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 (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 (Acipenser sturio ) was
changed to Acipenser spp. as Siberian sturgeon (A. baerii )
and Danube sturgeon (A. gueldenstaedtii ) were previously captured
by conventional methods but these species were missing from our
reference database. Vendace (Coregonus albula ) was corrected toCoregonus spp. as maraena whitefish and peled (C. peled )
are more often stocked in Czech waters. Similarly, Arctic char
(Salvelinus alpinus ) was corrected to Salvelinus spp. as
the closely related brook trout (S. fontinalis ) is more often
stocked in Czech waters, and these species cannot be distinguished using
the target 12S rRNA fragment. Silver carp (Hypophthalmichthys
molitrix ) and bighead carp (H. nobilis ) were merged toHypophthalmichthys spp. as both species are artificially
selected, resulting in hybrids, and share the same ecological niches.
Finally, river lamprey (Lampetra fluviatilis ) was corrected toLampetra spp. because river lamprey is not present in the studied
catchments and sequences likely belong to European brook lamprey
(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 (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 (Lota lota ), was
completely missed by eDNA metabarcoding having been previously detected
in Římov 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 (Jůza et al.,
2015; Říha 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-1with 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 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 (Šmilauer &
Lepš, 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.