operation were
used as input variables in the CE-QUAL-W2 model (Wells, 2019) to compute
hydrodynamics, age of water along the longitudinal and vertical profile
of reservoirs (i.e. a model variable that is used to estimate the
residence time, which accumulates at a rate of 1.0
d\textsuperscript{-1}with all new water entering the system having an
age of zero, Monsen et al., 2002) and real-time environmental parameters
during sampling campaigns. Measured water parameters were plotted using
Surfer software (Surfer 9, Golden Software Inc.). The ChlA concentration
data were log-transformed to achieve a normal data distribution. The
water age data were normalized within the range from 0--1 for each
reservoir to evaluate the proportion of fresh inflow water within each
reservoir locality.

Site occupancy and regression analyses were conducted in R v.4.0.0 (R
Development Core Team, 2020). We calculated sample-based site occupancy
estimates for each taxon (i.e. number of positive samples divided by
total number of samples, without inflows) as a proxy for relative
abundance in the main reservoir body. A Generalized Linear Mixed-effects
Model (GLMM) with reservoir identity as a random effect was applied to
test the difference in the number of taxa between the main and side
tributaries and in the main reservoir. A second GLMM with reservoir
identity as a random effect was used to test the difference in the
number of taxa between localities, habitats and seasons. Bray--Curtis
dissimilarity for samples taken in the same reservoir, season and
habitats was computed using the \emph{vegdist} function in the package
vegan (Oksanen et al., 2019). Differences in Bray--Curtis dissimilarity
between sites were compared using a GLMM with habitat and sampling
season as fixed effects and reservoir identity as a random effect.
Spearman's rank correlation coefficient was used to test the
relationship between eDNA-based site occupancy and the taxon score
derived from gillnet sampling. Differences in the number of taxa for
each reservoir between season and correlation coefficients for different
seasons were compared using a paired t-test.

Multivariate analyses were performed in CANOCO 5 software (\selectlanguage{polish}Š\selectlanguage{english}milauer \&
Lep\selectlanguage{polish}š, \selectlanguage{english}2014). We tested whether fish communities in each reservoir
(without inflows) were influenced by environmental variables using a
Constrained Canonical Correlation Analysis (CCA). Relationships between
fish taxa in the main reservoir body and temperature, DO concentration,
total ChlA concentration, normalized water age, locality and habitat
were tested using a CCA Interactive Forward Selection with the
covariable reservoir identity while restricting the permutations.
Log-transformed read counts were supplied to a Monte-Carlo permutation
test with 9999 permutations using a test of significance on all
constrained axes.

\textbf{Results}

\emph{Environmental parameters}

In summer, precipitation and inflow were lower than in late autumn
(Table 2). The air temperature was consistent with the temperate region,
wind was low and cloud cover variable with no or limited precipitation
in summer. In autumn, low precipitation was observed before the sampling
campaigns (Table 2). The water level decreased by approximately 1.0 m in
Kl\selectlanguage{ngerman}í\selectlanguage{polish}č\selectlanguage{english}ava and \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov and by 2.2 m in \selectlanguage{polish}Ž\selectlanguage{english}lutice between the summer and autumn
sampling campaigns (Table 2). In all reservoirs, stable thermal
stratification was established in summer when the surface water
temperature exceeded 20 \selectlanguage{ngerman}°C. In late autumn, surface water temperature
was on average 6 °C and thermal stratification eroded (Supplementary
Figure 1). The vertical distribution profiles of oxygen during summer
differed between the three reservoirs. In Klí\selectlanguage{polish}č\selectlanguage{english}ava, a positive
heterograde profile with a maximum oxygen concentration in the
metalimnion (middle layer) and anoxic hypolimnion (deep layer) was
observed. In contrast, a negative heterograde profile with a
metalimnetic minimum and anoxic deep layers characterized oxygen
stratification in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov. In \selectlanguage{polish}Ž\selectlanguage{english}lutice, a clinograde oxygen profile with
the maximum in the epilimnion (surface layer) and anoxic deep strata was
observed (Supplementary Figure 2). In late autumn, the hypolimnetic
layers remained anoxic in Kl\selectlanguage{ngerman}í\selectlanguage{polish}č\selectlanguage{english}ava and \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov, where weak thermal
stratification persisted. Klí\selectlanguage{polish}č\selectlanguage{english}ava had the lowest ChlA concentration with
summer maxima in the metalimnion (Supplementary Figure 3). In the other
two reservoirs, inflowing nutrients resulted in high summer ChlA
concentrations in the upstream parts (Supplementary Figure 3). The
longest water age was in Kl\selectlanguage{ngerman}í\selectlanguage{polish}č\selectlanguage{english}ava (>\selectlanguage{english}2 years), with little water exchange
and complete mixing of inflow within the reservoir in the tributary zone
(Supplementary Figure 4). The shortest water age was in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov, where
river water was directed above the metalimnetic layer and mixed with the
epilimnion in summer, but in autumn river water was vertically mixed
into the whole water column supplying flowing fresh water along the
longitudinal reservoir profile (Supplementary Figure 4). The water age
in \selectlanguage{polish}Ž\selectlanguage{english}lutice shows that the inflow mixed extensively with the epilimnetic
layer in summer and the reservoir was mostly homogeneous in autumn
(Supplementary Figure 4).

\emph{eDNA metabarcoding}

The 12S library generated 22.46 million raw sequence reads, with 13.51
million reads passing bioinformatic filters. After quality filtering and
removal of chimeric sequences, each reservoir sample contained 63,593
target reads (i.e. fish) on average. Zebra mbuna was the only fish
species detected in PCR positive controls. In negative controls
(field/filtration blanks, evaporation blanks, extraction blanks, and PCR
negative controls), no fish species were detected.

\emph{Diversity and site occupancy of fish communities}

Altogether 30 bony fish taxa and one lamprey were identified in the 189
reservoir samples (Table 4, Figure 2). Across the dataset (i.e. all
reservoirs and seasons), no fish were detected in nine pelagic and two
littoral samples. Six taxa were detected in all reservoirs and seasons,
and another 14 taxa were consistently detected in the same reservoir in
summer and autumn. Conversely, six taxa were only detected in one
sampling campaign (Table 4). Seven taxa were categorised as non-native
and others were sorted in six IUCN categories from Extinct, Critically
Endangered, and Vulnerable, to Least Concern (Table 4).

\selectlanguage{polish}Ř\selectlanguage{ngerman}ímov possessed the most taxa whereas Klí\selectlanguage{polish}č\selectlanguage{english}ava had the lowest number of
taxa (Figure 3). In all reservoirs, significantly more taxa were
detected in late autumn compared to summer (t\textsubscript{2} = 12.124,
p = 0.007) (Figure 3). The Spearman's rank correlation coefficient
between site occupancy and gillnet score for each taxon was
statistically significant for all sampling campaigns (Figure 5, p <
\selectlanguage{english}0.05). The rho correlation coefficients were higher in summer than in
late autumn, but this was not statistically significant due to low
sample size (t\textsubscript{2} = 1.981, p = 0.186).

More taxa per sample were detected in the main inflows than in the side
tributaries (GLMM: F\textsubscript{3,6} = 6.458, p = 0.026) and in the
main reservoirs (GLMM: F\textsubscript{3,177} = 11.918, p < \selectlanguage{english}0.001).
Differences in the number of taxa between samples from the reservoirs
and the side tributaries were not significant (GLMM:
F\textsubscript{3,177} = 0.327, p = 0.806) (Figure 3). In the main body
of the reservoirs, the number of taxa was significantly different
between localities showing an increasing trend from the dam to the
tributary (GLMM: F\textsubscript{3,171} = 14.473, p < \selectlanguage{english}0.001). Moreover,
the in-reservoir habitats differed in the number of taxa with highest
diversity in the littoral zone (GLMM: F\textsubscript{12,162} = 2.128, p
= 0.017) and without a significant seasonal effect (GLMM:
F\textsubscript{3,171} = 0.946, p = 0.420) (Figure 2, 3). The
Bray--Curtis dissimilarity indices were significantly different between
habitats (GLMM: F\textsubscript{5,723} = 21.104, p < \selectlanguage{english}0.001) and seasons
(GLMM: F\textsubscript{1,722} = 27.689, p < \selectlanguage{english}0.001) (Figure 4).

\emph{Environmental correlates of fish distribution}

Temperature (whole CCA test: 4.4 \% of explained variability, pseudo-F =
7.0, p \textless{} 0.001), normalized water age (whole CCA test: 3.7 \%
of explained variability, pseudo-F = 6.0, p \textless{} 0.001) and ChlA
(whole CCA test: 1.2 \% of explained variability, pseudo-F = 1.9, p
\textless{} 0.001) were significant environmental variables selected by
Interactive Forward Selection procedure (Figure 6a, b). The first axis
explained 7.07\% of the variation, whereas the second and third axes
explained 1.53\% and 0.65\% of the variation, respectively. Based on the
ordination score, the first axis is mostly correlated with temperature,
where some taxa prefer high temperature (e.g. pumpkinseed
{[}\emph{Lepomis gibbosus} {]}, wels catfish), some taxa are flexible
(e.g. bleak, freshwater bream, white bream {[}\emph{Blicca bjoerkna}
{]}, roach, ruffe {[}\emph{Gymnocephalus cernua} {]}), and other taxa
prefer cold water (e.g. all salmonids, bullhead {[}\emph{Cottus gobio}
{]},\emph{Coregonus} spp., \emph{Lampetra} spp., stone loach
{[}\emph{Barbatula barbatula} {]}, topmouth gudgeon
{[}\emph{Pseudorasbora parva} {]}). The second axis was predominantly
correlated with normalized water age, separating taxa detected in the
deep lacustrine part with longer retention time (e.g. prussian carp
{[}\emph{Carassius auratus gibelio} {]}) from those detected near the
river inflows (e.g. bullhead, \emph{Coregonus} spp., grayling
{[}\emph{Thymallus thymallus} {]}, gudgeon {[}\emph{Gobio gobio}
{]},\emph{Lampetra} spp., stone loach). Finally, the third axis
correlated with the proxy of the trophic gradient, with European eel
(\emph{Anguilla anguilla} ), pumpkinseed and wels catfish detection in
more trophic habitats, and prussian carp and \emph{Coregonus} spp.
detected in nutrient-low environments.

\textbf{Discussion}

Our study demonstrates that eDNA metabarcoding is a useful tool for the
assessment of fish diversity in spatially heterogeneous temperate water
bodies. This novel molecular approach can be used to provide baseline
data for reliable and sustainable long-term monitoring of these systems.
The studied reservoirs exemplify heterogeneous water bodies with
pronounced environmental gradients affecting spatial distribution of
organisms that is not easy to capture with conventional monitoring
methods. In this study, weather conditions during sample collection were
typical for temperate regions, with a hot dry summer followed by cold
wet autumn. The morphology of the studied reservoirs is similar, but
their trophic status and hydrodynamics are different. However, despite
such complex background variation, similar ecological relationships
between biological and environmental factors were detected in different
water bodies. Our results can therefore be used as an example of the
applications for other heterogeneous systems.

\emph{False positives and negatives}

Long-term fish surveys of the studied reservoirs provided robust
background data on the expected distribution and abundance of individual
species that allows these reservoirs to serve as models to study the
mechanisms underlying spatial fish distribution. Gillnets are most
commonly used to sample fish despite their invasiveness, size- and
species-selectivity (Blabolil et al., 2016). Plentiful long-term
(Znachor et al., 2020) and detailed spatially-resolved seasonal data
(Rychteck\selectlanguage{ngerman}ý \& Znachor, 2011) as well as gillnet data dating back to 2004
(Blabolil, et al., 2017b; \selectlanguage{polish}Ř\selectlanguage{ngerman}íha et al., 2015) are available for \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov.
Klí\selectlanguage{polish}č\selectlanguage{english}ava was intensively studied for decades after dam building
(Stra\selectlanguage{polish}š\selectlanguage{english}krabov\selectlanguage{ngerman}á \& Pivni\selectlanguage{polish}č\selectlanguage{english}ka, 2001), and together with \selectlanguage{polish}Ž\selectlanguage{english}lutice, is studied
for current ichthyological research interests (e.g. Vej\selectlanguage{polish}ř\selectlanguage{ngerman}ík et al.,
2019). Three and five gillnet campaigns in Klí\selectlanguage{polish}č\selectlanguage{english}ava and \selectlanguage{polish}Ž\selectlanguage{english}lutice
respectively are supported by data from other sampling methods (i.e.
electrofishing, fyke-nets, seining, hook lines and trawling) as well as
expert knowledge about fish-habitat relationships (Blabolil et al.,
2017b) All of these reservoirs are also routinely monitored by the
Vltava River authorities for water chemistry and biological parameters
(www.pvl.cz).

Based on expert knowledge and long-term monitoring of these reservoirs
and their fish fauna, only a single taxon was identified as a clear
false positive. Capelin, a marine fish of the North Atlantic, is likely
environmental contamination, possibly through the use of Northern pike
bait. No target fish DNA was found in the PCR positive and negative
controls or the field/filtration, evaporation, and extraction blanks,
which indicates that the field and laboratory protocols used were
effective in avoiding contamination. Only one species, burbot, was
previously observed in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov (last caught in spring 2011) and not
detected by eDNA metabarcoding. Burbot is the only member of the family
Gadidae in Czech waters, therefore misclassification is highly
improbable. However, rarer eDNA templates are more likely to be missed
during water sampling or lost during molecular workflow processing
(Kelly et al., 2019). Burbot is active in cold water, thus eDNA sampling
in winter or early spring may improve detection (Blabolil et al., 2018).
We performed three PCR replicates per sample in order to reduce
stochastic variation, but greater replication may have increased
detection probability of rare species within individual samples (Harper
et al., 2019b).

Some fishes, such as asp and rudd or European perch and pikeperch,
belong to different genera, but sequences amplified using the 12S-V5
primers were identical. These species pairs were excluded from
downstream analyses because of assignment uncertainty and differing
ecologies, i.e. piscivorous asp and pikeperch preferring open water, and
invertivorous European perch and omnivorous rudd preferring structured
habitats (Blabolil et al., 2016, 2017a, 2017b). Taxonomic coverage,
resolution, and accuracy could be improved for these and other
genetically similar taxa (e.g. sturgeons and coregonids) by using
different or additional primers that target bony fish, such as MiFish
(Miya et al., 2015) and Teleo (Valentini et al., 2016). For taxonomic
assignment, we used BLAST at 99\% identity against a reference database
for UK fishes (Hänfling et al., 2016) that was supplemented with
sequences for missing Czech fishes. Reference databases should ideally
comprise sequences from specimens occurring in the study area for all
species to account for potential phylogeographic variation as species
can be locally adapted (Delrieu-Trottin et al., 2019). With a more
comprehensive reference database, it may be possible to use 100\% BLAST
identity for taxonomic assignment.

\emph{Diversity and site occupancy of fish communities}

The identification of 31 taxa by eDNA metabarcoding represented more
taxa than expected. Higher fish detectability by eDNA metabarcoding
compared to conventional methods has been found in riverine as well as
lacustrine environments (Hänfling et al., 2016; Li et al., 2019a;
McDevitt et al., 2019; Olds et al., 2016; Pont et al., 2018). In each
reservoir, some taxa were detected for the first time by eDNA
metabarcoding, demonstrating the sensitivity of this method.

All conventional methods (i.e. boat electrofishing, fyke-nets,
gillnetting and seine netting) are selective, thus there are several
reasons for species underestimation with these tools (Kube\selectlanguage{polish}č\selectlanguage{english}ka et al.,
2009). Small-bodied species, such as European bitterling (\emph{Rhodeus
amarus} ) in Kl\selectlanguage{ngerman}í\selectlanguage{polish}č\selectlanguage{english}ava and stone loach in \selectlanguage{polish}Ž\selectlanguage{english}lutice, were most probably
overlooked in structured benthic habitats. Even species with medium body
size, such as white bream in Kl\selectlanguage{ngerman}í\selectlanguage{polish}č\selectlanguage{english}ava and \selectlanguage{polish}Ž\selectlanguage{english}lutice, could be misclassified
to the more common freshwater bream with similar appearance (especially
juveniles) or disguised as hybrids (Kottelat \& Freyhof, 2007). The
other explanation is small population size in a water body and occurence
in a specific habitat (Balasingham et al., 2017). Bullhead, Eurasian
minnow (\emph{Phoxinus phoxinus} ) and salmonids are restricted to
relatively small, structured and shallow parts of tributaries, which are
difficult habitats to sample by conventional methods. Typically netting
is used, but net diameters, mesh sizes and used material determine the
catch (Prchalov\selectlanguage{ngerman}á et al., 2009b; \selectlanguage{polish}Ř\selectlanguage{ngerman}íha et al., 2008). Large fish can
escape from small nets or break through fragile nets, whereas small fish
can escape through larger mesh sizes. Bleak, a common species in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov,
was probably recently introduced in \selectlanguage{polish}Ž\selectlanguage{english}lutice (maybe with piscivorous fish
stocking), as the first record in gillnets was in 2019
(Blabolil,\emph{pers. obs.} ).

eDNA metabarcoding is acknowledged as a highly sensitive method for
detection of invasive and endangered species (Lawson Handley, 2015). Of
the seven non-native species we detected, prussian carp and topmouth
gudgeon have invasive potential in the Czech Republic (Pergl et al.,
2016). Spread of these species can be facilitated by outflowing water
from ponds and therefore early and reliable detection is imperative to
help protect native fishes. Pumpkinseed was detected only in main part
of \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov and will require immediate action to prevent further spread as
it is naturally reproducing in the reservoir. Grass carp
(\emph{Ctenopharyngodon idella} ), \emph{Hypophthalmichthys} spp.,
rainbow trout (\emph{Oncorhynchus mykiss} ) and \emph{Salvelinus} spp.
are dependent on artificial stocking, but grass carp
and\emph{Hypophthalmichthys} spp. are undesirable in reservoirs. Grass
carp reduce submerged vegetation and \emph{Hypophthalmichthys} spp.
filter zooplancton in the water column, supporting phytoplankton.
Therefore, early eDNA detection of undesirable species should trigger
their elimination, similar to eradication management of common carp
(\emph{Cyprinus carpio} ) in Tasmania (Furlan et al., 2019) and topmouth
gudgeon in the UK (Robinson et al., 2019).

Three taxa were classified as regionally Extinct as current populations
are dependent on artificial stocking. Common carp is the most popular
game fish in the Czech Republic and stocked in ponds across the country
(Boukal et al., 2012). Ponds in catchments are the most probable source
of individuals found in the studied reservoirs. \emph{Coregonus} spp.
and \emph{Acipenser} spp. were detected only in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov. Ponds occur in
this catchment as well as fish hatcheries breeding this species
(Pstruha\selectlanguage{polish}ř\selectlanguage{english}stv\selectlanguage{ngerman}í \selectlanguage{polish}Č\selectlanguage{english}RS Kaplice, Ltd.) that are directly connected to the
inflowing river. Potential escapees from pools is therefore plausible
(Naylor et al., 2001). The European eel is categorised as Extinct in the
Wild because migration of juveniles upstream is impossible and high
mortality of adults during downstream spawning migration through dams
with hydro turbines (Trancart et al., 2019). European eel eDNA detection
in Kl\selectlanguage{ngerman}í\selectlanguage{polish}č\selectlanguage{english}ava and \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov could help initiate construction of suitable dam
bypasses and/or capture of spawning stock and transport downstream past
the dam.

The crucian carp (\emph{Carassius carassius} ) was the only Critically
Endangered species we detected and typically occurs in small ponds
(Harper et al., 2019a), but here was exclusively detected from the side
inflow to \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov. In this particular case, it is very probable that eDNA
originated upstream in a pool, the closest one being around 670 m from
the sampling locality. This suggests that populations of endangered
species could be identified through eDNA transport. Vulnerable taxa are
rare in the reservoirs associated with tributaries (Eurasian minnow,
grayling and \emph{Lampetra} spp.) or submerged macrophytes (tench
{[}\emph{Tinca tinca} {]}), but these species can be considered as
indicators of ecological quality and diverse habitats (Blabolil et al.,
2017b, \selectlanguage{polish}Š\selectlanguage{english}mejkal et al., 2014). The category of Near Threatened species is
important as these species could be at high risk of extinction in the
near future and therefore population assessment should follow any eDNA
detections. Finally, most of the species we detected were classified as
Least Concern, suggesting that conditions were suitable for common
species.

The cumulative number of detected taxa was highest (29) in \selectlanguage{polish}Ř\selectlanguage{ngerman}ímov
(largest reservoir) and lowest (16) in Klí\selectlanguage{polish}č\selectlanguage{english}ava (smallest reservoir). Our
observation confirms a general increase of fish diversity with the area,
volume and catchment size of a water body (Mehner et al., 2005).
However, the number of taxa in individual samples was much lower,
typically in single units. This confirms the observation from previous
studies that eDNA is not homogeneously distributed in lentic habitats
and reflects local species presence (Harper et al., 2019a; Lawson
Handley et al., 2019; Li et al., 2019b). We observed greater taxon
detection in autumn compared to summer, which may be due to prolonged
eDNA persistence at colder temperatures (Collins et al., 2018; Lawson
Handley et al., 2019). Summer sampling is more likely to reflect
contemporary species distribution and activity compared to colder
conditions that preserve eDNA and facilitate transport, and could
provide more accurate information on changes in species activity (e.g.
downstream migration for wintering, Jonsson \& Gravem, 1985) as well as
eDNA flushed from upstream. These results provide further evidence that
spatial and temporal replication in eDNA surveys is required for
reliable estimates of species richness (de Souza et al., 2016; Djurhuus
et al., 2020; H\selectlanguage{ngerman}änfling et al., 2016; Lawson-Handley et al., 2019; Li et
al., 2019b).

Despite this spatiotemporal heterogeneity, we found semi-quantitative
abundance estimates from eDNA metabarcoding in reservoirs (i.e. site
occupancy) were highly correlated with abundance categories based on
long-term fish surveys, which corroborates previous studies in UK lakes
(Hänfling et al., 2016; Lawson Handley et al., 2019). The majority of
fish in our studied reservoirs were of riverine origin, but the most
plastic species, such as freshwater bream and roach, prevailed and
occupied new niches of more or less standing water (Blabolil et al.,
2016). Although the pelagic zone was more homogeneous compared to the
structured littoral zone with different bottom substrate, vegetation,
slope, Bray-Curtis dissimilarity values were lower (indicating higher
similarity) for littoral samples compared to pelagic samples. This may
be due to the lower number of taxa per sample in pelagic habitats
resulting in more dissimilar samples. Studies in other geographic
regions also found more species in the littoral compared to the pelagic
zone (Lawson Handley et al., 2019; Zhang et al., 2019). Overall, more
taxa per sample resulted in higher similarity as shown in tributary
samples.

\emph{Environmental correlates of fish distribution}

Interpretation of eDNA-based results is not straightforward as eDNA
dynamics, including deposition, accumulation, transport, and fate, and
fish distribution are influenced by biological and environmental
variables (Barnes et al., 2014; de Souza et al., 2016; Mehner et al.,
2005). In heterogeneous environments, hydrodynamics affecting water
movements should be taken into account. Flow in lotic environments, such
as rivers or brooks, combines eDNA of local residents with eDNA being
transported from upstream (Jane et al., 2015; Pont et al., 2018). Flow
is also present in reservoirs and most likely explains the combined
detection of taxa associated with running water and upstream standing
waters as well as the high diversity found in the main inflows of the
study reservoirs. For example, detection of common carp and topmouth
gudgeon in running water may have been due to eDNA transport from
commercial ponds in the catchment, although escaped fish from these
locations could also explain the eDNA signal in rivers. Downstream eDNA
transport of eDNA was further supported by the negative correlation with
normalised water age for some taxa. In contrast, a positive correlation
with normalised water age reflects species' preference for a particular
habitat and lentic conditions, e.g. prussian carp and European
bitterling (Kottelat \& Freyhof, 2007).

Wind and wave action can also create currents in lentic environments
that transport eDNA (Moyer et al., 2014). This effect is more pronounced
in summer, with strong thermal stratification separating layers of
different temperatures and densities. In this study, species associated
with reservoir bottoms, such as European eel, stone loach and barbel
(\emph{Barbus barbus} ), were detected only in littoral habitats. One
exception is ruffe, the most common species associated with the benthos
in the studied reservoirs that has a pelagic juvenile phase before
settlement on the bottom (J\selectlanguage{polish}ů\selectlanguage{english}za et al., 2015). Ruffe detection in both
habitats can be explained by potential horizontal currents as well as
eDNA shed by juveniles. Nonetheless, horizontal eDNA transport could
prove useful to detect different ontogenetic phases of a species with
eDNA metabarcoding if they utilise distinct habitats (Zhang et al.,
2019).

Despite eDNA transport facilitated by flow, wind, and wave action in
reservoirs, eDNA signals for different taxa were spatially structured
and reflected species' ecological niches. Spatial distribution of fishes
in heterogeneous freshwater bodies can result from various environmental
factors individually or combined, but the most important factor is
temperature. Higher temperature stimulates metabolic and behavioural
activity of most poikilotherm fish (Stoner, 2004), which can also
increase eDNA release and deposition by fish into environment as they
exhibit greater rates of movement, feeding, digestion, and excretion (Jo
et al., 2019; Robson et al., 2016). More active fish may have higher
eDNA detection probabilities; however, warmer water temperature is also
associated with greater UV irradiation, greater microbial activity
(including feeding on eDNA as a dissolved organic phosphorus substrate),
and reduced oxygen solubility (Barnes et al., 2014; Collins et al.,
2018; Scharfenberger et al., 2019). All of these factors contribute to
eDNA degradation and subsequently lower detection probabilities (Jo et
al., 2019; Tsuji et al., 2017).

Species classed as cold water were detected in late autumn, mostly in
tributary parts of the reservoirs. Specifically, coldwater salmonids
were detected in running waters as expected due to higher water
temperature at the surface and low DO concentrations in the colder,
lower layers of standing water. eDNA accumulation in deep hypolimnetic
water was not supported by our data. Species typically associated with
deep open water, such as \emph{Coregonus} spp., are naturally missing in
artificial water bodies and where stocked, their presence is often
restricted by lack of DO during summer stratification (Kottelat \&
Freyhof, 2007). The effects of DO and locality are further masked by
tight correlations with temperature, trophic status and water age. For
robust statistical analyses, more seasons and an equal number of
habitats should be sampled. We selected two contrasting seasons and the
number of habitats was derived by each reservoir\selectlanguage{ngerman}´s morphology to achieve
a reasonable compromise between investigator effort and information
gained.

In our study, fish communities in canyon-shaped reservoirs were heavily
driven by trophic status along the longitudinal gradient. The proxy of
total ChlA concentration was positively correlated with temperature, and
species associated with warmer water were detected in samples with
higher trophic status. European eel was the only species strongly
associated with trophic status independent of temperature. This species
is an ambush predator, often with nocturnal activity (Trancart et al.,
2019), and so may benefit from the dense water blooms and artificial
darkness created by high trophic status. Community composition and
distribution of particular species were found to change in response to
trophic status in freshwater (Lawson Handley et al., 2018) and marine
(Djurhuus et al., 2020) systems using eDNA metabarcoding. These findings
and our own provide evidence that eDNA metabarcoding can be used to
understand shifts in communities in response to environmental gradients.

\emph{Conclusion}

Robust data are necessary to assess and improve ecological status.
Conventional methods are usually invasive or even destructive. To
increase the use of novel non-invasive methods, validation is necessary.
This study demonstrated that eDNA metabarcoding can provide valuable
data for ecological interpretation. The synthesis of eDNA-based data
with environmental data measured by mobile multiparameter devices has
great potential to substitute conventional fish monitoring. Moreover, a
combined analysis of both data types is suitable for addressing
fundamental ecological questions. This research has contributed to a
better understanding of eDNA dynamics and will motivate stakeholders for
broader application. The application of eDNA metabarcoding for
biomonitoring in heterogeneous ecosystems is a huge step forward to
reach high quality data with a non-invasive method that is worthy of the
21\textsuperscript{st} century.

\textbf{Acknowledgments}

Thanks are given to Vltava River Authority for enabling the fieldwork,
colleagues from FishEcU (www.fishecu.cz) for help in the field and
expert judgment, Ji\selectlanguage{polish}ř\selectlanguage{ngerman}í Jaro\selectlanguage{polish}š\selectlanguage{ngerman}ík and Allan Souza for help with the
graphical presentation. This study was supported by the project
QK1920011 ''Methodology of predatory fish quantification in
drinking-water reservoirs to optimize the management of aquatic
ecosystems'', MSM200961901 \selectlanguage{english},,The true picture of eDNA``, ERDF/ESF project
``Biomanipulation as a tool for improving water quality of dam
reservoirs'' (No. CZ.02.1.01/0.0/0.0/16\_025/0007417) and by the CAS
within the program of the Strategy AV 21 (VP21).

\textbf{Author Contributions}

PB, TJ, MV, ZS, PR, PZ and JP participated in the field work. PB, LH,
\selectlanguage{polish}ŠŘ, \selectlanguage{english}GS and CM participated in the laboratory work. PB, JP and BH
designed the study. PB, LH and JH conducted the data analysis. PB wrote
the first draft. All authors contributed substantial feedback during
manuscript preparation. The authors declare no conflict of interest.

\textbf{Data Accessibility}

DNA sequences: The \emph{de-novo} sequences were uploaded to Genbank,
asp accession numbers MT163435, MT163450, MT163449 and marena whitefish
accession numbers MT163451, MT163458, MT163460. For each PCR product, a
FASTQ file comprising all raw reads is archived in NCBI Sequence Read
Archive under accession number PRJNA611963.

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