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\begin{document}

\title{Effects of hydropeaking on drift, stranding and community composition of
macroinvertebrates: a field experimental approach in three regulated
Swiss rivers}



\author[1]{Diego Tonolla}%
\author[2]{Florian Dossi}%
\author[1]{Olivier Kastenhofer}%
\author[1]{Michael Doering}%
\author[2]{Christoph Hauer}%
\author[2]{Wolfram Graf}%
\author[2]{Lisa  Schülting}%
\affil[1]{Zurcher Hochschule fur Angewandte Wissenschaften}%
\affil[2]{Universitat fur Bodenkultur Wien}%


\vspace{-1em}



  
  \date{June 14, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Hydropeaking operation leads to fluctuations in wetted area between base
and peak flow and increases discharge-related hydraulic forces (e.g.,
flow velocity). These processes promote macroinvertebrate drift and
stranding, often affecting benthic abundance and biomass. Our field
experimental study -- conducted in three hydropeaking-regulated Swiss
rivers -- aimed to quantify (i) the short-term effects of the combined
increase in flow amplitude and up-ramping rate based on
macroinvertebrate drift and stranding, as well as (ii) long-term effects
based on the established community composition. Hydropeaking led to
increased macroinvertebrate drift compared to base flow and to
unaffected residual flow reaches. Moreover, stranding of
macroinvertebrates was positively related to drift, especially during
the up-ramping phase. Flow velocity and up-ramping rate were identified
as major determinants for macroinvertebrate drift, while flow ratio and
down-ramping rate for stranding. Particularly high sensitivity towards
HP was found for Limnephilidae, whereas Heptageniidae seemed to be
resistant in respect to short and long-term hydropeaking effects. In the
long-term, hydropeaking did not considerably reduce benthic density of
most taxa, especially of some highly resistant and resilient taxa such
as Chironomidae and Baetidae, which dominated the community composition
even though they showed comparably high drift and stranding responses.
Therefore, we argue that high passive drift and/or stranding, especially
of individual-rich taxa, does not necessarily indicate strong
hydropeaking sensitivity. Finally, our results demonstrate the necessity
to consider the differences in river-specific morphological complexity
and hydropeaking intensity, since these factors strongly influence the
community composition and short-term drift and stranding response of
macroinvertebrates to hydropower pressure.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

High-head storage hydropower plants often have detrimental ecological
impacts on downstream river ecosystems because of their high-volatile
electricity production, which cause severe daily and sub-daily
downstream fluctuations in discharge and water levels due to so-called
`hydropeaking' (hereafter, HP; Bruder et al., 2016; Young, Cech, \&
Thompson, 2011). Those hydropower operations influence the flow regime
not only during peak flow. Due to the temporal water storage in the
reservoir, the river segment located between storage and hydropower
plant experiences relatively constant base-flow conditions, so-called
residual flow (hereafter, RF). An increase of 1.2 TWh is forecasted by
2050 in the energy storage capability of the Swiss hydropower
reservoirs, corresponding to 20\% of today's energy storage capacity
(Boes et al., 2021). Consequently, further ecological impacts within
riverine ecosystems are expected with an increase in HP operation.

In alpine rivers, HP often occurs in high frequency and its intensity
can be characterized by several hydrological parameters (Greimel et al.,
2016; Li \& Pasternack, 2021). HP operation in the up-ramping phase
often leads to abrupt increase in discharge and related hydraulic forces
(e.g., flow velocity, bed shear stress). This can promote unintentional
and mechanical dislodgement of aquatic macroinvertebrates (hereafter,
MIV) from the substrate and their downstream transport in the water
column until passive (e.g., in hydraulic dead zones) or active
(behavioral or through morphological adaptations) exit of the drift
(Ciborowski, 1987; Naman, Rosenfeld, \& Richardson, 2016). The causes
for MIV drift by HP operation are manifold and complex and often
strongly taxon- and trait-specific (e.g., Kj\selectlanguage{ngerman}ærstad, Arnekleiv, Speed, \&
Herland, 2018; Leitner, Hauer, \& Graf, 2017). The following parameters
are of major importance regarding HP-induced passive MIV drift:
up-ramping rate, flow magnitude and amplitude, flow velocity, and to a
lesser extent also Froude number and bed shear stress (Bruno, Cashman,
Maiolini, Biffi, \& Zolezzi, 2016; Bruno, Maiolini, Carolli, \& Silveri,
2010; Gibbins, Vericat, Batalla, \& Buendia, 2016; Imbert \& Perry,
2000; Miller \& Judson, 2014; Schülting, Feld, Zeiringer, Hu\selectlanguage{polish}đ\selectlanguage{english}ek, \&
Graf, 2018a; Timusk, Smokorowski, \& Jones, 2016). Moreover, sudden
variations in water temperature (i.e., thermopeaking) may induce
behavioral MIV drift (Bruno, Siviglia, Carolli, \& Maiolini, 2013;
Sch\selectlanguage{ngerman}ülting, Feld, \& Graf, 2016), and re-suspension of fine sediments
increases turbidity and clogging risk (Hauer, Holzapfel, Tonolla,
Habersack, \& Zolezzi, 2019) further affecting MIV (Bo, Fenoglio,
Malacarne, Pessino, \& Sgariboldi, 2007; Crosa, Castelli, Gentili, \&
Espa, 2010; Jones et al., 2012). In addition to drift, stranding is a
possible consequence of HP operations. MIV stranding has been
documented, also if not unequivocally, to be related to down-ramping
rate, flow magnitude, amplitude and rate, flow velocity and the
extension of the dewatering area (Kroger, 1973; Perry \& Perry, 1986;
Tanno, Wächter, \& Gerber, 2021; Tanno, Wächter, \& Schmidlin, 2016).
Tanno et al. (2021) further found a positive correlation between MIV
drift and stranding. In a similar vein as for drift, water temperature
and turbidity are probably likely to influence stranding. For example,
water temperature has been shown to influence stranding risk of fish
(e.g., Halleraker et al., 2003). Besides the short-term effects of MIV
drift and stranding, long-term effects of altered hydromorphological
habitat conditions in HP rivers are reported to affect colonization
patterns of benthic populations (Bretschko \& Moog, 1990; Cushman, 1985;
Kjærstad et al., 2018). Morphological heterogeneity, for instance, is
known to be crucial in providing diverse habitats and refuges for MIV
communities under HP conditions (Hauer, Holzapfel, Leitner, \& Graf,
2017). The complex interplay of drift, stranding and altered
hydromorphological habitat conditions caused by HP operation, most
likely contribute to reductions in MIV abundance and biomasses
(Céréghino, Cugny, \& Lavandier, 2002; Céréghino \& Lavandier, 1998;
Elgueta et al., 2021; Leitner et al., 2017; Moog, 1993). Another
possible consequence is the alteration of the MIV community composition
due to the evolution of specific behavioral (e.g., mobility, ability to
regain a foothold, sinking postures) and morphological (e.g., body
shape, hooks) traits as well as life history strategies. HP may, for
example, contributes to a selection of rheobiont and rheophilic taxa
(Bretschko \& Moog, 1990; Cushman, 1985; Ruhi, Dong, McDaniel, Batzer,
\& Sabo, 2018) against limnophilic taxa or of taxa associated with
lentic and substrate surface areas (Graf et al., 2013; Leitner et al.,
2017; Ruhi et al., 2018; Schülting et al., 2018a; Schülting, Dossi,
Graf, \& Tonolla, 2021). Community changes due to HP may consequently
affect the local food web structure and the ecological functioning of
river systems (e.g., Holzapfel, Leitner, Habersack, Graf, \& Hauer,
2017; Pearce et al., 2019).

The complex interactions between MIV drift, stranding and established
benthic community remain poorly understood, and field studies are rare
(but see for example Miller \& Judson, 2014; Tanno et al., 2021; Timusk
et al., 2016). Such a comprehensive evaluation is yet of importance for
an exhaustive understanding of HP-related impacts on MIV and finally for
implementation of associated mitigation measures and sustainable
operation of HP power plants (Bruder et al., 2016; Moreira et al., 2019;
Tonolla, Bruder, \& Schweizer, 2017).

The main goal of our field experimental study -- conducted in three
HP-regulated Swiss rivers -- was therefore to quantify the short-term
effects of three HP scenarios with different intensity (simulated
through an increase in flow amplitude and up-ramping rate) on MIV drift
and stranding in addition to the long-term HP effects on the established
community composition. As comparison, MIV drift and community
composition was quantified in HP-unaffected RF reaches upstream. We
hypothesize that: (i) drift increases during the HP scenarios compared
to base flow and to drift in the RF reaches as well as a positive
relationship between drift and stranding; (ii) HP-intensity and
associated hydraulic forces, as summarized by pre-selected environmental
variables, can explain any observed differences in drift and stranding
propensity; (iii) compared to the RF reaches with almost constant
hydrological conditions, flow fluctuations due to HP operation lead to
lower benthic densities and a different MIV community composition,
linked to drift and stranding. Finally, we expected taxa-, trait- and
river-specific patterns.

\section*{Material and methods}

{\label{material-and-methods}}

Study sites and hydropower facilities

The study was conducted in three HP impacted river-reaches at the rivers
Sitter (hydropower plant Kubel, KUB; canton of St. Gallen), Hasliaare
(hydropower plant Innertkirchen, KWO; canton of Bern) and Linth
(hydropower plant Linth-Limmern, KLL; canton of Glarus; Figure 1).
Additionally, RF reaches located closely upstream of the hydropower
plants were used as hydrological comparisons with relatively constant
base-flow conditions.

All HP and RF reaches are of comparable size (Strahler order 5 -- 6;
natural mean annual discharge 11 -- 32 m\textsuperscript{3}/s) and
located at similar elevations (590 -- 680 m a.s.l.). Dominant grain size
(cover \textgreater{} 50\%) in all HP reaches is micro- to mesolithal
(grain size 2 -- 20 cm), whereas it is coarser (\textgreater{} 20 cm) in
the RF reaches of the Sitter and Linth.

The KUB hydropower plant has the lowest maximum turbinable peak flow (18
m\textsuperscript{3}/s), flow amplitude (16 m\textsuperscript{3}/s) and
ramping rates (0.1 and 0.05 m\textsuperscript{3}/s min). The
investigated HP reach was located \textless{} 0.5 km downstream of the
plant release and is characterized by the highest bankfull width (45 m),
a near-natural morphology with a right-sided gravel bar and comparably
fine substrate (D\textsubscript{m} = 28.1 mm). Since 2016, the
hydropower facilities KWO and KLL both include a HP retention volume of
80,000 m\textsuperscript{3} and 220,000 m\textsuperscript{3},
respectively. The maximum turbinable peak flow, amplitude and ramping
rates of the corresponding hydropower plant are higher than at KUB. The
investigated HP and RF reaches of the Hasliaare and Linth are
characterized by a strongly constrained morphology. The HP reach of the
Linth has the lowest bankfull width (15 m), no extended gravel bars, and
the coarser sediment size (D\textsubscript{m} = 59.9 mm). Moreover, four
small run-off power plants are situated between the water release and
the HP reach. The riverbed of all HP reaches showed no or insignificant
clogging, whereas it was moderate in the RF reaches of the Sitter and
Linth. The HP and RF reaches of these two rivers were also characterized
by higher cover of algae and mosses than the Hasliaare. Possible
relevance of these aspects on drift, stranding and the benthic community
composition, will be picked up in the discussion. Key characteristics of
the hydropower plants, as well as associated catchments, HP and RF
reaches are available in Data S1 (Appendix A).

Experimental setup

To investigate the short- and long-term effects of HP on MIV, we defined
three HP scenarios for each HP reach and requested respective
experimental flow releases from the hydropower plant operators. We
sampled benthic, drifted and stranded MIV and recorded several
environmental variables (see below) over a four-week period between
March 15\textsuperscript{th} and April 8\textsuperscript{th} 2018. In
each HP and RF reach we defined three sampling sites spaced by 10 -- 50
m (Figure 2a).

HP scenarios

The HP scenarios consisted of the combined increase of flow amplitude
and up-ramping rate (Figure 2c and Table 1). To reduce impacts on
hydropower flexibility and production, in each river all field
experiments were carried out over two consecutive days. The first day,
we targeted a combination of three flow amplitudes and three up-ramping
rates. The same three scenarios were repeated the next day but the drift
nets were placed further towards the river center to achieve higher peak
flow velocities as at day one (Figure 2a and Table 1). The selection of
the targeted up-ramping rates (1, 2 and 3 cm/min) and peak flow
velocities (0.4 and 0.8 m/s) was based on the results of previous flume
experiments, which showed distinct drift responses in these ranges
(Schülting et al., 2018b). In terms to achieve the targeted HP scenarios
and to define appropriate sampling sites, for each HP reach we set up
and calibrated 2D unsteady hydrodynamic-numerical models to pre-define
the operation mode of each hydropower plant (KUB) or retention basin
(KWO, KLL).

In contrast to KUB and KWO, at KLL the maximum turbinable peak flow had
to be kept constant to avoid flow velocities \textgreater{} 1 m/s and
thus ensure wadable conditions. With this limited discharge setting, for
the Linth, it was also necessary to define an alternative HP scenario
with a reduced up-ramping rate (0.5 cm/min) as for the Sitter and
Hasliaare. Moreover, due to different hydrological conditions (e.g.,
melting snow, rain) and operational characteristics of the hydropower
plants and/or retention basins (e.g., turbine type, concessional
limitations), base flow and down-ramping rates varied between the HP
reaches. These constraints resulted in some differences between the
targeted (modeled) and the measured flow amplitudes, peak flow
velocities and up-ramping rates (Appendix B in Data S1). Nevertheless,
measured flow amplitudes in the Sitter and Hasliaare increased
approximately twofold respectively threefold from HP scenario 1 (SC1) to
SC2 and ca. fourfold respectively five-sixfold from SC1 to SC3, whereas
it remained almost constant in the Linth (Figure 2c and Table 1). An
increase in mean and maximum up-ramping rate from SC1 to SC2 and to SC3
in addition to a higher peak flow velocity at day two was also achieved
but varied between HP reaches. Mean up-ramping rate between SC1 and SC3
increased 2.5 times in the Sitter, 1.5 to 4 times in the Hasliaare and
1.5 to 2 times in the Linth. From day one to day two, peak flow velocity
over all scenarios increased in average 4 times in the Sitter, 0.25
times in the Hasliaare and 0.15 times in the Linth (Table 1).

MIV sampling

The benthic community was sampled during base flow with Hess samplers
(sampling area 0.045 m\textsuperscript{2}, mesh size 500 \selectlanguage{greek}µ\selectlanguage{english}m,) 2 -- 5 m
upstream of each drift net. At each sampling site, three benthic samples
were collected transect-wise (from the bank margin to the in-stream
area) along a flow velocity gradient to include taxa of different
current preferences (Figure 2a). Benthic samples in the HP reaches were
collected four times a day (before each HP scenario and at the end of
the day; Figure 2b), whereas they were taken twice a day (beginning and
end of the day) in the RF reaches.

Drifting MIV were collected at each sampling site with drift nets (frame
size 53 x 27 cm, mesh size 500 \selectlanguage{greek}µ\selectlanguage{english}m) fixed with thick rebars on both
sides. The long side of each net was placed on the riverbed. Samples in
the HP reaches were taken separately (nets exchanged) during base flow
(exposure time 60 min) and during each HP phase: up-ramping (UR), first
and second part of the peak (P1, P2; each 30 min) and down-ramping (DR;
Figure 2b). In the RF reaches, MIV drift was sampled for approximately
60 min, simultaneously with the induced peak flows in the HP reaches.

Stranding MIV were collected according to Tanno et al. (2016). Four flat
nets (net size 50 x 50 cm, mesh size 500 \selectlanguage{greek}µ\selectlanguage{english}m) were installed at each
sampling site nearby the drift nets on the dewatering area (temporally
wetted area, dry at base flow and wetted during peak flow; Figure 2a):
two nets directly at the waterline and two nets 1 m away from the
waterline and 0.5 m offset from the other nets. Ten stones of the
dominant grain size were placed on each flat net to increase roughness
and thus better mimic the substrate condition of the dewatering area.
Stranded MIV on the nets and on the stones were collected after each HP
scenario (Figure 2b). In the RF reaches, stranding was not assessed, due
to stable flow conditions.

MIV samples treatment and identification of taxa

In total, 864 samples were collected, fixed with 90\% ethanol in the
field and then transported to the laboratory. MIV were separated from
other organic and inorganic material. The individuals were sorted and
identified to family or, in some cases, to lower taxonomic levels (i.e.,
Oligochaeta, Hydracarina, Lepidoptera) following the identification key
of Tachet, Richoux, Bournaud and Usseglio-Polatera (2000) and were then
counted (Appendix C in Data S1). Pupae, adult individuals in a
non-aquatic life stage and terrestrial invertebrates were excluded.

Sampling of environmental variables

At each drift net, flow velocity and water depth were measured three
times during base flow and three times each during the P1 and P2 phase.
The values of the three measurements were then averaged. Flow velocity
was recorded using a micro propeller (Flowatch Flowmeter) 2 cm above the
substrate surface (\emph{v\textsubscript{0}} ), at the net-center
approximately 13 cm from the substrate surface
(\emph{v\textsubscript{net}} ) and 6 cm below the water surface
(\emph{v\textsubscript{100}} ). Water depth was measured in front of
each net with a rigid meter. Concurrently, turbidity as NTU
(Nephelometric Turbidity Unit) was measured at the drift net of the
central sampling site using a portable turbidity meter (Hach Lange
2100Q). Additionally, the extension of the dewatering area
(\emph{DA\textsubscript{ex}} ) as distance between the waterline at base
flow (\emph{Q\textsubscript{base}} ) and at peak flow
(\emph{Q\textsubscript{peak}} ; i.e., flow magnitude) was measured
nearby the stranding nets.

Water temperature (\emph{T} ) and water level were continuously recorded
(1 min interval) using one logger (Vemco Minilog II-T, AMIRIX Systems
Inc.) and one pressure probe (DCX-22 SG/VG CTD, Keller), respectively,
placed at the upstream sampling site of each HP reach. Based on the
water level measurements, the mean and maximum up-
(\emph{UR\textsubscript{mean}} , \emph{UR} \textsubscript{max}) and
down-ramping rates (\emph{DR\textsubscript{mean}} ,\emph{DR}
\textsubscript{max}) were determined. Discharge data were obtained from
federal and cantonal gauging stations as well as from the operators of
the hydropower plants and were used to
determine\emph{Q\textsubscript{base}} , \emph{Q\textsubscript{peak}} ,
flow amplitude (\emph{Q\textsubscript{peak} - Q\textsubscript{base}} )
and ratio (\emph{Q\textsubscript{peak}/Q\textsubscript{base}} ). The
grain size distribution (GSD) along gravel bars of each HP reach was
determined by a representative number of line-by-number analyses (n = 1
-- 3) using a gravelometer. GSD curves were determined and analyzed,
taking particle values as `percent finer'. Froude number (\emph{Fr} )
and bed shear stress (\selectlanguage{greek}\emph{τ\textsubscript{b}}\selectlanguage{english} ) according to Whiting
and Dietrich (1990) were calculated for each HP sampling site.

Data analysis

Data analysis were either based on the total community or at taxa-level.
To ensure consistency and to down-weight the influence of rare taxa,
only taxa with a relative abundance \textgreater{} 1\% over all drift,
stranding or benthic samples, were selected to express taxa-specific
responses (Appendix C in Data S1). Additionally, to assess
trait-specific responses, we classified taxa found in the benthic
samples based on two relevant ecological trait categories related to HP
sensitivity (RHEOPHYLAX, 2021; Sch\selectlanguage{ngerman}ülting et al., 2021): `hydraulic
habitat preference' (classes: lentic/lotic; indicating taxa adaptation
to low or high current) and `vertical habitat preference' (classes:
interstitial/surface; indicating taxa flow exposure; Appendix C in Data
S1).

Benthic community composition and stranding were analyzed in terms of
density, expressed as the number of individuals standardized by the
sampled area (ind./m\textsuperscript{2}). Single samples were pooled per
sampling site (Figure 2a) and chronological sampling (Figure 2b),
resulting in 24 and 18 composite samples respectively for each HP reach
as well as 12 benthic composite samples for each RF reach.

MIV drift intensity was calculated following Pegel (1980) as the number
of individuals in the drift standardized by the drift-net area and the
exposure time (ind./m\textsuperscript{2}min). To consider the effect of
an entire HP scenario, drift samples collected during the UR, P1, P2 and
DR phase were pooled per sampling site and HP scenario. For each HP and
RF reach this resulted in 18 composite samples each for base flow and HP
scenario. Further, since MIV drift patterns, and probably also stranding
patterns, are strongly related to the benthic source population, we
calculated the drift and stranding propensity (e.g., Bruno et al., 2013;
Timusk et al., 2016) as the drift intensity and stranding density,
respectively, standardized by the benthic density. All benthic samples
were pooled per HP reach to generate a single estimate of benthic
density at the reach scale.

Statistical analyses

Canonical Correspondence Analyses (CCA) were used to explore differences
in drift and stranding propensity, respectively, among the three HP
reaches and to assess their correlation with a pre-selection of
explanatory environmental variables (based on the state of knowledge,
see introduction). Drift: \selectlanguage{greek}\emph{Q\textsubscript{peak},
Q\textsubscript{peak} - Q\textsubscript{base}, UR\textsubscript{mean},
UR\textsubscript{max}, v\textsubscript{0}, v\textsubscript{net},
v\textsubscript{100}, τ\textsubscript{b}, Fr, T, NTU}\selectlanguage{english} .
Stranding:\emph{Q\textsubscript{peak},
Q\textsubscript{peak}/Q\textsubscript{base}, DR\textsubscript{mean},
DR\textsubscript{max}, v\textsubscript{0}, v\textsubscript{net},
v\textsubscript{100}, DA\textsubscript{ex}, T, NTU} . Drift and
stranding propensity, respectively, were used as response variable.
Environmental variables were log-transformed (x + 1) and then
sequentially checked for variance inflation factor (vif), while
variables exceeding a vif of five were removed prior to the CCA
modeling. The final selection of vif validated variables was based on a
stepwise model selection (permutational forward and backward) based on
the Akaike information criterion (AIC). Environmental variables that
were selected in both approaches were used for the final CCA model
construction. The CCA model was then tested for total significance,
significant axes and significant terms via PERMANOVAs. An additional
factor fitting was performed to assess statistical differences in drift
and stranding propensity, respectively, between HP reaches (Sitter
\emph{vs} Hasliaare \emph{vs}Linth), sampling day (day 1 \emph{vs} day
2; corresponding to higher peak flow velocities at day 2, see Table 1)
and HP scenario (SC 1\emph{vs} SC2 \emph{vs} SC3).

Non-metric multidimensional scaling (NMDS) was used to explore
differences in the benthic community compositions among the different HP
and RF reaches. Benthic densities were used to create a resemblance
matrix based on the Bray-Curtis similarity index. Permutational
multivariate analysis of variance (PERMANOVA) with a similarity
percentage (SIMPER) analysis were performed to assess for significant
differences in the benthic community composition among reaches and to
identify the taxa that most contributed to dissimilarity.

In addition to CCA and NMDS, non-parametric Mann-Whitney or
Kruskal-Wallis tests followed by pairwise post hoc tests, and linear
regression models were applied for specific analyses. CCA and NMDS
analyses were performed using the software R version 4.1.1 (R Core Team,
2021) with the package `vegan' (Oksanen et al., 2019). Non-parametric
tests and linear regressions were performed using the software SPSS
Statistics version 27.0 and SigmaPlot version 12.5.

\section*{Results}

{\label{results}}

Short-term response of macroinvertebrate (drift, stranding) to
hydropeaking

Across all reaches, drift intensity was 2.5 times higher during HP (12.5
\selectlanguage{ngerman}± 0.9 ind./m\textsuperscript{2}min) compared to base flow (4.9 ± 0.8
ind./m\textsuperscript{2}min, p\textless{} 0.001) and approximately 1.5
times higher than in the RF reaches (7.4 ± 0.8
ind./m\textsuperscript{2}min, p \textless{} 0.001, `ALL' in Figure 3).
Drift intensity in the RF reaches was also significantly higher than at
base flow in the HP reaches (p \textless{} 0.05). At the river level,
significantly higher drift intensities were found during HP compared to
base flow of the Sitter (p \textless{} 0.01), Hasliaare (p \textless{}
0.001) and Linth (p \textless{} 0.01) as well as compared to the RF
reaches of the Sitter (p \textless{} 0.001) and Hasliaare (p \textless{}
0.01). The Linth showed a different pattern with higher drift intensity
in the RF reach compared to HP (p = 0.06) and to base flow (p
\textless{} 0.001).

Across all HP reaches, drift intensities during all HP phases (UR, P1,
P2, DR) were significantly higher than during base flow (p \selectlanguage{english}[?] 0.001,
Appendix D in Data S1). Moreover, drift intensity in the UR phase was
significantly higher compared to the DR phase (p \textless{} 0.01). The
highest average drift intensity was found for the UR phase (17.1 +- 1.7
ind./m\textsuperscript{2}min) and the first part of the peak phase (P1;
13.7 +- 1.3 ind./m\textsuperscript{2}min) which showed drift intensities
3.5 times and approximately three times higher, respectively, than
during base flow (4.9 +- 0.8 ind./m\textsuperscript{2}min).

Across all HP reaches, and considering the entire HP scenario (i.e., UR,
P1, P2 and DR phase), stranding density was not significantly related to
drift intensity (`ALL' in Figure 4a). However, if considering only the
UR phase, a positive significant relationship was found (R = 0.368, p
\textless{} 0.01, `ALL' in Figure 4b). At the reach level, a positive
significant relationship was found in the Hasliaare and in the Sitter
(but only for the UR phase) but not in Linth. In general, stranding was
much less pronounced than drift and many stranding samples contained
only few individuals (Appendix C in Data S1).

The CCA analyses revealed that taxa drift and stranding composition
significantly differed between the three rivers (drift:
r\textsuperscript{2} = 0.23, p \textless{} 0.01; stranding:
r\textsuperscript{2} = 0.32, p \textless{} 0.01; Figure 5).
Additionally, but to a lesser extent, the lateral sampling location (day
1 \emph{vs} day 2: higher peak flow velocities at day 2, see Table 1;
drift: r\textsuperscript{2} = 0.16, p \textless{} 0.01; stranding:
r\textsuperscript{2} = 0.27, p \textless{} 0.01) significantly
contributed to the differences. Drift composition in the Sitter and
Linth were more similar than in the Hasliaare (Figure 5a, confidence
ellipses). The HP scenario also significantly contributed to the drift
(r\textsuperscript{2} = 0.16, p = 0.05) but not to the stranding
differences. 43.5\% and 40.8\% of the total variation of drift and
stranding taxa distributions, respectively, can be explained by the
selected environmental variables. The main environmental variables that
could affect the taxa drift propensity to the greatest extent were the
flow velocity near the surface (\emph{v\textsubscript{100}} ; F = 12.5,
p \textless{} 0.001), mean up-ramping rate
(\emph{UR\textsubscript{mean};} F = 10.4, p \textless{} 0.001),
turbidity (\emph{NTU} ; F = 6.4, p \textless{} 0.001), and water
temperature (\emph{T} ; F = 6.0, p \textless{} 0.001) which explained
8.0, 9.8, 9.0, and 8.5\% of the variation, respectively (Figure 5a).
Froude number (\emph{Fr} ) also contributed to the explained variation
(8.2\%) but not significantly (F = 1.7, p = 0.119). The main
environmental variables that could affect the taxa stranding propensity
to the greatest extent were the flow ratio
(\emph{Q\textsubscript{peak}/Q\textsubscript{base}} ; F = 9.7, p
\textless{} 0.001), max. down-ramping rate (\emph{DR\textsubscript{max}}
; F = 8.8, p \textless{} 0.001), water temperature (\emph{T} ; F = 5.5,
p \textless{} 0.001), turbidity (\emph{NTU} ; F = 4.1, p \textless{}
0.001) and flow velocity near the surface (\emph{v\textsubscript{100}} ;
F = 3.7, p \textless{} 0.01), which explained 7.3, 8.8, 8.2, 8.1, and
8.3 \% of the variation, respectively (Figure 5b).

Across all HP reaches, Rhyacophilidae and Limnephilidae showed high
propensity to drift and strand, whereas Oligochaeta and Heptageniidae
showed the lowest propensity (`ALL' in Table 2). Simulidae also showed
high propensity to drift and Perlodidae to strand. However, drift and
stranding propensity varied considerably between reaches. Limnephilidae
and Simuliidae showed considerable propensity to drift in all three HP
reaches, whereas Rhyacophilidae only in the Hasliaare. Limnephilidae,
Rhyacophilidae and Nemouridae showed considerable propensity to strand
only in the Hasliaare, whereas Empididae and Perlodidae in the Linth.
Almost all taxa showed highest propensity to drift and strand in the
Hasliaare and lowest propensity to drift and strand in the Sitter.

Long-term response of macroinvertebrate (density and community
composition) to hydropeaking

Average benthic density was five times higher in the Hasliaare RF reach
(1146 +- 229 ind./m\textsuperscript{2}) compared to the HP reach (227 +-
23 ind./m\textsuperscript{2}, p \textless{} 0.001), whereas in the
Sitter a contrasting trend was observed with average density
approximately 3.5 times higher in the HP reach (1838 +- 357
ind./m\textsuperscript{2}) than in the RF reach (493 +- 97
ind./m\textsuperscript{2}, p \textless{} 0.001, Figure 6). The density
variability of the Sitter HP samples was the largest of all reaches. No
significant differences were found for the Linth and considering all
reaches together (`ALL' in Figure 6).

The NMDS analysis showed that benthic community composition was
significantly different between the six reaches (p = 0.001), whereby it
was more similar in the three RF reaches than in the three HP reaches
(Figure 7, overlap \emph{vs} no overlap of the confidence ellipses;
lower R\textsuperscript{2} and F values in Appendix E in Data S1). The
Hasliaare showed the largest dissimilarity between RF and HP reaches,
whereas the other two rivers grouped more together, indicating more
considerable consistency in benthic community composition.

Four taxa (Heptageniidae, Chironomidae, Baetidae and Leuctridae)
remarkably contributed to differences in community composition among RF
and HP reaches of all three rivers (Table 3). Among these taxa,
Heptageniidae, Chironomidae and Baetidae in the Sitter, and
Chironomidae, Baetidae and Leuctridae in the Linth cumulatively
contributed for approximately 70\% of the dissimilarities. In the
Hasliaare two other taxa, Limnephilidae and Nemouridae, accounted for
41\% of the differences among RF and HP reaches, and Chironomidae
additionally contributed for approximately 15\%.

Across all reaches, Leuctridae, Nemouridae and Limnephilidae showed
significantly higher benthic densities in the RF than in the HP reaches
(p \textless{} 0.001, Appendix E in Data S1). In contrast, Simuliidae
showed significantly higher benthic densities in the HP reaches (p
\textless{} 0.05). However, differences between HP and RF reaches varied
widely among taxa and river (Appendix E in Data S1).

On average, lentic taxa were 5.5 times more abundant in the benthic
samples of the RF reaches (191.2 +- 47.7 ind./m\textsuperscript{2})
compared to the HP reaches (35.0 +- 6.1 ind./m\textsuperscript{2}; p
\textless{} 0.001), whereas lotic taxa were two times more abundant in
the HP reaches (437.8 +- 105.3 ind./m\textsuperscript{2}) than in the RF
reaches (222.2 +- 35.1 ind./m\textsuperscript{2}), but the difference was
not significant (`ALL' in Figure 8). Differences between RF and HP
reaches for taxa classified as surface dwelling or interstitial were
also not statistically different.

\section*{Discussion}

{\label{discussion}}

Considering expected future developments of the energy demand and the
concurrent progression of the implementation of climate change goals,
the need for energy storage capabilities such as hydropower reservoirs
will become more and more important in the near future (Boes et al.,
2021). As a result, an increase in HP operation thus poses a further
threat to physical habitats and biodiversity within riverine ecosystems,
highlighting the need for a prudent approach. Even though the mitigation
of HP impacts is a legal requirement in many countries (e.g., Swiss
Water Protection Act, European Water Framework Directive), the complex
interplay of HP-related impacts on MIV still remains poorly understood,
which complicates the implementation of efficient mitigation strategies.

Several studies suggest that river reaches affected by HP are
characterized by reduced benthic abundance, biomass and altered
community composition (Cereghino et al., 2002; Cereghino \& Lavandier,
1998;; Elgueta et al., 2021; Leitner et al., 2017; Moog, 1993), which in
consequence may have negative effects on metacommunity-wide functional
diversity (Kjaerstad et al., 2018; Ruhi et al., 2018). Within this field
experimental study -- conducted in three HP-regulated Swiss rivers -- we
could prove short-term (drift and stranding) and long-term (benthic
density, established community composition) effects of HP operation
within the investigated river reaches. Further, we could identify some
HP-related parameters most likely responsible for triggering active
and/or passive drift and stranding, as well as show that MIV response to
HP is highly taxa-, trait- and river-context dependent. In conclusion,
our study adds knowledge about effective implementation of mitigation
measures and more sustainable operation of HP power plants (Bruder et
al., 2016; Moreira et al., 2019; Smokorowski, 2021).

General response of macroinvertebrate to hydropeaking

Consistent with our hypotheses and in accordance to other published
literature (Aksamit, Carolli, Vanzo, Weber, \& Schmid, 2021; Bruno et
al., 2010, 2016; Imbert \& Perry, 2000; Miller \& Judson, 2014;
Schulting et al., 2018a, 2021; Timusk et al., 2016), HP generally led to
increased MIV drift intensities compared to base flow and to the
upstream-located RF reaches with highest drift during the up-ramping
phase and the first part of the peak flow. Furthermore, the analyzed
data partially supports the result of Tanno et al. (2021) that an
increased drift of MIV also increases their risk of stranding
(especially considering the UR phase). Comparison could be biased by the
low number of individuals found in our stranding samples as well as the
different equations used for drift intensity (our study) and drift
density (Tanno et al., 2021). The use of drift density as response
variable artificially decreases the effects of HP scenarios with higher
flow magnitude and corresponding flow velocities because MIV drift
mostly does not increase proportionally to the increase in water volume,
leading to a dilution effect (Naman, Rosenfeld, Richardson, \& Way,
2017; Pegel, 1980). This effect was already highlighted in the study of
Naman et al. (2017) who showed that the parameter `drift density'
negatively correlates with the investigated water volume, whereas `drift
flux' (i.e., total number of drifting invertebrates) showed opposite
patterns. We therefore decided to use the `drift intensity' (because not
affected by dilution effects) as a parameter for analyzing the effect of
field HP experiments with non-comparable flow conditions.

We found taxa- and river-specific long-term effects on the benthic
community under HP (discussed below). However, in contrast to our
hypothesis, considering all taxa and all study reaches together, we did
not detect a significant reduction in benthic density between RF and HP
reaches, even though drift and/or stranding were increased in all HP
reaches. We conclude that drift and stranding can contribute to a
reduction in benthic density of some taxa, as reported in several
studies (e.g., Cereghino \& Lavandier, 1998; Leitner et al., 2017; Moog,
1993), but most likely HP further induces organism stress and hampers
important processes such as feeding and reproduction. The combination of
the HP-induced effects on biota may in consequence lead to a thinning of
the benthic community and to a long-term change in species composition.

Effect of HP intensity and associated hydraulic forces on drift and
stranding

Flow velocity near the surface (\emph{v\textsubscript{100}} ) and mean
up-ramping rate (\emph{UR\textsubscript{mean}} ) were identified as
major determinants for macroinvertebrate drift propensity, indicating
that the flow magnitude and amplitude (leading to different flow
velocities) as well as the up-ramping rate are important HP-related
parameters. A strong relationship between drift and flow-related
hydraulic forces such as flow velocity is widely known (e.g., Gibbins,
2016). Recently, Schulting et al. (2021) found that discharge-related
parameters, such as mean-column flow velocity at peak flow, primarily
affect MIV drift and the importance of the up-ramping rate increases
only once certain discharge-related thresholds are exceeded. Following,
a reduction of the up-ramping rate may lead to a lower drift since the
MIV have more time to seek shelter in the interstices during flow
increase (Imbert \& Perry, 2000; Schulting et al., 2021; Timusk et al.,
2016).

We also found a positive link between down-ramping rate
(\emph{DR\textsubscript{max}} ) and stranding propensity, corresponding
to findings by Kroger (1973) and Perry \& Perry (1986). In line with
Tanno et al. (2016), a larger flow ratio
(\emph{Q\textsubscript{peak}/Q\textsubscript{base}} ) also led to higher
stranding, possibly due to increased drift and a larger wetted area.
Additionally, we showed that turbidity (\emph{NTU} ) and water
temperature (\emph{T} ) most likely influence drift and stranding risk.
However, this could also be an indirect effect as variations in water
temperature and turbidity are influenced by HP operation (e.g., flow
ratio). We conclude that the magnitude of drift and stranding propensity
is most likely affected by a combination of hydrological and hydraulic
factors as well as by their interaction (\emph{v\textsubscript{100}}
,\emph{UR\textsubscript{mean},
Q\textsubscript{peak}/Q\textsubscript{base}, DR\textsubscript{max}} )
and possibly by behavioral (\emph{NTU} , \emph{T} ) responses. Indeed,
thermopeaking has been shown to induce behavioral MIV drift of many taxa
and often has a synergic magnifying effect with the HP wave (e.g., Bruno
et al., 2013). Similarly, water temperature has been shown to influence
stranding risk of fish, with greatest effect in winter due to low
temperatures (e.g., Halleraker et al., 2003). HP-induced changes of
turbidity and their effects on MIV are poorly understood. Though,
re-mobilized and re-suspended fine sediments during peak flows are
mainly responsible for an increase in the river's turbidity and can
induce fine sediment infiltration and potential clogging (Hauer et al.,
2019), thereby exposing MIV to increased abrasion and reduced potential
interstitial habitats (Bo et al., 2007; Crosa et al., 2010; Jones et
al., 2012).

Taxa- and traits-specific responses to hydropeaking

Long-term HP effects were found only for a few taxa such as
Limnephilidae, Nemouridae and Leuctridae, which were abundant in the RF
reaches yet strongly reduced in the HP reaches. Limnephilidae further
showed short-term response reflected in their high propensity to drift
and strand. In contrast, Heptageniidae seemed to be more resistant in
respect to short and long-term HP effects.

Limnephilidae are adapted to slow flowing lentic habitats on the
substrate surface and, due to their size and shape, they experience high
drag (Rader, 1997). Moreover, for case-building Limnephilidae it has
been shown that an increased flow velocity decreases their ability to
return to the stream bottom from the drift (de Brouwer,
Besse-Lototskaya, ter Braak, Kraak, \& Verdonschot, 2017). Therefore,
this taxon is easily detached passively from its habitat and has poor
settling efficiency which might explain its particularly high
sensitivity towards short- and long-term HP effects. In line with these
observations, other studies suggest that HP often contributes to a
decrease in the density of limnophilic taxa associated to lentic
habitats and to strong drift (Graf et al., 2013; Leitner et al., 2017;
Schulting et al., 2021). Correspondingly, in our study, lentic taxa were
more abundant in the RF reaches compared to the HP reaches, confirming
the sensitivity of this trait to HP (Schulting et al., 2021).

Heptageniidae showed higher abundances in the benthic samples of the HP
reaches compared to the RF reaches (Appendix F in Data S1) and lowest
propensity to drift and strand. This emphasized the tolerance towards HP
of this family. Similar observations were reported by other authors
(e.g., Bruno et al., 2013; Graf et al., 2013; Moog, 1993, Schulting et
al., 2018a, 2021). Although different taxa within this family may show
varying responses towards HP, they generally prefer lotic habitats on
the substrate surface and are characterized by high agility and active
settling efficiency (Elliott, 1971) as well as by various morphological
adaptations that enhance attachment (streamlined body shape, sharp
tarsal crawls, sucker-like ventral gills) and reduce drag (Rader, 1997).
Nemouridae and Leuctridae did not show distinctively high drift and
stranding following HP, possibly due to relatively thin source
populations. Schulting et al. (2021) reported high drift induced by HP
for Nemouridae (\emph{Nemoura/Nemurella} sp.), whereas Leuctridae
(\emph{Leuctra} sp., a taxon associated with the interstices) showed
comparably low response, and this family is often not affected by HP
(e.g., Graf et al., 2013; Moog, 1993).

Simuliidae and Rhyacophilidae also showed high propensity to drift --
Rhyacophilidae also to strand -- but both families were more abundant in
the benthic samples of the HP reaches compared to the RF reaches. Both
taxa are known to prefer lotic habitats and they own adaptations (claws
and silk) to prevent passive drift (Rader, 1997). Nevertheless,
Simuliidae have been reported to often drift (Bruno et al., 2013, 2016;
Imbert \& Perry, 2000) and strand (Perry \& Perry, 1986; Tanno et al.,
2021) following HP. As surface-dwelling taxon, they are naturally
exposed to high flow, which can likely explain their high drift
propensity, even under natural conditions (Elliot, 1967). Rhyacophilidae
are generally associated to the interstices, but as predators, they may
also actively move on the surface seeking prey and are thus exposed to
abruptly increased hydraulic forces. The cold thermopeaking in the
Hasliaare (Figure 2c) might also explain the high (probably behavioral)
drift and stranding of Rhyacophilidae (Bruno et al., 2013) in this
reach. However, our experimental setup does not allow a separation of
active and passive drift. In conclusion, our data suggests that high
drift and/or stranding induced by HP flow fluctuations do not lead to
distinct density reductions of Simuliidae and Rhyacophilidae. Thus,
these taxa are less sensitive towards HP compared to Limnephilidae.

Taxa associated to lentic areas showed reduced benthic density compared
to lotic taxa. However, on the long-term, HP did not considerably reduce
benthic densities of most taxa, especially of Chironomidae and Baetidae.
Even though these taxa showed comparably high drift and stranding
responses (Appendix E and F in Data S1), they were also dominant in the
respective benthic samples. Matching our results, these two families are
often reported as dominant components of the benthic assemblages and
drift following HP (Bruno et al., 2010, 2013, 2016; Gibbins et al.,
2016; Imbert \& Perry, 2000; Moog, 1993; Schulting et al., 2018a, 2021;
Tanno et al., 2016, 2021; Timusk et al., 2016) as well to frequently
strand (Perry \& Perry, 1986; Tanno et al., 2016, 2021). Chironomidae
and Baetidae are likely to be represented strongly in the drift and
stranding due to the high density and density dependence of drift
(Waters, 1972). Accordingly, they showed comparably low propensity to
drift and strand and did not show reduced benthic densities under HP,
probably due to their morphological and behavioral adaptations to high
currents (Chironomidae: rapid colonizers with rapid growth rates and
short life cycles; Baetidae: good swimmers with a streamlined body,
rapid settling capabilities, frequent intentional drift; Elliott, 1971;
Milner, 1994; Naman et al., 2016; Rader, 1997; Wilzbach, Cummins, \&
Knapp, 1988). Even though, results regarding Chironimidae cannot be
generalized since this family consists of many different taxa with
different adaptations. Our study suggests that some Chironomid species
and Baetidae are resistant and resilient to long-term HP effects and
most likely have flexible habitat needs. We therefore conclude that high
passive drift and/or stranding, especially of individual-rich taxa, does
not necessarily indicate strong HP sensitivity. Yet it must be stated
that the benthic community composition in all investigated river reaches
has already been significantly affected by the long history and huge
variety of anthropogenic impacts on river ecosystems. The presented
results should therefore only be interpreted in consideration of the
limitations related to the altered source populations.

Some studies suggest that functional descriptions are maintained, even
with coarser taxonomic resolution, and it barely influences the
discrimination of impact levels (e.g., Doledec, Olivier, \& Statzner,
2000; Gayraud et al., 2003). However, since organisms in our study were
identified only to family-level, taxa-specific results should not be
over-interpreted. This level of taxonomic resolution inevitably leads to
a loss of information, especially for families that have heterogeneous
adaptations among the constituent genera, such as Chironomidae. Thus,
diverse species/genera within a family can react quite differently to HP
or other flow modifications (e.g., RF). Lastly, some taxa were found
only in low numbers (e.g., Limnephilidae), limiting the statistical
power of some of our results. Moreover, the comparably low number of
drifting and stranding individuals can probably be explained by the peak
magnitude of our experimental setup, which were lower as normally found
in Swiss HP rivers (e.g., Tanno et al. 2016, 2021).

River- and reach-specific responses

MIV responses to HP were not only taxa- and trait-specific but varied
widely among the investigated rivers and reaches. Disparities in the
direction and magnitude of responses among rivers likely resulted from
differences in community composition but also from variability in the HP
intensity and river morphological characteristics. Indeed, it is likely
that the lower peak flow in the Sitter in combination with its higher
morphological heterogeneity compared to the Hasliaare and the Linth lead
to lower hydraulic forces acting on the MIVs favoring benthic abundance
in concert with a reduced drift and stranding risk. The high drift in
the RF reach of the Linth can be explained by the hydraulic forces
measured, such as flow velocities and Froude number, which were similar
to the ones measured in the HP reach during peak flow. In contrast, in
the RF reaches of the Sitter and Hasliaare, they were comparable to the
ones measured during base flow (Appendix B in Data S1). This finding
underlines that discharge-related hydraulic forces (e.g., flow velocity)
acting on the riverbed are most probably the major determinant for MIV
drift. Further, the HP-induced removal and mobilization of organic
matter from the substrate, which is often associated to passive drift of
MIV (Aksamit et al, 2021; Bruno et al., 2016; Miller \& Judson, 2014;
Timusk et al., 2016), is a another determining factor. The RF reach of
the Linth was characterized by high algal cover on the substrate
(\textgreater{}50\%; Appendix A in Data S1) and showed the highest
drifting biomass (ash free dry mass) of FPOM and CPOM compared to the
other two rivers (Tonolla, Kastenhofer, Vogeli Kummert, \& Gufler,
2020).

In general, these river- and reach-specific responses confirm the
importance of structural complexity (such as in the Sitter) for
providing lentic habitats and hydraulic refugia at the locale scale as
well as the retention of organic matter during high flow disturbance
(e.g., Bruno et al., 2016; Hauer et al., 2017; Lancaster, 2000). In
structurally complex rivers with moderately sloping bank, velocity and
bottom shear stress remain relatively constant (Naman et al., 2017),
thereby reducing passive drift. In contrast, fluctuation of the
dewatering area is enhanced if the river-bank slope is low, forcing
organisms to shift habitats which subsequently increase their risk of
stranding.

In addition to river widening and a general increase in structural
complexity, morphological measures should aim to reduce
substrate-deficit and clogging due to fine substrate infiltration in the
dewatering area (often occurring in combination; Hauer et al., 2019),
thereby increasing interstitial space as potential hyporheic refugial
habitat during HP (Bruno et al., 2010). Further, the reconnection of
tributaries with a natural flow and sediment regime, as well as
improving the morphology upstream of HP impacted reaches (e.g., in the
RF reach), may provide refugial habitats and a connection with benthic
source populations for faster recolonization downstream of HP power
plants (Aksamit et al., 2021; Bruno et al., 2016; Hauer et al., 2017;
Kennedy et al., 2016; Milner, Yarnell, \& Peek, 2019).

Overall, our results highlight that different MIV taxa (and traits) vary
in their vulnerability and response to flow alteration (HP and RF) and
that the river-specific physical context (HP intensity, morphology,
distance to potential, source populations) mediate the magnitude and
direction of this response. Therefore, next to structural and/or
operational measures to reduce HP discharge (and related hydraulic
forces) and the ramping-rates, morphological measures should be
considered as an essential component of HP mitigation.

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\section*{Data Availability Statement}

{\label{data-availability-statement}}

The data that support the findings of this study are available from the
corresponding author, {[}D.T.{]}, upon reasonable request.

\section*{Tables}

{\label{tables}}

\emph{Table 1. Measured hydrological/hydraulic variables in the HP
reaches, separated in the three HP scenarios SC1, SC2, SC3. Values for
day one (D1) are indicated in black and values for day two (D2) in light
grey. Q\textsubscript{base}: base flow; Q\textsubscript{peak}; peak
flow; Q\textsubscript{peak}/Q\textsubscript{base}: flow ratio;
Q\textsubscript{peak} - Q\textsubscript{base}: flow amplitude;
UR\textsubscript{mean} and UR\textsubscript{max}: Mean and maximum
up-ramping rate; v\textsubscript{net}: peak flow velocity at net; dh:
difference between water depth at Q\textsubscript{peak} and
Q\textsubscript{base}} . \emph{v\textsubscript{net} is indicated as mean
+- standard deviation of the HP phases P1 and P2 at the three sampling
sites (n = 6 for each HP scenario). dh is indicated as mean +- standard
deviation at the three sampling sites (n = 3 for each HP scenario).}\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{KUB -- Sitter}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{KUB -- Sitter}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{KUB -- Sitter}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{KWO -- Hasliaare}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{KWO -- Hasliaare}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{KWO -- Hasliaare}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{KLL -- Linth}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{KLL -- Linth}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{KLL -- Linth}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{Sampling date}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
March 15\textsuperscript{th} \& 16\textsuperscript{th}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
March 15\textsuperscript{th} \& 16\textsuperscript{th}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
March 15\textsuperscript{th} \& 16\textsuperscript{th}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
March 30\textsuperscript{th} \& 31\textsuperscript{st}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
March 30\textsuperscript{th} \& 31\textsuperscript{st}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
March 30\textsuperscript{th} \& 31\textsuperscript{st}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
April 7\textsuperscript{th} \& 8\textsuperscript{th}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
April 7\textsuperscript{th} \& 8\textsuperscript{th}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
April 7\textsuperscript{th} \& 8\textsuperscript{th}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{Hydropeaking scenario}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
SC1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
SC2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
SC3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
SC1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
SC2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
SC3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
SC1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
SC2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
SC3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{Characteristics}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Q\textsubscript{base}} {[}m\textsuperscript{3}/s{]}; D1 \textbar{}
D2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.97 \textbar{} 1.96\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.97 \textbar{} 1.97\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.99 \textbar{} 1.99\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.72 \textbar{} 3.53\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.65 \textbar{} 3.57\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.73 \textbar{} 3.82\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.16 \textbar{} 6.32\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.13 \textbar{} 6.08\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.25 \textbar{} 6.22\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Q\textsubscript{peak}} {[}m\textsuperscript{3}/s{]}; D1 \textbar{}
D2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.99 \textbar{} 5.92\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
10.04 \textbar{} 10.75\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
17.50 \textbar{} 17.55\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.02 \textbar{} 5.53\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
8.74 \textbar{} 9.59\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
14.36 \textbar{} 15.84\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
11.52 \textbar{} 13.62\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
11.53 \textbar{} 13.22\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
11.99 \textbar{} 14.68\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Q\textsubscript{peak}/Q\textsubscript{base}} {[}-{]}; D1
\textbar{} D2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.04 \textbar{} 3.02\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.10 \textbar{} 5.46\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
8.79 \textbar{} 8.82\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.85 \textbar{} 1.57\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.30 \textbar{} 2.69\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.26 \textbar{} 4.15\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.23 \textbar{} 2.16\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.25 \textbar{} 2.17\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.28 \textbar{} 2.36\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Q\textsubscript{peak} - Q\textsubscript{base}}
(m\textsuperscript{3}/s); D1 \textbar{} D2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4.02 \textbar{} 3.96\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
8.07 \textbar{} 8.78\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
15.51\textbar{} 15.56\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.30 \textbar{} 2.00\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6.09 \textbar{} 6.02\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
11.63 \textbar{} 12.02\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6.36 \textbar{} 7.30\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6.40 \textbar{} 7.14\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6.74 \textbar{} 8.46\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{UR\textsubscript{mean}} {[}cm/min{]}; D1 \textbar{} D2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.75 \textbar{} 0.80\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.65 \textbar{} 1.60\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.00 \textbar{} 2.00\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.40 \textbar{} 0.50\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.70 \textbar{} 0.60\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.60 \textbar{} 0.75\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.95 \textbar{} 1.00\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.55 \textbar{} 1.80\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.90 \textbar{} 1.60\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{UR\textsubscript{max}} {[}cm/min{]}; D1 \textbar{} D2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.60 \textbar{} 1.70\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.80 \textbar{} 3.40\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4.70 \textbar{} 4.80\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.80 \textbar{} 1.10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.80 \textbar{} 1.20\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.00 \textbar{} 3.00\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.30 \textbar{} 2.20\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.60 \textbar{} 5.10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
7.10 \textbar{} 5.20\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\emph{v\textsubscript{net}} {[}m/s{]}; D1 \emph{v\textsubscript{net}}
{[}m/s{]}; D2\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.12 \selectlanguage{ngerman}± 0.07\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.21 \selectlanguage{ngerman}± 0.09\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.33 \selectlanguage{ngerman}± 0.07\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.24 \selectlanguage{ngerman}± 0.04\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.44 \selectlanguage{ngerman}± 0.14\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.24 \selectlanguage{ngerman}± 0.11\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.34 \selectlanguage{ngerman}± 0.22\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.41 \selectlanguage{ngerman}± 0.25\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.38 \selectlanguage{ngerman}± 0.24\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.77 \selectlanguage{ngerman}± 0.29\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.59 \selectlanguage{ngerman}± 0.17\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.55 \selectlanguage{ngerman}± 0.12\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.58 \selectlanguage{ngerman}± 0.10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.54 \selectlanguage{ngerman}± 0.08\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.57 \selectlanguage{ngerman}± 0.10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.42 \selectlanguage{ngerman}± 0.14\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.55 \selectlanguage{ngerman}± 0.08\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.55 \selectlanguage{ngerman}± 0.09\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\emph{dh} {[}m{]}; D1 \emph{dh} {[}m{]}; D2\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.13 \selectlanguage{ngerman}± 0.02\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.26 \selectlanguage{ngerman}± 0.01\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.43 \selectlanguage{ngerman}± 0.04\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.09 \selectlanguage{ngerman}± 0.00\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.21 \selectlanguage{ngerman}± 0.17\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.03 \selectlanguage{ngerman}± 0.01\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.16 \selectlanguage{ngerman}± 0.02\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.16 \selectlanguage{ngerman}± 0.00\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.17 \selectlanguage{ngerman}± 0.01\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.21 \selectlanguage{ngerman}± 0.04\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.33 \selectlanguage{ngerman}± 0.02\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.44 \selectlanguage{ngerman}± 0.02\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.09 \selectlanguage{ngerman}± 0.02\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.27 \selectlanguage{ngerman}± 0.03\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.37 \selectlanguage{ngerman}± 0.06\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.18 \selectlanguage{ngerman}± 0.03\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.14 \selectlanguage{ngerman}± 0.01\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.18 \selectlanguage{ngerman}± 0.02\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

Table 2. Propensity to drift/strand based on drift intensity
respectively stranding density to benthic density of the most common
taxa across all HP reaches (`ALL') and separated by reach. Larger
numbers (mean ± standard error) suggest higher propensity to
drift/strand (the three largest numbers of each reach and for `ALL' are
indicated in bold). Asterisks indicate taxa with relative abundances
\textgreater{} 1\% in the drift/stranding but not in the benthic samples
(Appendix C in Data S1).\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Taxa Drift\textsuperscript{+}} & \textbf{ALL (n = 54)} &
\textbf{Sitter (n = 18)} & \textbf{Hasliaare (n = 18)} & \textbf{Linth
(n = 18)}\tabularnewline
\midrule
\endhead
Oligochaeta & 0.005 \selectlanguage{ngerman}± 0.002 & 0.011 \selectlanguage{ngerman}± 0.005 & 0.003 \selectlanguage{ngerman}± 0.001 & 0.003 \selectlanguage{ngerman}±
0.001\tabularnewline
Baetidae & 0.079 \selectlanguage{ngerman}± 0.013 & 0.007 \selectlanguage{ngerman}± 0.001 & 0.197 \selectlanguage{ngerman}± 0.020 & 0.032 \selectlanguage{ngerman}±
0.003\tabularnewline
Heptageniidae & 0.004 \selectlanguage{ngerman}± 0.001 & 0.002 \selectlanguage{ngerman}± 0.000 & 0.004 \selectlanguage{ngerman}± 0.001 & 0.005 \selectlanguage{ngerman}±
0.001\tabularnewline
Leuctridae & 0.068 \selectlanguage{ngerman}± 0.013 & 0.003 \selectlanguage{ngerman}± 0.001 & 0.176 \selectlanguage{ngerman}± 0.021 & 0.026 \selectlanguage{ngerman}±
0.004\tabularnewline
Nemouridae & 0.046 \selectlanguage{ngerman}± 0.007 & 0.009 \selectlanguage{ngerman}± 0.002 & 0.086 \selectlanguage{ngerman}± 0.015 & 0.043 \selectlanguage{ngerman}±
0.007\tabularnewline
Elimidae & 0.040 \selectlanguage{ngerman}± 0.012 & 0.011 \selectlanguage{ngerman}± 0.001 & 0.063 \selectlanguage{ngerman}± 0.031 & \selectlanguage{ngerman}\textbf{0.045
± 0.015}\tabularnewline
Chironomidae & 0.041 \selectlanguage{ngerman}± 0.007 & 0.009 \selectlanguage{ngerman}± 0.001 & 0.095 \selectlanguage{ngerman}± 0.012 & 0.017 \selectlanguage{ngerman}±
0.001\tabularnewline
Simuliidae & \selectlanguage{ngerman}\textbf{0.125 ± 0.024} & \selectlanguage{ngerman}\textbf{0.021 ± 0.003} &
\selectlanguage{ngerman}\textbf{0.311 ± 0.046} & \selectlanguage{ngerman}\textbf{0.044 ± 0.012}\tabularnewline
Taeniopterygidae* & 0.035 \selectlanguage{ngerman}± 0.005 & \selectlanguage{ngerman}\textbf{0.024 ± 0.008} & 0.042 \selectlanguage{ngerman}±
0.008 & 0.040 \selectlanguage{ngerman}± 0.010\tabularnewline
Limnephilidae* & \selectlanguage{ngerman}\textbf{0.299 ± 0.085} & \selectlanguage{ngerman}\textbf{0.084 ± 0.053} &
\selectlanguage{ngerman}\textbf{0.683 ± 0.224} & \selectlanguage{ngerman}\textbf{0.128 ± 0.036}\tabularnewline
Rhyacophilidae* & \selectlanguage{ngerman}\textbf{0.691 ± 0.194} & 0.008 \selectlanguage{ngerman}± 0.003 & \selectlanguage{ngerman}\textbf{2.057
± 0.432} & 0.007 \selectlanguage{ngerman}± 0.002\tabularnewline
\selectlanguage{english}\textbf{Taxa Stranding\textsuperscript{++}} & \textbf{ALL (n = 54)} &
\textbf{Sitter (n = 18)} & \textbf{Hasliaare (n = 18)} & \textbf{Linth
(n = 18)}\tabularnewline
Oligochaeta & 0.041 \selectlanguage{ngerman}± 0.018 & \selectlanguage{ngerman}\textbf{0.022 ± 0.009} & 0.013 \selectlanguage{ngerman}± 0.008 &
\selectlanguage{ngerman}\textbf{0.086 ± 0.052}\tabularnewline
Baetidae & 0.042 \selectlanguage{ngerman}± 0.011 & 0.001 \selectlanguage{ngerman}± 0.000 & 0.104 \selectlanguage{ngerman}± 0.028 & 0.021 \selectlanguage{ngerman}±
0.004\tabularnewline
Heptageniidae & 0.006 \selectlanguage{ngerman}± 0.003 & 0.001 \selectlanguage{ngerman}± 0.000 & 0.003 \selectlanguage{ngerman}± 0.001 & 0.014 \selectlanguage{ngerman}±
0.007\tabularnewline
Nemouridae & 0.232 \selectlanguage{ngerman}± 0.068 & 0.002 \selectlanguage{ngerman}± 0.002 & \selectlanguage{ngerman}\textbf{0.617 ± 0.171} &
0.078 \selectlanguage{ngerman}± 0.030\tabularnewline
Chironomidae & 0.075 \selectlanguage{ngerman}± 0.015 & \selectlanguage{ngerman}\textbf{0.005 ± 0.001} & 0.167 \selectlanguage{ngerman}± 0.035 &
0.054 \selectlanguage{ngerman}± 0.007\tabularnewline
Simuliidae & 0.079 \selectlanguage{ngerman}± 0.034 & \selectlanguage{ngerman}\textbf{0.006 ± 0.003} & 0.231 \selectlanguage{ngerman}± 0.000 &
0.000 \selectlanguage{ngerman}± 0.000\tabularnewline
Empididae* & 0.049 \selectlanguage{ngerman}± 0.022 & 0.000 \selectlanguage{ngerman}± 0.000 & 0.000 \selectlanguage{ngerman}± 0.000 &
\selectlanguage{ngerman}\textbf{0.148 ± 0.059}\tabularnewline
Perlodidae* & \selectlanguage{ngerman}\textbf{0.550 ± 0.295} & 0.000 \selectlanguage{ngerman}± 0.000 & 0.090 \selectlanguage{ngerman}± 0.000 &
\selectlanguage{ngerman}\textbf{1.560 ± 0.847}\tabularnewline
Limnephilidae* & \selectlanguage{ngerman}\textbf{0.270 ± 0.103} & 0.000 \selectlanguage{ngerman}± 0.000 & \selectlanguage{ngerman}\textbf{0.810
± 0.270} & 0.000 \selectlanguage{ngerman}± 0.000\tabularnewline
Rhyacophilidae* & \selectlanguage{ngerman}\textbf{0.322 ± 0.130} & 0.000 \selectlanguage{ngerman}± 0.000 & \selectlanguage{ngerman}\textbf{0.960
± 0.348} & 0.006 \selectlanguage{ngerman}± 0.006\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

\selectlanguage{english}\textsuperscript{+} \emph{Hydracarina was excluded because drift
propensity was not computable in one or more of the three reaches; also
for the drift CCA (Figure 5a).} \selectlanguage{english}\textsuperscript{++}

\emph{Chloroperlidae, Lepidostomatidae and Gammaridae were excluded
because stranding propensity was not computable in one or more of the
three reaches; also for the stranding CCA (Figure 5b).}

Table 3. Simper statistic showing the taxa that most contributed to
dissimilarity among RF and HP reaches of the three rivers. Taxa that
cumulatively contribute (cum sum) to ca. 90 \% of the differences
between each pair of groups (RF vs HP) are shown.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{River reach}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Taxa (cum sum)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Taxa (cum sum)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Taxa (cum sum)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Taxa (cum sum)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Taxa (cum sum)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Taxa (cum sum)}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Sitter RF \emph{vs} HP\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Heptageniidae 0.292\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Chironomidae 0.551\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Baetidae 0.731\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Simuliidae 0.791\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Leuctridae 0.848\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Oligochaeta 0.889\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Hasliaare RF \emph{vs} HP\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Limnephilidae 0.228\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Nemouridae 0.410\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Chironomidae 0.562\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Baetidae 0.702\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Heptageniidae 0.822\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Leuctridae 0.918\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Linth RF \emph{vs} HP\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Chironomidae 0.352\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Baetidae 0.535\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Leuctridae 0.678\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Nemouridae 0.814\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Heptageniidae 0.921\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\section*{Figure legends}

{\label{figure-legends}}

Figure 1. Overview map of the three study sites. White dots show the
water releases of the hydropower plant (KUB) and of the retention basins
(KWO and KLL). HP and RF reaches are identified with white and yellow
rectangles, respectively. White arrows show the flow direction. Source
background Orthophotos: Swissimage \selectlanguage{ngerman}© Swisstopo.

Figure 2. Experimental setup. (a) Schematic sampling design for HP (top
panel) and RF reaches (bottom panel). (b) Chronological sampling
illustrated as example of the three HP scenarios (SC1, SC2, SC3) at day
one in the HP reach of the Sitter. Drift samples were collected
separately at base flow (B) and during each HP phase: up-ramping (UR),
first and second peak (P1, P2), down-ramping (DR). (c) HP scenarios.
Water level (blue solid line) and water temperature (red solid line)
measured in the HP reaches as well as water temperature measured in the
RF reaches (red dotted line). Grey squares indicate sampling period at
day one and two. Please note different scales on y-axes.

\emph{Figure 3.} \emph{Drift intensity (ind./m\textsuperscript{2}min)
across all reaches (`ALL') and separated by reach. RF: residual flow
reach; B: base flow; HP: during hydropeaking (i.e., entire HP scenario:
UR, P1, P2 and DR phase). Boxplots show the 25\textsuperscript{th} and
75\textsuperscript{th} percentiles, median (solid line in the box), mean
(dashed line in the box), whiskers (10\textsuperscript{th} and
90\textsuperscript{th} percentiles) and outliers (white dots). Numbers
indicate the sample size, whereas letters show group affiliation
according to post hoc tests with Bonferroni correction.}

\emph{Figure 4. Relationship between drift intensity
(ind./m\textsuperscript{2}min) and stranding density
(ind./m\textsuperscript{2}) based on linear regression models across all
three HP reaches (`ALL') and separated by reach (Sitter: black square;
Hasliaare: dark grey circle; Linth: light grey triangle). (a) Drift data
of an entire HP scenario: UR, P1, P2 and DR phase. (b) Only drift data
of the UR phase. Solid lines: linear regressions; dashed black lines in
the left plots: 95\% confidence intervals. To better meet assumptions of
normality and homogeneity of variances, all data were log transformed
(X+1) prior to computing regressions. Although, in (b) the data for
`ALL' and Hasliaare are not normally distributed.} \emph{R: coefficient
of multiple correlation.}

\emph{Figure 5. Biplot of Canonical Correspondence Analysis (CCA) based
on (a) drift propensity and (b) stranding propensity ofthe most common
taxa in the three HP reaches. Left panels: confidence ellipses (95\%
confidence limit, standard deviation) are fitted on the CCA plots to
depict differences between river reaches. Right panels: distribution of
the species scores of the most common taxa (the larger the circle the
higher the propensity). Asterisks indicate taxa with abundances
\textgreater{} 1\% in the drift/stranding but not in the benthic samples
(Appendix C in Data S1). Factor fitting shows the statistical
differences in drift/stranding propensity between HP reaches (Sitter vs
Hasliaare vs Linth), sampling day (day 1 vs day 2) and HP scenario (SC 1
vs SC2 vs SC3). Significance levels: *0.05, **0.01, ***0.001.}

\emph{Figure 6.} \emph{Benthic density (ind./m\textsuperscript{2})
across all three RF respectively all three HP reaches(`ALL\_RF',
`ALL\_HP') and separated by reach.} \emph{Numbers indicate the sample
size, whereas letters show group affiliation according to Mann-Whitney
tests.}

Figure 7. NMDS ordination of the three RF and three HP reaches based on
benthic densities (ind./m\textsuperscript{2}) of the most common taxa
(relative abundance \textgreater{} 1\%, Appendix C in Data S1). 95\%
confidence ellipses of the standard deviations of site scores of the
three rivers (Sitter, Hasliaare, Linth) within the two respective
reaches (RF: solid ellipses; HP: dashed ellipses) are depicted. Taxa
that most contributed to dissimilarity (p \textless{} 0.05) are fitted
on the NMDS plot and indicated with black dots and taxon name.

\emph{Figure 8.} \emph{Benthic density (ind./m\textsuperscript{2})
across all three RF respectively all three HP reaches separated by the
two traits `interstitial/surface' and `lentic/lotic'. Numbers indicate
the sample size, whereas letters show group affiliation according to
Mann-Whitney tests.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2-(1)-1/Figure-2-(1)-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5-0/Figure-5-0}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-6/Figure-6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-7-0/Figure-7-0}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-8/Figure-8}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

