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\begin{document}

\title{Effects of mining activities on fish communities and food web dynamics
in a lowland river}



\author[1]{Kristin Scharnweber}%
\author[2]{Carolin Scholz}%
\author[3]{Victor Schippenbeil}%
\author[4]{Stefania Milano}%
\author[5]{Daniel Hühn}%
\affil[1]{University of Potsdam}%
\affil[2]{Leibniz Institute for Zoo and Wildlife Research}%
\affil[3]{Humboldt-Universitat zu Berlin Mathematisch Naturwissenschaftliche Fakultat}%
\affil[4]{Leibniz-Institut fur Zoo- und Wildtierforschung (IZW) im Forschungsverbund Berlin eV}%
\affil[5]{Institut fur Binnenfischerei eV}%


\vspace{-1em}



  
  \date{May 16, 2024}


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\selectlanguage{english}
\begin{abstract}
Fish communities of streams and rivers might substantially be subsidized
by terrestrial insects that fall into the water. Although such
animal-mediated fluxes are increasingly recognized, little is known on
how anthropogenic perturbations may influence the strength of such
exchanges. Intense land-use, such as lignite mining may impact a river
ecosystem due to the flocculation of iron (III) oxides, and thus
altering food web dynamics. We compared sections of the Spree River in
North-East Germany that were greatly influenced by iron oxides with
sections located downstream of a dam where passive remediation
technologies are applied. Compared to locations downstream of the dam,
the abundance of benthic macroinvertebrates at locations of high iron
concentrations upstream of the dam was significantly reduced. Similarly,
catch per unit effort of all fishes was significantly higher in
locations downstream of the dam compared to locations upstream of the
dam and condition of juvenile and adult piscivorous pike Esox lucius
were significantly lower in size in sections of high iron
concentrations. Using an estimate of short-term (i.e., metabarcoding of
the gut content) as well as longer-term (i.e., hydrogen stable isotopes)
resource use, we could demonstrate that two of the three most abundant
fish species, perch Perca fluviatilis, and bleak Alburnus alburnus,
received higher contributions of terrestrial insects to their diet at
locations of high iron concentration. In summary, lotic food webs
upstream and downstream of the dam greatly differed in the overall
structure with respect to the energy available for the highest tropic
levels and the contribution of terrestrial insects to the diet of
omnivorous fish. Therefore, human-induced environmental perturbation
such as river damming and mining activities represent strong pressures
that can alter the flow of energy between aquatic and terrestrial
systems, indicating a broad impact on the landscape level.%
\end{abstract}%



\sloppy


\textbf{Introduction}

In the current era of the Anthropocene, river ecosystems worldwide are
extensively modified by human activities (Cid et al. , Dudgeon et al.
2006). Land-use practices are one of the main threads that leads to the
loss of freshwater biodiversity (Dudgeon 2019). Besides the direct
negative effect on overall abundance and richness of organisms, indirect
effect may co-occur by altering the linkages among habitats and
communities (Foley et al. 2005). Rivers and their adjacent terrestrial
zones are tightly linked via multiple fluxes and pathways (Polis and
Hurd 1996, Baxter et al. 2005, Bartels et al. 2012). In this study, we
focus on the animal-mediated flux from land-to-water, which is the one
of terrestrial invertebrates falling into water bodies and being eaten
by fish. This flux can be pronounced and may provide up to 50\% of the
annual energy budget of fish individuals (Nakano and Murakami 2001,
Baxter et al. 2005). While it has been shown that even the nutrient pool
of a lake can substantially be subsidized by terrestrial phosphorus
excreted by fish feeding on surface insects (Mehner et al. 2005), these
fluxes are generally assumed to be strongest in aquatic systems with
extended riparian zones, especially in streams with a high canopy cover
(Edwards and Huryn 1996, Kawaguchi and Nakano 2001). Although this
animal-mediated flux between adjacent ecosystems is increasingly
recognized, little is known on how anthropogenic perturbations may
influence the strength of such exchanges (but see Larsen et al. 2016).
For example, the degradation of riparian zones, such as from livestock
grazing (Saunders and Fausch 2012) or deforestation (Kawaguchi and
Nakano 2001), significantly decreased the reliance of fish on
terrestrial insects. However, another aspect of land use with a strong
impact on our river ecosystems and the potential to alter food web
dynamics is the one from mining activities.

Lignite (brown coal) mining activities on aquatic systems is influencing
the chemical, physical, biological, and ecological properties of the
system (Gray 1997, Byrne et al. 2012), and iron is one of the key
contaminants involved. Due to the oxidation of ferrous sulphide metals
(e.g. pyrite and marcasite) during mining or related to the progressive
groundwater resurgence in mine reclamation areas, large amounts of iron
contaminate groundwater and adjacent aquatic systems. The
well-oxygenated and neutral pH conditions in lotic systems will evoke a
flocculation of iron (III) oxides, leading to browning and a turbid
environment (Gray 1997, Byrne et al. 2012; Figure 1). Besides potential
toxic effects via direct metal uptake (Vuori 1995), river organisms
suffer from the formation of precipitates covering gills and eggs
(Andersson and Nyberg 1984, Gerhardt 1992). However, most dominantly,
iron precipitates alter the habitat availability for stream organisms as
the fine iron sediments bury hard substrates, periphyton, organisms, and
clog interstitial spaces of the benthic habitat (Letterman and Mitsch
1978, McKnight and Feder 1984). The communities of benthic invertebrates
in river sections characterized by high iron oxide concentrations are
therefore typically found to be of lower abundance, species biomass, and
diversity (Rasmussen and Lindegaard 1988, Vuori 1995, Maret et al. 2003,
Cadmus et al. 2016). In turn, this could have consequences for the food
availability of fish inhabiting the river sections of high iron
concentrations, potentially limiting the trophic transfer and the energy
available for fish production. Furthermore, we suggest that iron has the
potential to restructure the lotic food webs and rearrange the
connections to the terrestrial systems. It can be expected that fish
inhabiting river sections where benthic invertebrates as food is scarce
rely to a higher degree on terrestrial invertebrate food resources.

A straight-forward approach to estimate the contribution of terrestrial
insects to the diet of fish is to simply look at the gut content (Manko
2016). However, the identification of different prey items can be
problematic in cyprinid fishes that have pharyngeal teeth that crush the
food. Here, dietary studies using DNA metabarcoding can provide a high
resolution of resource use (Amundsen and S\selectlanguage{ngerman}ánchez-Hernández 2019).
Nonetheless, DNA metabarcoding of gut content analyses only allows a
snap-shot of the recently ingested prey. In addition, stable isotopes
can provide an integrated estimate of resource use over longer time
periods (Grey 2006, Boecklen et al. 2011). In the context of identifying
the contribution of terrestrial insects to the diet of fish, stable
isotopes of hydrogen (\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H) are particularly useful due
to the strong divergence between aquatic and terrestrial endmembers
(Doucett et al. 2007, Vander Zanden et al. 2016).

Here, we studied the cumulative effects of iron (III) oxides on a lotic
food web and the linkages to the terrestrial surroundings. Specifically,
we predict that in river sections of high iron concentrations (i.e.
upstream of the dam), less benthic invertebrates are present and fishes
feed on terrestrial insects to a higher degree compared to fish
individuals found in river sections of lower iron concentrations (i.e.
downstream of the dam, following water remediation). This will be
indicated by a higher number of terrestrial species found in the gut
content (identified by their DNA-sequences using metabarcoding
techniques), and further, by a higher terrestrial index calculated from
hydrogen stable isotope values demonstrating the longer-term resource
use. In addition, we predict that low abundance of benthic invertebrates
upstream of the dam will decrease the general fish abundance, and hence
the condition factor of the top predator (piscivorous pike \emph{Esox
lucius} ).

\textbf{Methods}

\emph{Study area}

We conducted a study at the Spree River, which is a sixth order lowland
river located in north-eastern Germany. It flows through the Lusatian
region, where lignite mining activities during the times of the German
Democratic Republic have been extensive and are still conducted to date
(Kr\selectlanguage{ngerman}ümmelbein et al. 2012). In the 1960s, the Spree River was damned for
flood control purposes and hydropower generation. As a consequence, the
Spremberg Reservoir was built, which is one of the largest reservoirs in
Germany. Nowadays, the reservoir plays a crucial role in securing the
water quality of the Spree River water impacted from the mining
activities. Due to the post-mining groundwater rerise, sections of the
rivers are characterized by large amounts of iron, sulphate, trace
metals, and aluminum (Friedland et al. 2021). To retain the iron load
and to safeguard the drinking water supply, passive remediation
technologies are applied. Sequential conditioning of the river water
comprised the addition of lime to increase pH to accelerate iron (II)
oxidation and the addition of a flocculation aid to enhance flocculation
of iron hydroxide sludge (Uhlig et al. 2016). The precipitated iron
sludge usually ranges between 1 000-4 000 kg, occasionally 150 000 kg on
a daily base (Uhlmann et al. 2021). The visibility threshold of the
dominant fraction of iron in the study area, iron (III) hydroxide, is
about 2-3 mg L\textsuperscript{-1} (Benthaus and Totsche 2015); hence
the conditioning of the iron-rich water lead to contrasting environments
in river sections with turbid water of high iron concentrations upstream
of the dam and clear water of lower iron concentrations downstream of
the dam (Friedland et al. 2021, Uhlmann et al. 2021, Figure 1).

\emph{Field sampling}

All sampling was conducted in summer, between August
16\textsuperscript{th} and 27\textsuperscript{th} 2021. Sampling sites
were located along a stretch of the Spree River of about 20 km, close to
the town of Spremberg, Brandenburg, Germany, where the dam is located
(Figure 1). We sampled at six locations in total, three located upstream
of the dam (i.e., at total iron (TFe) \textgreater{}3.8 mg
L\textsuperscript{-1}; Zerre, Trattendorf, Wilhelmstal), and three
downstream of the dam (i.e., at TFe \textless{} 0.3 mg
L\textsuperscript{-1}; Bräsinchen, Frauendorf, Madlow). Besides the
water quality, sites were comparable with regards to habitat
characteristics (Table 1). For three sites (Zerre, Wilhelmstal and
Bräsinchen) data regarding turbidity, TFe, total phosphorus, and total
nitrogen (TN) were obtained from the local authority (Landesamt für
Umwelt Brandenburg) who collects this data monthly within the context of
the European water framework directive. We collected benthic
macroinvertebrates for abundance estimates and stable isotope analysis
from river sediments using an Ekman grab. Samples were sieved through
500\selectlanguage{greek}µ\selectlanguage{english}m and stored in ethanol. In the lab at the Institute of Inland
Fisheries Potsdam-Sacrow, they were sorted into broad taxon groups and
individuals were counted. Abundances were expressed as individuals per
m\textsuperscript{-2} and for every location an average of three samples
was calculated for each taxon group. Seston samples for stable isotopes
were collected using a 100\selectlanguage{greek}µ\selectlanguage{english}m plankton net that was lowered into the
water. Samples included few and small zooplankton. We used Malaise traps
without any collecting agent to sample flying terrestrial insects for
the stable isotope analysis (i.e. Tineidea, Diptera, and Tipulidae),
which we employed for 24 hours. Permit for sampling was received from
the respective authority (Landesamt f\selectlanguage{ngerman}ür Umwelt Brandenburg). In
addition, single groups (i.e., Formicidae, and Orthoptera) were
hand-collected from the meadows. Terrestrial insects were collected at
two locations only: at Trattendorf representing insects upstream of the
dam, and at Bräsinchen representing insects downstream of the dam
(Figure 1). Insects were killed by freezing, sorted into groups and
stored at -20°C prior to isotope analyses.

Electrofishing to sample the fish communities was conducted under the
permit number 2-2021 approved by the local environmental agency (hunting
and fisheries authority of the administrative district Spree-Neiße).
Sampling was performed at both river banks during day-time using a
generator-powered DC electrofishing unit (Type FEG 8000, 8 kW, EFKO
Elektrofischfanggeräte GmbH, Leutkirch im Allgäu, Germany) equipped with
one anodic handnet of 45 cm ring diameter and a 4 m long cupper litz
wire as cathode. Length of electro fished river sections ranged between
300-640 m at the six locations. Each section was fished in both flow
directions (i.e., upstream and downstream) in a standardized way
according to the European standard protocol EN 14011:2000 and this
procedure was repeated 3 times. Natural boundaries of each section (i.e.
weirs or riffles) prevented fish to escape from electro fishing. At each
section, captured fishes were identified to species level, counted,
measured (up to 1 mm), weighted (up to 0.1 g), and placed in a holding
net. Once completed, all captured fishes were released, unless they were
taken for further analyses. We summed all fishes caught at one
particular section and calculated catch per unit effort (CPUE, expressed
as individuals 1000 m\textsuperscript{-1}) and biomass per unit effort
(BPUE, g 1000 m\textsuperscript{-1}). Individuals of the most abundant
fish species caught at Trattendorf (upstream of the dam) and Bräsinchen
(downstream of the dam) were stored on ice and brought to the lab. Here,
gut content was collected for metabarcoding and a small piece of dorsal
muscle tissue was sampled for stable isotope analyses of hydrogen.
Scales of pike were collected to allow age estimates via counting of
annuli in this top predator. To compare the condition of the top
predator, and thus, allow a comparison for the amount of energy that
reaches the highest trophic levels, we calculated Fultons condition
factor (K) according to the Htun-Han (1978) equation for pike of the
different age cohorts:

\(K=\frac{W\times 100\ \ }{L^{3}}\),

where W =weight of fish (g) and L= total length of fish (g).

\emph{Metabarcoding}

We followed the methodological approach described in Scholz and Voigt
(2022). In brief, DNA was extracted from the frozen fish gut content by
applying NucleoSpin© Food and NucleoSpin© Soil Kit (Macherey-Nagel GmbH
\& KG, Düren, Germany) as outlined in the manufacturer's instructions.
We performed two DNA extractions for each gut content sample. The
concentration of the extracts was determined by fluorometric
quantification in a Qubit Fluorometer (Qubit fluorometric quantification
dsDNA High Sensitivity Kit, ThermoFisher Scientific, Walham, USA). Some
of the DNAs had to be cleaned and concentrated using a DNA Clean and
Concentrator Kit (Zymo Research, 17062 Murphy Ave, Irvine, CA 92614,
USA) to get rid of PCR-inhibitors. Throughout the laboratory work, we
strictly applied protocols to prevent contaminations by alien DNA or PCR
products. The presence of contaminations was checked through all
laboratory steps using different negative controls.

We performed a double-PCR strategy with dual indexing. The first PCR
amplified the target region CO1 (Cytochrome oxidase subunit 1) region
(Galan et al. 2018), the second PCR added the indices to the target
region. Products were checked with an agarose gel and cleaned twice with
magnetic beads (CleanNGS, GC biotech, Waddinxveen, Niederlande). All
products were quantified by fluorometric quantification in the plate
reader (Quant-iT\selectlanguage{english} dsDNA Assay Kit, high sensitivity, ThermoFisher
Scientific, Walham, USA) and pooled in equimolar concentration. If
necessary, the final library was purified and concentrated by using
CleanNGS beads. The quality and integrity of the library were confirmed
using the Agilent 2200 TapeStation with D1000 ScreenTapes (Agilent
Technologies, Santa Clara, California, USA).

Sequences were generated at the Berlin Centre for Genomics in
Biodiversity Research (BeGenDiv) in two runs on the Illumina MiSeq
platform (Illumina, San Diego, California, USA) using v3 chemistry with
600\selectlanguage{english} cycles. The quality of the generated reads was evaluated using
FastQC v.0.11.9 and multiqc. The remaining adapter sequences were
removed using cutadapt (version 2.8).

Sequencing reads processing from quality control to taxonomic assignment
was performed using the R package ``dada2'' (Callahan et al. 2016). We
assigned taxonomy to the inferred Amplified Sequence Variants (ASVs) up
to species level based on the reference database provided by the
BeGenDiv (Heller et al. 2018). Taxonomy was assigned based on the single
best hit or a last common ancestor (in case of multiple best hits) with
50 out of 100 bootstrap replicates as minimum bootstrap confidence for
assigning a taxonomic level. For post-sequencing removal of reads caused
by contamination, we used the R package ``microDecon'' (McKnight et al.
2019) which uses the proportions of ASVs in blank samples (negative
controls) to systematically identify and remove contaminant reads from
the metabarcoding data set. Afterwards, we summed up reads for
pseudo-biological replicates and removed reads which were only present
in one of two technical replicates to further increase the power and
quality of our data set.

We restricted our dataset to results of sequences on the species level
and deleted the finding of bat sequences (\emph{Myotis daubentoniid}
and\emph{Pipistrellus pygmaeus} ) in the gut content of seven fish
individuals, as we assumed that this was not the results of selective
feeding on the bats, but instead the incorporation of bat feces. All
species identified in the gut content were classified into the
categories ``aquatic'' and ``terrestrial'', corresponding to their
dominant life phases and we counted the number of ingested terrestrial
species for each fish individual.

\emph{Stable isotope analysis}

Analyses of stable isotopes of hydrogen (\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H) of fish
muscle tissue (bleak: \emph{N} = 27, perch: \emph{N} = 13,
roach:\emph{N} = 26), seston (\emph{N} = 6), and terrestrial insects
(\emph{N}= 12) samples were conducted at the Leibniz-Institute for
Zoo-and Wildlife Research Berlin. To remove contaminants, each insect
sample was washed using a 2:1 chloroform: methanol solution for 24 h.
Fish muscle samples were fat-extracted using a Soxtherm extractor (C.
Gerhardt GmbH \& Co. KG, K\selectlanguage{ngerman}önigswinter, Germany). After being dried in an
oven at 50\selectlanguage{english} for 72 hours, sample aliquots of 0.35 +- 0.10 mg were placed
into silver capsules (IVA Analysentechnik, Meerbusch, Germany).
Non-exchangeable hydrogen isotope ratios were measured using a High
Temperature Conversion Elemental Analyzer (Thermo Fischer Scientific
Inc., Waltham, USA) connected to an online temperature-controlled
vacuum-equilibration autosampler Uni-Prep (EuroVector, Pavia, Italy) and
coupled to a continuous\selectlanguage{english}-flow isotope\selectlanguage{english}-ratio mass spectrometer (Delta V
Advantage; Thermo Fischer Scientific Inc.). Measurements were determined
using a comparative equilibration method (Wassenaar and Hobson 2003).
Samples and standards were loaded into the autosampler at 60\selectlanguage{english}. After
flushing with helium and evacuating the carousel, 20 \selectlanguage{greek}μ\selectlanguage{english}l of water of
known isotopic composition was injected trough the Uni-Prep septum for
equilibration (1 h). To ensure that a similar H exchangeability among
standards and samples, samples were measured together with in-house
standards, previously calibrated against international reference
materials. Stable hydrogen isotope ratios were expressed as deviations
from the international standard V-SMOW. Precision of
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H measurements was always better than 1.1%0 (1 SD).

\emph{Terrestrial index modelling}

Prior to estimating the contribution of terrestrial species to the diet
of individual fish (i.e., terrestrial index), we corrected for the
influence of environmental (or dietary ) water on the
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H of fish (Solomon et al. 2009) and followed the
approach outlined in Vander Zanden et al. (2016):

\begin{equation}
\omega_{\text{compound\ }}=\ {1-\left(1-\omega\right)}^{TL-1}\nonumber \\
 \end{equation}

Where \(\omega_{\text{compound\ }}\)is the proportion of water \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H
in \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H values of fish consumers,\(\omega\) is
the proportion of water \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H entering the consumer. For
\(\omega\), we assumed a value of 0.2 as suggested by Wilkinson
et al. (2015). We did not estimate the trophic level
(\selectlanguage{ngerman} TL) of the fish, but assumed a theoretical level of
2.5, as we analyzed omnivorous species and individuals.

In a second step, we modelled \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H values for fish
consumers either obtaining their unexchangeable hydrogen either entirely
from aquatic, or terrestrial resources:

\begin{equation}
\delta^{2}H_{C\ 100\%\ aquatic\ }=\left(\omega_{\text{compound\ }}\times\ \delta^{2}H_{\text{water\ }}\right)+\left(1-\ \omega_{\text{compound\ }}\right)\times\ \delta^{2}H_{\text{aquatic\ }}\nonumber \\
 \end{equation}\begin{equation}
\delta^{2}H_{C\ 100\%\ terrestrial\ }=\left(\omega_{\text{compound\ }}\times\ \delta^{2}H_{\text{water\ }}\right)+\left(1-\ \omega_{\text{compound\ }}\right)\times\ \delta^{2}H_{\text{terrestrial\ }}\nonumber \\
 \end{equation}

\(\delta^{2}H_{\text{water\ }}\)is the \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H of the river water. We
did not collect water samples ourselves, but instead used a mean value
(-55.06 %0) from Chen et al. (2023), who collected river water in
Spremberg and Zerre in June and December 2021. For\(\delta^{2}H_{\text{aquatic\ }}\), we
used the mean value of seston (from locations downstream and upstream of
the dam respectively), whereas for \(\delta^{2}H_{\text{terrestrial\ }}\), we used the mean
value of the terrestrial insects (from locations downstream and upstream
of the dam respectively).

The terrestrial index for individual fish was modelled, using a modified
two-end members mixing model similar to the allochthony index from Keva
et al. (2022).

\begin{equation}
terrestrial\ index\ =\ \frac{{(\delta}^{2}H_{\text{C\ \ }}-\ \delta^{2}H_{C\ 100\%\ aquatic\ })}{{(\delta}^{2}H_{C\ 100\%\ terrestrial\ }-\ \delta^{2}H_{C\ 100\%\ aquatic\ })}\nonumber \\
 \end{equation}

\(\delta^{2}H_{\text{C\ \ }}\)is the measured value of the individual fish
consumer. The terrestrial index ranges from 0-1, with high values
indicating the incorporation of hydrogen from terrestrial insects, while
low values indicate the incorporation of hydrogen from aquatic insects.

\emph{Statistical analyses}

Data analyses were conducted in R version 4.1.0 (R Core Team 2021) and R
Studio 1.4.1717. For the analyses of fish community composition, we
removed all fish species with an abundance [?] 0.1\% and we focused on the
remaining 15 species. We used the vegan package and conducted Analysis
of similarities (ANOSIM) on the Bray-Curtis dissimilarly measure (9999
permutations) to determine the significance of differences among
communities of benthic invertebrates and fish upstream and downstream of
the dam. We further used non-parametric Mann-Whitney U Tests to compare
the total abundance of benthic invertebrates, CPUE and BPUE of fish, and
condition factor of pike upstream and downstream of the dam. We used
Mann-Whitney U Tests to compare the number of terrestrial insects in the
gut, as well as the terrestrial index for the three most abundant fish
species (i.e. roach, bleak, perch) upstream and downstream of the dam.

\textbf{Results}

\emph{Macroinvertebrate and fish community}

While Chironomidae were the most abundant taxa in the invertebrates
samples of sites located upstream of the dam, Bivalvia and Gastropoda
dominated the community composition upstream of the dam (Appendix 1).
However, ANOSIM revealed that the composition of community structure of
benthic invertebrates upstream and downstream of the dam was not
significantly different. Total abundance was significantly higher at
locations downstream of the dam (Z = 3.86, P = 0.0495; 5945 individuals
m\textsuperscript{-2} \selectlanguage{ngerman}± 2106, mean ± SD) compared to locations upstream
of the dam (982 individuals m\textsuperscript{-2} ± 1100; Figure 2).

We caught 20 fish species in total (Appendix 2). The three most abundant
fish species were roach \emph{Rutilus rutilus} , bleak, and perch (37.6
\%, 17.5 \%, and 15.7 \% of total abundance, respectively; Appendix 2).
ANOSIM indicated that the fish community composition upstream and
downstream of the dam was not significantly different, neither when
accounting abundances (i.e., CPUE), nor biomass data (i.e., BPUE).
However, total CPUE of all fishes was significantly higher in locations
downstream of the dam (Z = 3.86, P = 0.0495; 158 individuals 1000
m\textsuperscript{-1} ± 49) compared to locations upstream of the dam
(60 individuals 1000 m\textsuperscript{-1} ± 35; Figure 3), but BPUE was
not significantly higher.

\emph{Condition factor of pike}

In total, we caught 55 pike, 31 individuals upstream and 24 individuals
downstream of the dam. From scale annuli, we were able to clearly
identify age-0 pike (\emph{N} = 35), but for other cohorts, age
identification was not so clear and we pooled all adult individuals
\textless{} age 0. Condition factor of age-0 pike individuals was
significantly higher downstream of the dam (0.64 ± 0.12, average ± SD),
compared to individuals upstream of the dam (0.54 ± 0.05; Z = 11.33, P
\textless{} 0.0001). Similarly, condition factor of adult pike
individuals \textgreater{} age 0 was significantly higher downstream of
the dam (0.68 ± 0.08, average ± SD), compared to individuals upstream of
the dam (0.60 ± 0.04; Z = 6.325, P = 0.012).

\emph{Metabarcoding}

Unfortunately, some problems during the PCR-procedure in the lab
appeared that resulted in less fish individuals with a sequenced gut
content. We identified 32 aquatic and 17 terrestrial species in the gut
content of the 31 fish individuals analyzed (Appendix 1). Fish
individuals feed on 1-8 different species and perch, irrespective of the
location, had the most diverse diet (5 ± 3 species), in contrast to
roach (3 ± 2 species), and bleak (2 ± 1 species) (Table 2). No
terrestrial species were identified in the gut content of fish caught
downstream of the dam and no terrestrial species were identified in the
gut content of roach upstream of the dam either, thus, not allowing any
statistical comparison (Table 2). However, in locations upstream the
dam, terrestrial species were ingested by bleak (1 species ± 1) and
perch (4 species ± 1) and this result was significant for the latter
species (Z = 6.137, P = 0.013, Table 2).

\emph{Stable isotope analysis and terrestrial index}

Generally, seston samples were more depleted in deuterium, and thus had
lower \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H values compared to terrestrial insects
(Appendix 2). Terrestrial index (i.e., the contribution of terrestrial
resources to the diet of the consumer) was significantly higher for
roach (Z = 16.25; P \textless{} 0.001; 0.60 \selectlanguage{ngerman}± 0.05), perch (Z = 7.71; P
= 0.005; 0.86 ± 0.10), and bleak (Z = 21.39; P = \textless{} 0.001; 0.79
± 0.13) caught upstream of the dam, compared with individuals of the
respective species downstream of the dam (roach:0.40 ± 0.06, perch: 0.33
± 0.07; bleak: 0.28 ± 0.12; Figure 4).

\textbf{Discussion}

In this study, we demonstrate that iron flocculation resulting from
open-cast lignite mining has the ability to restructure the lotic food
web, including the abundance of benthic invertebrates and fishes, growth
of YOY top predators and the strength of the linkage to the adjacent
terrestrial ecosystem. While several studies have shown the consequences
of iron concentrations for single species or specific communities, our
study is among the first that integrates consequences to stream
functioning and structure, i.e., the animal-mediated fluxes occurring
across the land-water interface.

Fluxes from land to water occur primarily in the appearance of
terrestrial carbon from plants, either in the particulate form as plant
litter or in dissolved form, mostly as humic substances. Several studies
could demonstrate that terrestrial carbon has the ability to fuel
aquatic food webs and can be traced up to the highest trophic levels
(Karlsson et al. 2012, Scharnweber et al. 2014, Tanentzap et al. 2014).
However, another pathway of terrestrial particulate carbon into aquatic
systems, especially important for lotic systems, is the one via
terrestrial insects falling on the water surface and being consumed by
freshwater fish (Baxter et al. 2005). In the famous greenhouse
experiments of Nakano et al. (1999), terrestrial insects were prevented
from falling into a forest stream, and the strong impact of these
pathways could be demonstrated, being able to shape the feeding ecology
(Fausch et al. 1997) and overall fish biomass (Kawaguchi and Nakano
2001).

In the present study, we demonstrate how the human impact on land-use,
more precisely via the contamination of iron resulting from lignite
mining is able to alter these linkages and shape fish communities.
Generally, fish abundance and biomass are known to be low in lotic
systems characterized by high iron concentrations (Vuori 1995, Amisah
and Cowx 2000), and our results are in line with these previous
findings. The low abundance of benthic invertebrates as food, as
demonstrated in this study, might play a crucial role in this
correlation. Such low availability of aquatic prey at high iron section
of the Spree River might force some fish species to integrate
terrestrial insects into their diet. Here, we could demonstrate this
switch using a combination of techniques illustrating the short-term
(metabarcoding of gut content), but also long-term (stable isotopes of
hydrogen) resource use. Indicated by the DNA-sequences from
metabarcoding, perch and bleak caught upstream of the dam had a higher
number of terrestrial species in their gut content, but this result was
not significant for roach. While perch and roach are known to usually
ingests only minor proportions of terrestrial insects (e.g., Persson
1983, Svanbäck and Bolnick 2007, Marklund et al. 2019), bleak has an
upwards facing snout showing the adaptions feeding on insects from the
water surface (Biro and Musko 1995). The metabarcoding approach does not
allow for a qualitative assessment of the ingested prey, as biomass
estimates are not possible. However, the pattern was further confirmed
by the stable isotope data, and for perch, bleak and also roach, the
incorporation of terrestrial resources to the diet increased
significantly from 34\% on average at low iron sections to 74 \% on
average. Terrestrial species identified in the gut content of the fish
included a wide range of species and taxa, including earth worms, ants
or flying insects and most identified species (except for two species)
appeared only once in the entire dataset. Fish that incorporate
terrestrial species into their diet may therefore not specialize on
specific prey, but might respond rather opportunistically to organisms
that end up on the stream surface.

Significant differences between the terrestrial reliance of fish
individuals upstream and downstream of the dam was pronounced, as
demonstrated from the two methods integrating different time scales.
However, some uncertainty in the absolute values of the terrestrial
contributions have to be considered. When correction for the proportion
of water \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H entering the consumer, we applied and
correction factor of 0.2, as suggested by Wilkinson et al. (2015).
However, this correction factor is still under debate and the literature
also shows that the value of \selectlanguage{greek}ω \selectlanguage{english}can be assumed as 0.17 (Solomon et al.
2009) or averaging 0.25-0.30 (Brett et al. 2018). The outputs of the
mixing models are very dependent on this assumption, thus making it
difficult to compare the obtained estimates to previous reported
terrestrial contributions from other systems.

Such diet shift towards a higher integration of terrestrial insects was
also reported from Brook and Brown Trout (\emph{Salvelinus
fontinalis}and \emph{Salmo trutta} ) that compensated for the low
availability of aquatic prey in stream sections of high heavy metal
concentrations (Kraus et al. 2016). However, the feeding on terrestrial
organisms may come at a cost. First, fish individuals feeding on the
water surface might be more vulnerable to attacks from aerial predators.
In addition, aquatic resources are generally rich in PUFAs, while
terrestrial insects are characterized by low concentrations of these
important biomolecules (Twining et al. 2016, Twining et al. 2021). Thus,
fish individuals feeding to a high degree on terrestrial insects and
therefore facing a diet of lower quality when it comes to PUFA content,
might have to internally synthesize PUFAs to meet the physiological need
(Scharnweber et al. 2021), which could potentially affect growth and
fitness (Scharnweber and G\selectlanguage{ngerman}årdmark 2020, Závorka et al. 2022).

A reduction of energy at the base of the food web was further translated
up the food chain, thus it most likely reduced the condition in juvenile
and adult pike, which is the most abundant top predator in this system.
Juvenile pike in their first year caught in high-iron sections were on
average 9.9 cm smaller compared to juveniles caught in sections of clear
water (data not shown). Turbidity in the high-iron sections may affect
the foraging abilities of pike as a visual predator by altering the
reactive distance to the prey fish, with consequences for attack rates
and the amount of consumed prey (Jacobsen and Engström-Öst 2018).
However, pike might compensate low visibility by other sensory modes,
such as the ones from the lateral line (Raat 1988). Instead, it can
reasonably be assumed that the differences in total length of juvenile
pike is rather caused by the overall low availability of food (i.e., low
abundances of prey fish) and likely the limited aquatic vegetation,
which is important as habitat for the juvenile pike (Jacobsen and
Engström-Öst 2018). Unfortunately, low sample sizes of adult pike
individuals did not allow further comparisons of cohorts, but it can be
assumed that pike of all cohorts living in high-iron sections of the
Spree River have an overall lower fitness. Our results thus highlight
the broad impact of iron load on each level of the aquatic food web. The
results presented here, however, represent a single timepoint of the
year. We would like to emphasize that longer studies including the
seasonal and temporal variances of abundances and fish resource use are
needed to draw more general conclusions on the post-mining effects on
the lotic food webs.

Besides the overall low availability of aquatic prey, other factors may
add to the detrimental effects of iron (III) for fish communities. For
example, iron could directly impact fishes, as precipitates can clog
gills and eggs (Andersson and Nyberg 1984, Gerhardt 1992), but further
indirect effects are possible. The growth of submerged macrophytes, for
instance, can be hampered under brown water conditions (Blanchet et al.
2022). Macrophytes provide a complex habitat, favoring fish and
invertebrate diversity and richness simultaneously (Soukup et al. 2022).
Certainly, more studies are needed to understand the underlying and
potentially multifactorial causes for the low fish abundance found in
this study.

Intensive land-use as from mining activities may not be the only
human-induced perturbation acting on the river food web, as multiple
stressors often act jointly (Sabater et al. 2019). The modification of
the river flow via the building of dams poses one of the biggest impacts
on rivers across the globe (e.g., Dudgeon et al. 2006, Schmutz and Moog
2018). The disruption of the river flow by damming and the formation of
a reservoir is a drastic process, which will alter many aspects of the
ecosystems, e.g. fish migration and the transport of nutrients. An
additional factor may occur from the modification of the downstream
thermal regime, which depends on the mode of operation and specific
mechanism of the water release from the reservoir (Olden and Naiman
2010). While many large dams may release cold water from below the
thermocline of the reservoir, more shallow reservoirs may alter the
downstream thermal regime by warming with profound impact on metabolic
rates, physiology and life-history traits of aquatic species (Lessard
and Hayes 2003, Olden and Naiman 2010). The rather shallow Spremberg
reservoir releases the water from the bottom, but the reservoir itself
is usually not stratified (data not shown). In the study presented here,
the river water downstream of the dam was generally warmer during
summertime, but colder during wintertime (Appendix 3). This indicates
that the Spremberg Reservoir severely alters the thermal regime further
downstream. This pattern could potentially affect the abundance of
invertebrates and fish, and further the growth of pike. The study set-up
does not allow for the differentiation between the effects of iron
flocculation and the changes of the water temperature downstream of the
dam and further studies are need to investigate the cumulative stressor
of human-induced perturbations on our river systems.

\emph{Conclusion}

In this study, we show how land use, i.e., lignite mining activities and
the resulting iron flocculation in lotic systems may decrease benthic
invertebrate and fish biomass. In addition, we further were able to
demonstrate the potential of mining activities to restructure aquatic
food webs, forcing the fish community to an increased reliance on
terrestrial insects. Therefore, mining activities represent an
environmental perturbation that can alter the flow of energy between
aquatic and terrestrial systems. Another human-induced impact that could
potentially act in a similar way is the one of the installations of
artificial light near a waterbody which could attract more insects to be
closer to the water (Perkin et al. 2011), but empirical evidence is
missing. We therefore agree with Soininen et al. (2015) and argue to
incorporate a holistic and unifying view of ecology on the landscape
level when considering the effects of human-induced environmental change
on our ecosystems.

\textbf{Acknowledgements}

We are grateful to for the valuable help of Marius Hennicke and Frank
Weichler in the field and Fabienne Pritsch in the lab. Furthermore, we
thank Florian Jeltsch for his support of this project.

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hydrogen exchange. Limnology and Oceanography-Methods\textbf{13}
:213-223.

Závorka, L., A. Blanco, F. Chaguaceda, J. Cucherousset, S. S. Killen, C.
Liénart, M. Mathieu-Resuge, P. N\selectlanguage{polish}ě\selectlanguage{english}mec, M. Pilecky, K. Scharnweber, C. W.
Twining, and M. J. Kainz. 2022. The role of vital dietary biomolecules
in eco-evo-devo dynamics. Trends in Ecology \& Evolution.

\textbf{Tables}

\textbf{Table 1:} Description of the field sites. Values for total iron
(TFe), total nitrogen (TN), total phosphorus (TP) and turbidity
represent averages of monthly water samples between April and August
2021. FNU: Formazine Turbidity Unit. Depth near shore indicates the
steepness of the shoreline.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
\textbf{Location} & \textbf{Site} & \textbf{location} & \textbf{TFe (mg
L \textsuperscript{-1})} & \textbf{TN (mg L \textsuperscript{-1)}} &
\selectlanguage{greek}\textbf{TP (µg L \textsuperscript{-1)}}\selectlanguage{english} & \textbf{Wetted with (m)} &
\textbf{Turbidity (FNU)} & \textbf{Depth near shore (m)} & \textbf{Flow
velocity (m s\textsuperscript{-1})} & \textbf{Primary substrate} &
\textbf{Aquatic vegetation}\tabularnewline
\midrule
\endhead
upstream of the dam & Zerre & above dam & 4.1 & 1.6 & 29.2 & 22 & 22.2 &
1.5 & 0.2 -0.5 & gravel & infrequent\tabularnewline
& Wilhelmstal & above dam & 3.8 & 2.1 & 38.2 & 22 & 37.8 & 1.1 & 0.2
-0.5 & gravel & infrequent\tabularnewline
& Trattendorf & above dam & - & - & - & 24 & - & 1.4 & 0.2 -0.5 & sand &
frequent\tabularnewline
downstream of the dam & Br\selectlanguage{ngerman}äsinchen & below dam & 0.3 & 1.7 & 17.2 & 28 &
3.1 & 1.7 & 0.2 -0.5 & sand & frequent\tabularnewline
& Frauendorf & below dam & - & - & - & 28 & - & 1.2 & 0.2 -0.5 & gravel
& infrequent\tabularnewline
& Madlow & below dam & - & - & - & 29 & - & 1.2 & 0.2 -0.5 & gravel &
infrequent\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2:} Overview on the number of ingested aquatic and
terrestrial species identified using metabarcoding of gut content of
perch, bleak, and roach caught in river sections a) upstream of the dam
(i.e., high iron concentrations); and b) downstream of the dam (i.e. low
iron concentrations). See Appendix 1 for an extended list of the
different species.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& \textbf{Number of species identified} \selectlanguage{ngerman}\textbf{(average ± standard
deviation)} & \textbf{Number of species identified} \selectlanguage{ngerman}\textbf{(average ±
standard deviation)} & \textbf{Number of species identified}
\selectlanguage{ngerman}\textbf{(average ± standard deviation)}\tabularnewline
\midrule
\endhead
& \textbf{Perch} & \textbf{Bleak} & \textbf{Roach}\tabularnewline
upstream of the dam & & &\tabularnewline
aquatic species & 3 \selectlanguage{ngerman}± 1 & 1 \selectlanguage{ngerman}± 1 & 2 \selectlanguage{ngerman}± 1\tabularnewline
terrestrial species & 4 \selectlanguage{ngerman}± 1 & 1 \selectlanguage{ngerman}± 1 & 0\tabularnewline
downstream of the dam & & &\tabularnewline
aquatic species & 3 \selectlanguage{ngerman}± 3 & 2 \selectlanguage{ngerman}± 1 & 4 \selectlanguage{ngerman}± 2\tabularnewline
terrestrial species & 0 & 0 & 0\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure legends}

\textbf{Figure 1:} Map of study area, including the location of sampling
sites. Photos showing the Spremberg Reservoir with inflow of brown,
iron-rich water (upper photo), and the dam with outflow of clear water
(lower photo). Red arrow depicts flow direction. Photos were taken in
2020 and 2021 and kindly supplied by the Lausitzer und Mitteldeutsche
Bergbauverwaltungsgesellschaft mbH, Senftenberg/Germany.

\textbf{Figure 2:} Abundance of benthic macroinvertebrates (individuals
m\textsuperscript{-2}) collected at the three locations upstream (i.e.,
high iron concentrations) and the three locations downstream (i.e., low
iron concentrations) of the dam. See Appendix 1 for a list of
invertebrate taxa found at each site.

\textbf{Figure 3:} a) Abundance (represented as catch per unit effort,
CPUE, individuals 1000 m\textsuperscript{-1}) and b) biomass of fish
species (represented as biomass per unit effort, BPUE, g 1000
m\textsuperscript{-1}) collected at the three locations upstream (i.e.,
high iron concentrations) and the three locations downstream (i.e., low
iron concentrations) of the dam. See Appendix 2 for a list of species
abundances at each site.

\textbf{Figure 4} : Contribution of aquatic and terrestrial resources to
the diet of the three dominant fish species (roach, perch, and bleak),
as depicted by the terrestrial index of fishes. A higher index indicates
higher contribution of terrestrial resources to the diet. Boxplots
indicate medians, with whiskers expanding to the 25th and 75th
percentile. Dots depict outliers and asterixes level of significance
(***:P [?] 0.001; **: P [?] 0.01).

\textbf{Figure 1}\selectlanguage{english}
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\textbf{Figure 4}\selectlanguage{english}
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