Behaviour of anadromous brown trout (Salmo trutta) in a hydropower regulated freshwater system

Abstract

Many Norwegian rivers and lakes are regulated for hydropower, which affects freshwater ecosystems and anadromous fish species, such as sea trout (Salmo trutta). Lakes are an important feature of many anadromous river systems. However, there is limited knowledge on the importance of lakes as habitat for sea trout and how hydropower affects the behaviour of sea trout in lakes. To investigate this, we conducted an acoustic telemetry study. A total of 31 adult sea trout (532 ± 93 mm total length) were captured by angling in river Aurlandselva, Norway, and tagged between July 20 and August 12, 2021. The tags were instrumented with accelerometer, temperature, and depth sensors, which provided information on the sea trout’s presence and behaviour in lake Vassbygdevatnet. Our results indicate that there was a large prevalence of sea trout in the lake during the spawning migration, and that the sea trout were less active in the lake compared to the riverine habitats. An increase in activity of sea trout in the lake during autumn might indicate that sea trout spawn in the lake. However, the discharge from the high-head storage plant into the lake did not affect the depth use or activity of sea trout in the lake. Furthermore, the large prevalence of spawners in the lake during autumn will likely cause an underestimation of the size of the sea trout population in rivers with lakes during annual stock assessment. In conclusion, our results could not find evidence of a large impact of the discharge on the behaviour of sea trout in the lake.

Supplementary Information
The online version contains supplementary material available at 10.1186/s40462-023-00429-7.

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Dahlmoetal. Movement Ecology (2023) 11:63
https://doi.org/10.1186/s40462-023-00429-7
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Movement Ecology
Behaviour ofanadromous brown trout
(Salmo trutta) inahydropower regulated
freshwater system
Lotte S. Dahlmo1,2*, Gaute Velle1,2, Cecilie I. Nilsen1,2, Ulrich Pulg1, Robert J. Lennox1,3 and Knut W. Vollset1
Abstract
Many Norwegian rivers and lakes are regulated for hydropower, which affects freshwater ecosystems and anadro-
mous fish species, such as sea trout (Salmo trutta). Lakes are an important feature of many anadromous river sys-
tems. However, there is limited knowledge on the importance of lakes as habitat for sea trout and how hydropower
affects the behaviour of sea trout in lakes. To investigate this, we conducted an acoustic telemetry study. A total
of 31 adult sea trout (532 ± 93 mm total length) were captured by angling in river Aurlandselva, Norway, and tagged
between July 20 and August 12, 2021. The tags were instrumented with accelerometer, temperature, and depth
sensors, which provided information on the sea trout’s presence and behaviour in lake Vassbygdevatnet. Our results
indicate that there was a large prevalence of sea trout in the lake during the spawning migration, and that the sea
trout were less active in the lake compared to the riverine habitats. An increase in activity of sea trout in the lake
during autumn might indicate that sea trout spawn in the lake. However, the discharge from the high-head storage
plant into the lake did not affect the depth use or activity of sea trout in the lake. Furthermore, the large prevalence
of spawners in the lake during autumn will likely cause an underestimation of the size of the sea trout population
in rivers with lakes during annual stock assessment. In conclusion, our results could not find evidence of a large
impact of the discharge on the behaviour of sea trout in the lake.
Keywords Biologging, Anadromous brown trout, Hydropower, Lake ecology, Acceleration
Introduction
Freshwaters comprise only a small fraction of the Earth,
yet freshwater habitats are disproportionately threatened
by overexploitation, pollution, and regulation [22, 26, 65,
91]. Salmonids and other species that rely on freshwater
are therefore vulnerable [43], and changes to rivers and
lakes can impact resident and migratory fish populations
[11, 58]. Hydropower regulations can cause changes to
the natural water flow, such as the timing, magnitude,
and variability of the water flow [59, 77]. Hydrologi-
cal changes affect both the biotic and abiotic variables
upstream and downstream of modified areas by altering
the movement of sediments and organic resources, avail-
ability of habitat types, shelters, and forage opportunities,
and the distribution, abundance, and richness of species
[59, 60, 82]. e effects of regulation and modifications of
rivers on freshwater fish are frequently studied (e.g., [11,
69]) and restoration interventions (e.g., fishways, barrier
removal, gravel augmentation) are increasingly imple-
mented to improve habitats, such as the connectivity or
quality (e.g., [44, 61, 63, 66]). In contrast, there is a lack of
*Correspondence:
Lotte S. Dahlmo
lottesdahlmo@gmail.com
1 LFI Laboratory for Freshwater Ecology and Inland Fisheries, NORCE
Norwegian Research Centre, Nygårdsgaten 112, 5008 Bergen, Norway
2 Department of Biological Sciences, University of Bergen, Thormøhlens
Gate 53A, 5008 Bergen, Norway
3 NINA Norwegian Institute for Nature Research, Høgskoleringen 9,
7034 Trondheim, Norway
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Dahlmoetal. Movement Ecology (2023) 11:63
studies on how hydropower impacts lake habitat for ana-
dromous species [46].
Norwegian rivers and lakes are highly exploited to
generate hydropower due to a topography with an abun-
dance of freshwater systems across different altitudes,
steep mountains, and high annual precipitation [1]. In
contrast to run-of–river hydropower plants that pro-
duce energy by implementing physical barriers, such as
dams and weirs in rivers [3, 8], the topography of Norway
allows for high-head storage plants [1]. Storage plants
exploit the potential energy of water from reservoirs and
often discharge into lakes, which are important habitats
for anadromous brown trout (hereafter referred to as sea
trout, Salmo trutta) and Atlantic salmon (Salmo salar,
[46]). e intake of high-head storage plants is often in
the deeper part of reservoirs, which results in the transfer
of hypolimnetic water through turbines and into a ord,
river, or a reservoir, such as a natural lake or artificial res-
ervoir [31]. e hypolimnetic water (~ 4℃) transported
by the storage plant therefore supplies relatively cold
water during summer and warm water during winter
[31, 67]. More than 30% of Norwegian rivers run through
lakes, many of which are highly exploited to generate
hydroelectricity [1] and may also involve migration barri-
ers, such as weirs and dams.
Animal choice of habitat depends on a trade-off
between their energy budget (i.e., growth) and mortality
rate [85]. Alteration of habitats can affect behaviour and
accelerate energy depletion of animals [36], for instance
through increasing movement and activity. A logical
question is therefore whether high-head storage plants
increase the activity level of sea trout in lakes and alter
their habitat choice. is study aims to provide insight
into the lake use and activity of sea trout by measuring
their movement in the three spatial axes. By using acous-
tic transmitters (i.e., tags) equipped with acceleration and
depth sensors, we investigated whether adult sea trout
in a watercourse including a lake used the lake before
spawning and whether their behaviour was affected by
discharge from the high-head storage plant. Specifically,
we hypothesised that: 1) the lake is used by sea trout
before spawning, and that 2) the activity (acceleration) of
sea trout is higher in the rivers than in the lake, and 3) the
high-head storage plant discharge alters the behaviour of
sea trout during the spawning migration.
Methods
Study site
e study was conducted in the Aurland watercourse
in Vestland county, Norway (Fig.1). e upstream river
Vassbygdelva runs from the mountains and constitute
the main river inflow into lake Vassbygdevatnet. Ana-
dromous fish can migrate up nearly 5km of the lowest
reaches of river Vassbygdelva until steep areas act as
natural barriers hindering further migration (see Fig. 1.
in [62, 80]). Vassbygdevatnet has a length of 3.3 km,
Fig. 1 Map of Aurland watercourse with the location of receivers (circles, triangles) deployed prior (blue) and post (green) tagging, the ‘Aurland
1’ high-head storage plant, ‘Vangen’ storage plant, and the flap weir and fish ladder at the outlet of Lake Vassbygdevatnet (red line). Two
synchronization transmitters were placed with two of the receivers in the lake (triangles). Receivers are numbered between 1–22 for identification
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Dahlmoetal. Movement Ecology (2023) 11:63
covers an area of 1.9 km2, and has an average depth of
42m and maximum depth of 65m. e river Aurland-
selva, runs 6.7 km downstream from the lake before it
ends in the ord Aurlandsorden, an arm in the Sog-
neord about 170km from the open ocean. In Aurland,
sea trout can inhabit approximately 15km of the water-
course [80]. Following the river regulation from 1969,
both Atlantic salmon and trout populations exhibited
a dramatic decline by the late 1980s [80]. Today, the sea
trout population dominates and has large recreational
value to anglers and great socio-economic importance to
the local community, while the salmon population is still
significantly reduced and has been protected since 1989
[37, 63].
Hydropower plants
e construction of the hydropower system in Aurland
began in 1969 and lasted until 1989 [80]. Today, the
hydropower system consists of five power plants, which
together with 14 reservoirs and several tunnels, regulate
the Aurland watercourse [80]. Two of these power plants
directly influence the lake Vassbygdevatnet in Aurland
(Fig.1). e ‘Aurland 1’ plant is a high-head storage plant
(850m in head height, 840MW) with outlet running into
the southeastern part of Vassbygdevatnet and is the larg-
est power plant in the watercourse. Aurland 1 constitutes
the primary supply of water into the lake by transport-
ing water from the mountain reservoirs. erefore, the
lake surface temperature is impacted, being colder dur-
ing summer and warmer during the winter, which results
in a low thermal stratification of the lake [80]. e ‘Aur-
land 4’ storage plant (55m in head height, 38MW), also
known as ‘Vangen’, has its intake in the western part of
lake Vassbygdevatnet that leads to a tunnel running down
to the power plant by the ord. e Vangen station oper-
ates from September 15 until the end of April, and during
this period a flap weir located at the outlet to river Aur-
landselva is elevated, thereby regulating the water flow
downstream in the river (Fig.1). While Vangen is oper-
ating, Aurlandselva has a mandatory minimum discharge
of 3 m3/s that is upheld by release of water over the flap
weir [94]. e lake functions as a semi-natural reservoir
while Vangen is operating. A pool and weir fish ladder
along the west side of the flap weir allows for fish migra-
tion between the lake and the river (head 1–2m) when
the weir is elevated.
Discharge data
Aurland 1 released an average discharge of 20.97 m3/s
(± 16.95) into the lake during the study period (July 20–
November 14, 2021, see Additional file1: Figure S1), with
a minimum discharge of 0 m3/s and a maximum dis-
charge of 108.46 m3/s. Before the flap weir was elevated,
the downstream river Aurlandselva had an average dis-
charge of 27 m3/s (± 10.40) and a minimum and maxi-
mum discharge of 3.75 and 51.11 m3/s, respectively. After
the elevation of the flap weir, the average discharge was
4.25 m3/s (± 0.54), the minimum discharge was 2.96 m3/s,
and the maximum discharge was 8.13 m3/s in Aurland-
selva. Discharge data for the study period were provided
by the hydropower company Hafslund ECO.
Study design
All sea trout were captured, tagged, and released between
July 20 and August 12, 2021. Prior to capturing fish, a
total of 19 TBR 700 and 700L acoustic receivers (elma
Biotel AS, Trondheim, Norway) were deployed: three
in river Vassbygdelva; five in river Aurlandselva; and
eleven in lake Vassbygdevatnet (Fig.1). Two synchroniz-
ing transmitters (“sync tags”) were deployed with two
receivers to correct clock drift of the receivers in the lake.
ree additional receivers were deployed September 2
in river Aurlandselva after all fish were captured, tagged,
and released, to maximize the coverage in the river dur-
ing the autumn migration and spawning (Fig. 1). Data
were downloaded from all 22 receivers on November 15
and 16, 2021.
Sampling andtagging
A total of 31 sea trout (540 ± 102mm total length) were
captured by recreational anglers in river Aurlandselva.
All sea trout were tagged and released in proximity of
where they were caught, with a total of nine capture sites
located between the confluence of the lake and the river,
and the site furthest down (close to the river mouth). Sea
trout were kept in keepnets or tubes for a minimum of
30min after hooking to provide a recovery period. Most
sea trout were caught during night and tagged within
6h the following morning, and a few sea trout were held
up to 20h before being tagged and released. To ensure
a tag burden less than 2% of body weight (e.g., Jepsen
etal. 2005; [76]), a lower weight limit was converted to a
lower length limit of fish by using Fulton’s condition for-
mula [66] with an assumed K value of 1. e minimum
total fish length was calculated to 38cm, and the small-
est fish tagged was 41.5cm. us, the maximum tag bur-
den was approximately 1.6% of the fish’s body weight. To
avoid selection of sea trout, all captured sea trout above
the minimum total length requirement in the present
study were assessed suitable for tagging by visual assess-
ment (any visible wounds, marks, or lice) and response
to external stimuli were checked. Remarks on visible
wounds, marks, or lice were noted, however, none of the
sea trout had severe external marks or wounds, or was
assessed to be in an unsuitable condition for tagging.
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Dahlmoetal. Movement Ecology (2023) 11:63
Prior to surgery, each sea trout was anaesthetized with
1.5–2 mL Aqui-S in a container with 50 L water until
equilibrium was lost (6–9 min). e fish was placed
supine in a tube where fork length (mm) and total length
(mm) were measured. A silicone tube with running water
containing 50% dose of the anaesthetics was placed in
its mouth to maintain anaesthesia and oxygenation dur-
ing surgery. A 15–18mm incision was performed with a
sterile scalpel approximately 3cm posterior to the pec-
toral fins and 1–2mm from the linea alba. e sterilized
LP13-ADT acoustic tag (S64K protocol, 90 s nominal
delay, 11.5g in air, 33.3mm long, 13mm wide; elma
Biotel, Trondheim, Norway) was placed into the abdo-
men, followed by three interrupted sutures to close the
incision. e sensors had a range between 0 and 255,
thus any values above maximum were registered as 255.
e surgery, including the anaesthetic period, lasted
for approximately 16min. Tagged fish were transferred
to keepnets or containers with fresh river water and
observed during recovery for about ten to fifteen minutes
before being released. Every fish was tagged and released
close to its capture site (hereafter referred to as tagging
site). Approval of the project was given by the Norwegian
Food Safety Authority (FOTS, application nr. 23016), and
handling and tagging of sea trout was conducted accord-
ing to the Norwegian animal welfare regulations.
Data analysis
All preparation, visualization, and statistical analy-
ses of data were conducted in R-Studio 4.1.2 [64]. Posi-
tions were derived for all individuals in the lake based
on multilateralization of the detections in the receiver
grid. Transmissions of two synchronisation tags (Fig.1)
were used to synchronise the receiver clocks in the lake
using Yet another positioning solver (YAPS, [7]) func-
tion getSyncModel with an eps threshold of 10. A custom
wrapper function for the YAPS algorithm was written to
fit five model fits to each fish day in the time series and
select the model with the best fit. Positions with esti-
mated error > 20m in both the x and the y dimensions
were discarded.
Acoustic telemetry and detection data are prone to
false detections [70], which is necessary to account for.
False detections were identified and removed with clean-
ing tools (such as the filter(), mutate(), and case_when()
functions) in the dplyr package [88]. Data were visualized
with the ggplot2 package [87] and model interpretations
were visualized with the gratia package [71].
All generalized additive models (GAMs) used in the
data analyses were implemented with the bam() func-
tion from the mgcv package [90], which is suitable for
larger datasets. Additionally, a gamma distribution with
a log link function was used in all the GAM models. e
gamma distribution was used because the response vari-
able of the models was continuous and positive [92]. e
collinearity between explanatory variables was checked
with the ggpairs() function from the GGally package [68]
to exclude variables that were correlated. To test whether
the smoothers (term to account for non-linear variation
over time) followed the same pattern, the concurvity()
function from the mgcv package was used. e function
calculates three measures of concurvity (worst, observed,
and estimate), and by using the concurvity values from
the most pessimistic measure (worst), values above 0.8
indicates strong presence of concurvity [21] and there-
fore similar patterns between two smoothers.
e raw dataset was filtered so that only data from
the study period (July 20–November 14, 2021) and the
unique IDs from the S64K-69kHz protocol were retained
in the dataset. One individual ID (ID 4697) died or lost
its tag one month after tagging. For this individual, only
detections up until August 26, 2021, were included. One
fish (ID 4685) was never detected, giving a final sam-
ple size of 30 sea trout. Nine additional detections from
three individuals were manually removed following
closer inspection of the raw dataset. To account for any
additional potential false detections, three filtering codes
with different criteria were constructed and any detec-
tions that met the criteria were removed. e dataset was
first filtered by grouping the dataset by fish ID, then cal-
culating the speed (m/s) and distance (m) from the pre-
vious detection. erefore, the first detection from each
unique fish had a distance and speed equal to zero. e
three filtering codes were: 1) detections from one of the
river receivers where the previous detection was in the
lake and the distance calculated was greater than 1000m;
2) detections from a lake receiver with a previous detec-
tion from one of the river receivers and a calculated dis-
tance greater than 1000m; and 3) any detections with a
distance larger than 800m and with a speed greater than
5m/s. e speed criteria was set to 5m/s as it is unlikely
that salmonids swim faster than 5m/s over longer dis-
tances [23], [56].
Hypothesis 1: Habitat use
e time spent in the two habitats (i.e., river or lake) was
calculated by assigning each individual to a habitat at any
given minute after their respective tagging day until the
end of the study period. For undetected time stamps,
habitat was interpolated using the previous habitat that
an individual was detected in. For the time between the
tagging and first detection, the habitat was interpolated
using the first habitat an individual was detected in.
In order to test whether the lake is an important habitat
for sea trout before spawning, a generalized linear model
was built with a poisson distribution by using the glm()
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Dahlmoetal. Movement Ecology (2023) 11:63
function in R with the number of trout in the lake as a
response variable (lake). All individuals were assigned
to either river or lake per minute throughout the study.
us, undetected minutes per individual were interpo-
lated by using the previous habitat a fish was detected in.
Day of year (day) and the number of sea trout that could
have been in the lake (calculated by offset; total) were
used as explanatory variables. e model was given as:
Model 1.1
Lake ~ day + offset(log(total)), family = “Poisson”.
Hypothesis 2: Eect ofhabitat onactivity
To investigate the effect of habitat on the activity, accel-
eration (m/s2) was used as proxy for activity as demon-
strated by Mulder etal. [52] with Arctic charr (Salvelinus
alpinus) in a similar environment. is sensor is a tri-
axial accelerometer with a range of 0–3.465 m/s2 that
measures both dynamic and static acceleration. e
tags were programmed to measure acceleration in the
three axes for 27s at 12.5Hz and then calculate a root
mean square value summarising the three axes, encod-
ing this value as a number between 0 and 255 (the inte-
ger range of the sensor), and transmitting this value to
the receiver. e raw acceleration being a value between
0 and 255, this was transformed back to root mean
square (RMS) using the following equation: RMS = raw
data × 3.456/255.
To test whether the activity of sea trout differs between
the rivers and the lake, a GAM model was built. Activ-
ity based on accelerometer data (or acceleration (m/
s2), accel) was modelled as the response variable, while
habitat (lake or river, as factor), day of year (denoted as
day), and time of day (time) were included as explana-
tory variables. e unique fish ID (individual) variable
was included as a random effect. ere was high corre-
lation between day of year and discharge in the down-
stream river (-0.835), and high correlation between day
of year and temperature (-0.944, temperature measured
from temperature sensor in the tags). Temperature and
the discharge in the downstream river were therefore not
included, to retain the temporal structure of the variance
in the models.
A smoother (s()) was used for each of the temporal var-
iables (day and time) to account for non-linear variation
over time. When the wiggliness of values of a variable
differ substantially, it can be useful to include an interac-
tion in the smoother, which informs the model to apply
a separate smoother for each level of a factor [57]. e
term ‘by = habitat’ was included in each of the temporal
smoothers so that a smoother was fitted to each level of
habitat (i.e., lake and river). For the random effect of fish
ID, a smoother was used to account for nestedness and
repeated measurements of observations, with “re” speci-
fying that the basis for smoothing (bs) is adjusted to the
random effect of the variable and k equals to the sam-
ple size (k = N = 30). e amount of wiggliness (k) was
adjusted to the other smoothers.
Because the dataset was built up by repeated measure-
ments from the same sea trout individuals over time, an
autocorrelation term was included to test if the autocor-
relation structure improved the model. e autocorre-
lation term was calculated based on the first model and
then included in the second model. Akaike Information
Criterion (AIC; [39] was used to compare the fit of the
two models. e final models were:
Model 2.1
Accel ~ habitat + s(day, by = habitat, k = 40) + s(time,
by = habitat, k = 10) + s(individual, bs = "re", k = 30),
method = "fREML", family = G amma(link = "log").
Model 2.2
Accel ~ habitat + s(day, by = habitat, k = 40) + s(time,
by = habitat, k = 10) + s(individual, bs = "re", k = 30),
AR.start = starting_timepoint, rho = rho_value,
method = "fREML", family = G amma(link = "log").
Hypothesis 3: Eect ofhigh‑head storage plant discharge
onbehaviour inthelake
To test if the high-head storage plant discharge alters the
behaviour of sea trout during the spawning migration,
GAM models were built with- and without the discharge
as an explanatory variable based on a subset of the data
only from the lake. e models were built by the explana-
tory variables day of year (day), time of day (time), and
a bivariate smoother to account for the spatial interac-
tion between longitude (longitude) and latitude (latitude)
calculated from the YAPS positioning algorithm. e
spatial smoother had a k-value of 100 to allow for large
spatial variation. A smoother was also used for each of
the two temporal variables to account for seasonal- and
daily variation in depth use. To account for the random
effect of individual sea trout, the fish IDs (individual)
was included as a factor in a smoother, with k equal to
the number of sea trout detected in the lake (k = N = 26).
A calculated autocorrelation structure was included in
all models. e discharge data from the high-head stor-
age plant Aurland 1 (AU1) was included as an additional
explanatory variable. Four models were built to inves-
tigate the effect of the discharge on the depth use and
activity in the lake independently, with average depth
(depth, model 3.1 and model 3.2) and activity (activ-
ity, model 3.3 and model 3.4) as response variables in
two of the models each. For the two models of activity
in the lake (model 3.3 and model 3.4), depth (depth) was
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Page 6 of 15
Dahlmoetal. Movement Ecology (2023) 11:63
added as an additional explanatory variable in the two
models. An AIC model comparison was implemented
to test whether the discharge data from the storage plant
improved the model fit. e best fitted models (one for
depth and one for activity) were visualised for inspection
of the explanatory variables by drawing predictions from
the model output on a grid of all possible values in the
data series. e four models were:
Model 3.1
Depth ~ s(longitude, latitude, k = 100) + s(day,
k = 40) + s(time, k = 4) + s(individual, bs = "re", k = 26),
AR.start = starting_timepoint, rho = rho_value,
method = "fREML", family = G amma(link = "log").
Model 3.2
Depth ~ s(AU1, k = 4) + s(longitude, lati-
tude, k = 100) + s(day, k = 40) + s(time,
k = 4) + s(individual, bs = "re", k = 26), AR .start = start-
ing_timepoint, rho = rho_value, method = "fREML",
family = Gamma(link = "log").
Model 3.3
Accel ~ depth + s(longitude, latitude, k = 100) + s (da y,
k = 40) + s(time, k = 4) + s(individual, bs = " re" ,
k = 26), AR.start = starting_point, rho = rho_value,
method = "fREML", family = G amma(link = "log").
Model 3.4
Accel ~ depth + s(AU1, k = 4) + s(longitude, latitude,
k = 100) + s(day, k = 40) + s(time, k = 4) + s(individual,
bs = "re", k = 26), AR .start = starting_point, rho = rho_
value, method = "fREML", f amily = Gamma(link = "log").
Results
Hypothesis 1: Habitat use
Most of the tagged sea trout were detected in the lake
(87%, N = 26), whereas nine sea trout were only detected
in the lake and four sea trout only detected in the river
(Figs. 2 and 3). Sea trout spent on average 83 days
(SD = 34, median = 98, min = 3, ma x = 118) in the lake
and on average 66days (SD = 39, median = 76, min = 8,
max = 118) in the rivers (Fig.2). Among the 26 sea trout
that were detected in the lake, half were tagged at the
confluence of the river and the lake (N = 13) and half
ascended from their tagging sites in the downstream
river (N = 13, Figs. 2 and 3). e remaining 13% of the
sea trout remained in the river (N = 4). A few sea trout
ascended to the upstream river (13%, N = 4). Out of the
sea trout tagged at the confluence, nearly 70% remained
in the lake (N = 9, Figs.2 and 3). None of the 30 sea trout
were detected by the receiver at the river mouth of the
downstream river (Receiver 1, Fig.1).
Out of the sea trout that ascended to the lake, 57%
ascended before (N = 8) and 43% ascended after (N = 6)
Fig. 2 Habitat use of 30 tagged sea trout during the study period (July 20–Nov. 14, 2021, x-axis). Y-axis represents tagging site (1–9) and unique fish
ID. Vertical dashed line indicates when the flap weir was elevated (Sep. 15). Tagging site 1 was at confluence of the downstream river and the lake,
while tagging site 9 was close to the downstream river mouth
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Dahlmoetal. Movement Ecology (2023) 11:63
the elevation of the flap weir (Sep. 15, Figs.2 and 3). Six
sea trout descended from the lake to the downstream
river, in which 33% descended before (N = 2) and 77%
descended after (N = 4) the elevation of the flap weir.
us, the sea trout that ascended or descended after the
flap weir was elevated used the fish ladder. All sea trout
that remained in the river throughout the study ascended
from their tagging site.
e generalized linear model showed that there was
a significant effect of day of year on the number of sea
trout in the lake (model 1.1, z = 3.031, p = 0.002), such
that there were more sea trout in the lake later in the
study period compared to earlier in the study period.
Hypothesis 2: Eect ofhabitat onactivity
Sea trout were more active in the rivers than in the lake
(Fig. 4). e predicted average activity was 0.373 m/s2
(SD = 0.049, median = 0.373) in the rivers and 0.183m/
s2 (SD = 0.016, median = 0.185) in the lake. e AIC
test resulted in a lower AIC value (ΔAIC = 7014) for the
model with the autocorrelation term (Model 2.2) com-
pared to the model without (Model 2.1). Sea trout were
more active during the day than during the night in
both the lake and the rivers, however the effect size was
small with a difference of 0.045m/s2 between the least
(hour = 4) and most (hour = 14) active hour of the day in
the lake and a difference of 0.134m/s2 between the least
(hour = 4) and most (hour = 14) active hour of the day in
the rivers. ere was an overall decrease in the sea trout
activity in the rivers throughout the study period, while
the activity of sea trout in the lake slightly increased
towards mid-November when data were recovered.
Hypothesis 3: Eect ofhigh‑head storage plant discharge
onbehaviour inthelake
All 26 sea trout in the lake mainly utilised the upper
water column throughout the study period with an over-
all mean depth use of 3.7 m (SD = 3.7, median = 2.6).
e predicted spatial interaction revealed that sea trout
showed an overall uniform shallow depth use in the lake
(Fig. 5). However, most (81%) of the sea trout were at
some point detected at the tag depth limit (25.5m) dur-
ing the study period. Fish length did not affect depth use
in the lake. e first model that did not include the high-
head storage plant discharge (Model 3.1) had a lower
AIC (ΔAIC = 5895) than the model that included the dis-
charge (Model 3.1), suggesting that the addition of dis-
charge did not improve the model. ere was an effect of
individual variation in depth use. Six sea trout exploited
deeper parts of the lake to a larger extent than the
remaining sea trout. ere was a marginal effect of time
of day on depth use, such that sea trout were at deeper
Fig. 3 Number of sea trout moving between habitats or remained within one habitat throughout the study (July 20 to Nov. 14, 2021).
Movement between downstream river and lake before (blue) and after (orange) elevation of the flap weir (Sep. 15), movement between the lake
and the upstream river (red), and black points indicate how many sea trout that were only detected within the given habitat
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Dahlmoetal. Movement Ecology (2023) 11:63
depths at night. e deeper habitats were used more fre-
quently by sea trout as the study period progressed.
For the models on the effect of the storage dis-
charge on the activity of sea trout in the lake, the model
including storage discharge (Model 3.4) had a better fit
(ΔAIC = 5456) than the model without the discharge
(Model 3.3). e discharge, however, had a minimal effect
on the activity in the lake (F = 1.689, p = 0.19). ere was
a small increase in activity throughout the study period.
Time of day had a relatively small effect on the activity,
nevertheless, sea trout were more active during the day
than during the night. e sea trout activity was nega-
tively correlated with depth used such that they were less
active deeper in the lake. e predicted spatial interac-
tion on the activity of sea trout in the lake indicated that
sea trout were less active around the south and south-
western areas of the lake and more active in the northern
and eastern part of the lake (Fig.6). e highest activity
was in the eastern basin of the lake, where the outlet of
the upstream river Vassbygdelva is located. However,
there was an overall low activity level throughout the
lake.
Discussion
Vassbygdevatnet provided an important habitat for the
sea trout before spawning, supporting previous findings
from this lake [48]. e activity and depth of sea trout
were not affected by discharge from the high-head stor-
age plant. Ultimately, the results suggest a minimal effect
from the hydropower discharge on sea trout during the
period of study. Given that most sea trout inhabited the
lake during the spawning migration, including the period
of annual stock assessments by drift counting, prolonged
residence within the lake might conceal a significant part
of the sea trout population and cause an underestimation
of the spawning stock biomass.
Hypothesis 1: Habitat use
Most sea trout spent several days in the lake Vassby-
gdevatnet during the study, suggesting that the lake was
used not only as a transition path to the upstream river,
but provided an important habitat for the adult sea trout
during the spawning migration. e mechanisms under-
lying this use, however, were not clearly revealed from
this study. In rivers, pools are premium habitats used by
Fig. 4 The average activity (m/s2) of sea trout in the two habitats: lake ( Vassbygdevatnet) and river (Aurlandselva and Vassbygdelva). Colours
represent different sea trout individuals
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Dahlmoetal. Movement Ecology (2023) 11:63
migrating salmonids as refuge from temperature (Fre-
chette etal. 2018) and to minimise energy expenditure
[24], or as potential refuge from predators, such as Euro-
pean Otters (Lutra lutra) that preferably hunt at more
narrow and shallow sites [19, 75]. As an alternative to
pools, lakes such as Vassbygdevatnet can provide refuge
habitat suitable for a large fraction of the population. e
large number of sea trout inhabiting the lake indicates
that there is an advantage to seeking refuge in the lake
during the spawning migration compared to remaining in
the rivers. In theory, changes to the river flow regime can
affect the behaviour and distribution of fish in the water-
course, and reduce the availability of prey and spawning
habitats in the rivers (as in [6, 59, 60, 82, 86]). e water
level in the upstream river, Vassbygdelva, is unnaturally
low due to the hydropower regulations. When the flap
weir is elevated at the outlet of the lake (mid September
to end of April), the discharge in Aurlandselva is artifi-
cially low and nearly constant (min flow 3 m3/s, [80]). Sea
trout may therefore be more vulnerable to predation in
the river during this low flow period (e.g., by otters; [81]).
During summer, the hydropower regulations have also
caused a warming in the upstream river, Vassbygdelva,
coincident with a cooling in the downstream river, Aur-
landselva because of water abstraction and redistribution
in the system [67, 80]. Furthermore, both the down-
stream and upstream rivers are subject to angling during
summer. Consequently, the lake may be used as a refuge
by sea trout because of these anthropogenic stressors
or to avoid predators. An alternative explanation is that
lakes provide feeding grounds, which is observed among
pre-spawning trout in Norwegian lakes [2, 27, 38, 45].
Hanssen et al. [27] documented predation of Atlantic
salmon smolts by adult sea trout in lake Evangervatnet
after spawning (April-June). Additionally, the lake might
offer refuge for energy conservation or thermoregulation
(i.e., seek certain water temperatures) before spawning
[49, 51, 53]. Comparative studies between systems with
and without lakes, as well as experimental manipula-
tions of fish (e.g., displacement into or out of lakes) may
Fig. 5 Predicted spatial depth use from the generalized additive model on the effect of discharge on the depth use of sea trout in the lake
(log transformed, colour coded). Longitude and latitude on x-axis and y-axis, respectively. Warmer colour indicates deeper predicted depth use
in an area
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Dahlmoetal. Movement Ecology (2023) 11:63
help reveal the nature of the mechanistic relationships
between trout and these habitats.
Although brown trout exhibit a variety of life history
strategies (e.g., sea-run trout, freshwater residents; [43]),
the high prevalence of sea-run trout (i.e., sea trout) in the
lake in the present study is consistent with previous stud-
ies (e.g., [4, 40, 45]). In contrast, Atlantic salmon spend
less time in lakes than sea trout [42, 54], despite being
closely related. For instance, Atlantic salmon in the Vosso
river system mainly use the lakes as aid in migration
[54], while trout are abundant in the lake Evangervatnet
during springtime and feeding on salmon smolts [27,
30]. Because sea trout are morphologically less adapted
to strong water currents in rivers compared to Atlantic
salmon [41], these two species might use freshwater habi-
tats differently and the presence of lakes may therefore
alter the competitive landscape for the two sympatric
congeners. When sea trout and Atlantic salmon sympa-
trically inhabit river systems with lakes, competition for
resources and habitat might have caused a spatial segre-
gation whereby adult Atlantic salmon dominate in rivers
and sea trout dominate in lakes. Both sea trout and Atlan-
tic salmon inhabit the Aurland watercourse, however, the
abundance of spawners differs substantially between the
two species. In 2018, approximately 60 Atlantic salmon
spawners and 840 sea trout spawners were registered by
drift diving in the two rivers in Aurland [72, 73]. Atlantic
salmon roe is stocked by a hatchery in both the upstream
and downstream rivers [80], however the low abundance
of Atlantic salmon spawners indicates a high mortality
of Atlantic salmon at sea. e last stocking of sea trout
by a hatchery was conducted in 1999 in the Aurland
watercourse [80]. us, the lake may contribute to a bet-
ter adaptation of sea trout in the watercourse in Aurland
compared to Atlantic salmon.
Hypothesis 2: Eect ofhabitat onactivity
Sea trout were more active in the rivers than in the lake.
e lower activity of sea trout in the lake indicates that
the sea trout spent less energy in the lake than in the
rivers (e.g., [16, 20, 47]). e high survival rate of sea
Fig. 6 Predicted spatial activity from the generalized additive model on the effect of discharge on the activity of sea trout in the lake (log
transformed, colour coded). Longitude and latitude on x-axis and y-axis, respectively. Warmer colour indicates higher activity in an area
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Dahlmoetal. Movement Ecology (2023) 11:63
trout following spawning ([9,28] indicates that sea trout
exhibit a sufficient strategy for conserving and allocat-
ing their energy. Strategic allocation and conservation of
energy might be promoted by habitat preference whereby
they can limit behaviours that are energy-depleting.
Because the activity registered in the rivers in this study
is likely caused by the active movement required to
ascend rivers or maintain position against flowing water
[34], sea trout likely exploited the lake Vassbygdevatnet
as a habitat for energetic refuge [49, 53]. Energy expendi-
ture during migration ultimately reduces the energy that
can be used for reproduction [24].
ere was temporal variation in the activity of sea trout
in both lake- and river habitats. Sea trout exhibited an
increase in activity throughout the study period in the
lake that could be explained by spawning activity near the
end. Because the spawning period of sea trout in Aurland
lasts from October to early January (U. Pulg, unpublished
data), the higher activity of sea trout near the end of the
observation period could indicate spawning or spawn-
ing-related behaviour. Sea trout have been observed
spawning in lake Vassbygdevatnet in Aurland (U. Pulg,
unpublished data), and there are an increasing number
of studies that document spawning in lakes in sea trout
populations [12], such as in lake Røldalsvatnet, Norway
[14]. us, the seasonal increase in activity exhibited by
sea trout in the present study may potentially represent
spawning or spawning-related activity in the lake.
In contrast to the observed increasing activity in
the lake, there was a reduction in activity in the rivers
throughout the study period. After the flap weir at the
confluence of the lake and the river was elevated (Sep.
15), the water flow in the river was greatly reduced.
Reduced water flow can result in a greater difficulty
to migrate in rivers [79]. Berg and Berg [10] found that
larger-sized sea trout resided longer at sea when the
water level fell in August, which could indicate difficulty
to migrate upriver. Alternatively, adult sea trout com-
monly seek deep pools in rivers [5, 18], where there is a
lower necessity to be active due to reduced water flow.
e flap weir itself also represents a migration barrier.
e fish ladder is passable for fish, however bypass-fish-
ways may restrict fish migration because they are not
always easy to find [25]. Moreover, behavioural patterns,
such as aggressive males at spawning sites at the fishway’s
entrance may restrict fish migration. Hence, the hydro-
power regulations may partially explain the reduced
activity of sea trout in the river.
e diel activity of sea trout was similar in the lake and
the rivers. Sea trout were consistently more active during
the day than at night in both habitats. Other studies have
mostly found nocturnal or crepuscular peaks in activity
of sea trout [13, 15, 18, 55, 93]; Barry etal. 2020), which
is consistent with the diel activity of other salmonids
(e.g., [29, 33, 35]). Fish are thought to be least active dur-
ing the day to minimise the risk of predation by otters,
birds, or piscivorous fish species. Hence, the higher activ-
ity observed during midday in both the lake and river
habitats in this study contradicts theory. A higher activity
of sea trout during the day in lake Vassbygdevatnet may
indicate a low predation pressure on the relatively large
sea trout. Alternatively, the higher activity during the
day than during the night might be due to spawning or
spawning-related movement (e.g., searching for spawn-
ing grounds), as have been demonstrated with Chinook
salmon [50].
Receivers have a varied range that is affected by envi-
ronment and climatic conditions, and swift currents
likely reduce the range due to more background noise
compared to calmer water areas. River receivers were
therefore placed in relatively calm areas. e implication
is that there is a potential bias in which we miss detec-
tions from areas where the sea trout is active, such as
spawning grounds. Nevertheless, migrating fish spend
most of their time holding and not actively navigat-
ing rapids or cascades that are energetically challenging
[95]. However, the array still functioned well for provid-
ing a comparison between the rivers and lake as holding
areas as the sea trout staged in areas for weeks or months
ahead of spawning.
Hypothesis 3: Eect ofhigh‑head storage plant discharge
onbehaviour inthelake
Sea trout were mostly found near the surface of the lake
but showed individual variation in depth use. e vary-
ing vertical habitat used among sea trout (i.e., random
effect intercept) was larger than the effect of the other
parameters and contributed to explaining a large part of
the variation in the data. Six sea trout used deeper depths
than the remaining sea trout throughout the study. e
individual variation in depth use is potentially a result of
differences in personalities among sea trout. For instance,
the ‘shy-bold continuum’ proposed by Wilson etal. [89]
suggests that personality traits affect the observed behav-
ioural variations among individuals. For the vertical
behaviour of sea trout in lake Vassbygdevatnet, the ‘shy-
bold continuum’ can potentially contribute to explaining
the individual variation in depth preference. Shy indi-
viduals, compared to bold individuals, are more likely to
remain at deeper depths to limit their exposure to threats
(e.g., fishing, terrestrial or avian predators). Additionally,
the individual vertical movement differences observed in
the present study might be a result of individual fitness
because vertical movement is costly [78] or a result of
food availability.
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Dahlmoetal. Movement Ecology (2023) 11:63
e high-head storage plant discharge did not influence
depth use of sea trout in the lake. ere were, however,
temporal effects on the depth use of sea trout in the lake,
such that sea trout displayed greater depth use as the
study progressed. In comparison, the diel temporal effect
on the depth use in the lake was smaller. Sea trout were
detected more frequently at shallow water depth dur-
ing the day than during the night, which is aligned with
the activity peak of the sea trout in the present study.
Because sea trout are visual feeders [43], sea trout might
utilise daylight to feed at the surface.
e high-head storage plant discharge did not affect
the activity of sea trout in the lake. Because the inflow of
water from the high-head storage plant affects the strati-
fication of the lake [80], and temperature is closely related
to energy consumption and activity [17], it is likely that
there is an effect of discharge on the activity of sea trout
that is not accounted for by the change in discharge.
Although the addition of discharge improved the model
on the activity of sea trout in the lake, the effect was
small, and the shape of the fit was seemingly impacted
by a few extreme values of discharge that were rarely
encountered by the sea trout during the study period.
us, the discharge from the high-head storage plant had
no evident effect on the activity of sea trout in the lake.
e higher activity of sea trout observed in the east-
ern part of the lake may indicate that there was an effect
from the high-head storage plant discharge, despite the
model not accounting for the discharge location directly.
e additional supply of water from the high-head stor-
age plant into the surface layer of the lake caused a higher
surface flow that could result in an increase in sea trout
activity, particularly around the discharge area. Swim-
ming towards discharging water will require higher activ-
ity, similar to the demands of holding position against the
flow in a river. us, it is likely that the observed increase
in activity around the Aurland 1 discharge is related to
the outflow of water.
Implications formanagement
e large prevalence of sea trout inhabiting the lake dur-
ing the spawning migration demonstrates that the lake
provided an important habitat for sea trout, where they
likely conserved energy and found refuge from predators
prior to spawning. Based on factors, such as hydropower
regulations, overfishing, and sea lice from open net pen
aquaculture in the ords, assessment of Norwegian sea
trout populations has concluded that only 25% of the
populations are in a good condition [83, 84]. e sea
trout assessment is based on drift diving in rivers [74].
Given that most sea trout inhabited the lake during the
spawning migration, including the period of annual stock
assessments, the lake might conceal a significant part of
the sea trout population and cause an underestimation
of the spawning stock biomass. us, lake-residing fish
should be taken into consideration when management
efforts are made based on spawning stocks. For exam-
ple, stock assessment of several Norwegian river systems
might be underestimated given that about 30% of river
systems in Norway contain lakes [27].
With the increasing demand of renewable energy,
lakes are likely to become increasingly exploited as res-
ervoirs for hydropower [32]. Given that lakes provide
such important habitat for sea trout, effects of hydro-
power on this habitat may render sea trout particularly
vulnerable. However, the effect of hydropower regula-
tions on the lake ecology of salmonids is poorly docu-
mented [46], despite being among the most frequently
studied fish species globally [12]. Because sea trout and
Atlantic salmon exhibit different life history strategies
[43], hydropower mitigation efforts based on the ecol-
ogy of Atlantic salmon can misrepresent the require-
ments of sea trout. Consequently, current management
mitigations and regulations might not be sufficient if
they fail to consider the unique ecology of trout. us,
management and the hydropower industry should fur-
ther invest in research on the lake ecology of sea trout
to provide necessary knowledge on the requirements of
sea trout populations.
Conclusion
is study demonstrated that the lake offered an
important habitat for sea trout during their spawn-
ing migration based on acoustic detections and posi-
tion calculations with YAPS. e activity of sea trout
was higher in the rivers than in the lake, indicating
that the lake offered a refuge where sea trout could
conserve energy during the holding phase of migra-
tion as the fish prepared for spawning. Additionally,
there was a seasonal difference in activity of sea trout
between the lake and river habitats; the activity of
sea trout peaked earlier in the rivers than in the lake.
is could indicate that spawning or spawning-related
movement might have occurred in the lake as the
spawning period approached. ere was not an effect
of discharge from the high-head storage plant on depth
use or activity of sea trout in the lake. Our results indi-
cate thattrout have low activity in the lake compared
to the river and may use lake habitats as a refuge dur-
ing their stay in freshwater, which may have carryover
benefits to the animals that use the lake, which have
not yet been revealed from this research. In a regulated
river where the hydrodynamic condition is altered, one
could expect trout to use the lake more. Although we
have revealed little direct impact of the discharge from
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Page 13 of 15
Dahlmoetal. Movement Ecology (2023) 11:63
the hydropower plant, further research on the effect of
storage plants and its facilities on the lake and migra-
tion behaviour of fish is needed.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s40462- 023- 00429-7.
Additional le1: Figure S1. Average discharge data from the high-head
storage plant ‘Aurland 1’ during the study period, July 20. to Nov. 14., 2021.
Average daily discharge data (dark blue) and overall average discharge
during the study period (light blue straight line). Time on x-axis and water
discharge (m3/s) on y-axis.
Acknowledgements
The project was funded by the Norwegian Research Council LaKES Project
(320726), 80% financed by the state and 20% by two industry partners (includ-
ing Hafslund ECO operating in Aurland).
Author contributions
LSD: project design, data collection, data analysis, writing, editing. GV: writing,
editing. CIN: data collection, editing. UP: editing, knowledge on study site. RJL:
project design, data collection, data analysis, writing, editing. KWV: project
design, data collection, data analysis, writing, editing.
Funding
The project was funded by the Norwegian Research Council (LaKES pr.nr.
320726), Eviny, and Hafslund Eco.
Availability of data and materials
All telemetry data are available through the Ocean Tracking Network.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
There are no interests in competition with the results of this research declared
by the research team.
Received: 23 December 2022 Accepted: 3 October 2023
References
1. Alfredsen K, Amundsen PA, Hahn L, et al. A synoptic history of the
development, production and environmental oversight of hydropower in
Brazil, Canada, and Norway. Hydrobiologia. 2022;849:269–80. https:// doi.
org/ 10. 1007/ s10750- 021- 04709-4.
2. Amundsen PA, Knudsen R. Winter ecology of Arctic charr (Salve-
linus alpinus) and brown trout (Salmo trutta) in a subarctic lake,
Norway. Aquat Ecol. 2009;43:765–75. https:// doi. org/ 10. 1007/
s10452- 009- 9261-8.
3. Anderson D, Moggridge H, Warren P, Shucksmith J. The impacts of ‘run-
of-river’ hydropower on the physical and ecological condition of rivers.
Water Environ J. 2015;29:268–76. https:// doi. org/ 10. 1111/ wej. 12101.
4. Andersson A, Greenberg LA, Bergman E, Su Z, Andersson M, Piccolo JJ.
Recreational trolling effort and catch of Atlantic salmon and brown trout
in Vänern, the EU’s largest lake. In Fisheries Research 2020;(Vol. 227, p.
105548). Elsevier BV. https:// doi. org/ 10. 1016/j. fishr es. 2020. 105548
5. Arnekleiv JV, Rønning L. Migratory patterns and return to the catch site
of adult brown trout (Salmo trutta L.) in a regulated river. River Res Appl.
2004;20:929–42. https:// doi. org/ 10. 1002/ rra. 799.
6. Banks JW. A review of the literature on the upstream migration of adult
salmonids. J Fish Biol. 1969;1:85–136.
7. Baktoft H, Gjelland KØ, Økland F, Thygesen UH. Positioning of aquatic
animals based on time-of-arrival and random walk models using YAPS
(Yet Another Positioning Solver). Sci Rep. 2017;7(1):1–10.
8. Belletti B, Garcia de Leaniz C, Jones J, Bizzi S, Börger L, Segura G, Castel-
letti A, van de Bund W, Aarestrup K, Barry J, Belka K, Berkhuysen A,
Birnie-Gauvin K, Bussettini M, Carolli M, Consuegra S, Dopico E, Feierfeil
T, Fernández S, Giannico G. More than one million barriers fragment
Europe’s rivers. Nature (London) 2020;588(7838), 436–441. https:// doi.
org/ 10. 1038/ s41586- 020- 3005-2
9. Bendall B, Moor A, Quayle V. The post‐spawning movements of migratory
brown trout Salmo trutta L. J Fish Biol. 2005;67(3):809–822.
10. Berg OK, Berg M. Sea growth and time of migration of anadromous Arctic
char (Salvelinus alpinus) from the Vardnes River, in northern Norway. Can
J Fish Aquat Sci. 1989;46(6):955–60.
11. Birnie-Gauvin K, Candee M, Baktoft H, Larsen M, Koed A, Aarestrup K.
River connectivity reestablished: Effects and implications of six weir
removals on brown trout smolt migration. River Res Appl. 2018;34(6):548–
54. https:// doi. org/ 10. 1002/ rra. 3271.
12. Birnie-Gauvin K, Thorstad EB, Aarestrup K. Overlooked aspects of the
Salmo salar and Salmo trutta lifecycles. Rev Fish Biol Fish. 2019;29:749–66.
https:// doi. org/ 10. 1007/ s11160- 019- 09575-x.
13. Björnsson B. Diel changes in the feeding behaviour of Arctic char (Salve-
linus alpinus) and brown trout (Salmo trutta) in Ellidavatn, a small lake in
southwest Iceland. Limnologica. 2001;31(4):281–8.
14. Brabrand Å, Koestler AG, Borgstrøm R. Lake spawning of brown trout
related to groundwater influx. J Fish Biol. 2002;60:751–63. https:// doi. org/
10. 1111/j. 1095- 8649. 2002. tb016 99.x.
15. Bremset G. Seasonal and Diel Changes in Behaviour, Microhabitat use
and Preferences by Young Pool-dwelling Atlantic Salmon, Salmo salar,
and Brown Trout, Salmo Trutta. Environ Biol Fishes. 2000;59:163–79.
https:// doi. org/ 10. 1023/A: 10076 91316 864.
16. Briggs CT, Post JR. In situ activity metabolism of rainbow trout (Onco-
rhynchus mykiss): estimates obtained from telemetry of axial muscle
electromyograms. Can J Fish Aquat Sci. 1997;54(4):859–66. https:// doi.
org/ 10. 1139/ cjfas- 54-4- 859.
17. Brown JH, Gillooly JF, Allen AP, Savage VM, West GB. Toward a metabolic
theory of ecology. Ecology. 2004;85:1771–89. https:// doi. org/ 10. 1890/
03- 9000.
18. Bunnell DB Jr, Isely JJ, Burrell KH, Van Lear DH. Diel Movement of Brown
Trout in a Southern Appalachian River. Trans Am Fish Soc. 1998;127:630–
6. https:// doi. org/ 10. 1577/ 1548- 8659(1998) 127% 3c0630: DMOBTI% 3e2.0.
CO.
19. Cho HS, Choi KH, Lee SD, Park YS. Characterizing habitat preference of
Eurasian river otter (Lutra lutra) in streams using a self-organizing map.
Limnology. 2009;10:203–13. https:// doi. org/ 10. 1007/ s10201- 009- 0275-7.
20. Cooke SJ, Brownscombe JW, Raby GD, Broell F, Hinch SG, Clark TD, Sem-
mens JM. Remote bioenergetics measurements in wild fish: opportuni-
ties and challenges. Comp Biochem Physiol Part A Mol Integr Physiol.
2016;202:23–37. https:// doi. org/ 10. 1016/j. cbpa. 2016. 03. 022.
21. Cuthbert RN, Diagne C, Hudgins EJ, Turbelin A, Ahmed DA, Albert
C, Bodey TW, Briski E, Essl F, Haubrock PJ, Gozlan RE, Kirichenko N,
Kourantidou M, Kramer AM, Courchamp F. Biological invasion costs
reveal insufficient proactive management worldwide. Sci Total Environ.
2022;819:153404–153404. https:// doi. org/ 10. 1016/j. scito tenv. 2022.
153404.
22. Dudgeon D, Arthington AH, Gessner MO, Kawabata Z-I, Knowler DJ,
Lévêque C, Naiman RJ, Prieur-Richard A-H, Soto D, Stiassny MLJ, Sullivan
CA. Freshwater biodiversity: importance, threats, status and conservation
challenges. Biol Rev Camb Philos Soc. 2006;81(2):163–82. https:// doi. org/
10. 1017/ S1464 79310 50069 50.
23. Farrell AP, Lee CG, Tierney K, Hodaly A, Clutterham S, Healey M, Hinch S,
Lotto A. Field-based measurements of oxygen uptake and swimming
performance with adult Pacific salmon using a mobile respirometer swim
tunnel. J Fish Biol. 2003;62:64–84. https:// doi. org/ 10. 1046/j. 1095- 8649.
2003. 00010.x.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 14 of 15
Dahlmoetal. Movement Ecology (2023) 11:63
24. Fenkes M, Shiels HA, Fitzpatrick JL, Nudds RL. The potential impacts of
migratory difficulty, including warmer waters and altered flow conditions,
on the reproductive success of salmonid fishes. Comp Biochem Physiol A
Mol Integr Physiol. 2016;193:11–21.
25. Fjeldstad HP, Pulg U, Forseth T. Safe two-way migration for salmonids
and eel past hydropower structures in Europe: a review and recom-
mendations for best-practice solutions. Marine Freshwater Res.
2018;69(12):1834–1847.
26. Grill G, Lehner B, Thieme M, Geenen B, Tickner D, Antonelli F, Babu S,
Borrelli P, Cheng L, Crochetiere H, Ehalt Macedo H, Filgueiras R, Goichot
M, Higgins J, Hogan Z, Lip B, McClain ME, Meng J, Mulligan M, Zarfl C.
Mapping the world’s free-flowing rivers. Nature. 2019;569(7755):215–21.
https:// doi. org/ 10. 1038/ s41586- 019- 1111-9.
27. Hanssen EM, Vollset KW, Salvanes AGV, Barlaup B, Whoriskey K, Isaksen TE,
Normann ES, Hulbak M, Lennox RJ. Acoustic telemetry predation sensors
reveal the tribulations of Atlantic salmon ( Salmo salar ) smolts migrating
through lakes. Ecol Freshw Fish. 2022. https:// doi. org/ 10. 1111/ eff. 12641.
28. Haraldstad T, Höglund E, Kroglund F, Lamberg A, Olsen EM, Haugen TO.
Condition-dependent skipped spawning in anadromous brown trout
(Salmo trutta). Can J Fish Aquat Sci. 2018;75(12):2313–9. https:// doi. org/
10. 1139/ cjfas- 2017- 0076.
29. Harrison PM, Gutowsky LFG, Martins EG, Patterson DA, Leake A, Cooke
SJ, Power M. Diel vertical migration of adult burbot: a dynamic trade-off
among feeding opportunity, predation avoidance, and bioenergetic gain.
Can J Fish Aquat Sci. 2013;70(12):1765–74.
30. Haugen TO, Kristensen T, Nilsen TO, Urke HA. Vandringsmønsteret til
laksesmolt i Vossovassdraget med vekt på detaljert kartlegging av åtferd i
innsjøsystema og effektar av miljøtilhøve. MINA Fagrapport. 2017;41:85.
31. Heggenes J, Stickler M, Alfredsen K, Brittain JE, Adeva-Bustos A,
Huusko A. Hydropower-driven thermal changes, biological responses
and mitigating measures in northern river systems. River Res Appl.
2021;2021(37):743–65. https:// doi. org/ 10. 1002/ rra. 3788.
32. Hirsch PE, Eloranta AP, Amundsen PA, et al. Effects of water level regula-
tion in alpine hydropower reservoirs: an ecosystem perspective with a
special emphasis on fish. Hydrobiologia. 2017;794:287–301. https:// doi.
org/ 10. 1007/ s10750- 017- 3105-7.
33. Huusko A, Greenberg L, Stickler M, Linnansaari T, Nykänen M, Vehanen T,
Koljonen S, Louhi P, Alfredsen K. Life in the ice lane: the winter ecology
of stream salmonids. River Res Appl. 2007;23:469–91. https:// doi. org/ 10.
1002/ rra. 999.
34. Hynes HBN. The ecology of running waters. Liverpool University Press;
1970.
35. Jakober MJ, McMahon TE, Thurow RF. Diel habitat partitioning by bull
charr and cutthroat trout during fall and winter in rocky mountain
streams. Environ Biol Fishes. 2000;59:79–89. https:// doi. org/ 10. 1023/A:
10076 99610 247.
36. Jeffrey JD, Hasler CT, Chapman JM, Cooke SJ, Suski CD. Linking landscape-
scale disturbances to stress and condition of fish: implications for restora-
tion and conservation. In Integrative and Comparative Biology 2015; (Vol.
55, Issue 4, pp. 618–630). Oxford University Press (OUP). https:// doi. org/
10. 1093/ icb/ icv022
37. Jensen AJ, Johnsen BO, Møkkelgjerd PI. Sjøaure og laks i Aurlandsvassdra-
get 1911–92. NINA Forskningsrapport. 1993;48:1–31.
38. Jensen H, Kiljunen M, Amundsen P-A. Dietary ontogeny and niche shift
to piscivory in lacustrine brown trout Salmo trutta revealed by stomach
content and stable isotope analyses. J Fish Biol. 2012;80:2448–62. https://
doi. org/ 10. 1111/j. 1095- 8649. 2012. 03294.x.
39. Johnson JB, Omland KS. Model selection in ecology and evolution. Trends
Ecol Evol. 2004;19(2):101–8. https:// doi. org/ 10. 1016/j. tree. 2003. 10. 013.
40. Jonsson B. Life history and habitat use of Norwegian brown trout (Salmo
trutta). Freshw Biol. 1989;21:71–86. https:// doi. org/ 10. 1111/j. 1365- 2427.
1989. tb013 49.x.
41. Jonsson B, Jonsson N. Ecology of Atlantic Salmon and Brown Trout.
Springer, Netherlands. 2011. https:// doi. org/ 10. 1007/ 978- 94- 007- 1189-1.
42. Kennedy R, Allen M. The pre-spawning migratory behaviour of
Atlantic salmon Salmo salar in a large lacustrine catchment. J Fish Biol.
2016;89:1651–65. https:// doi. org/ 10. 1111/ jfb. 13068.
43. Klemetsen A, Amundsen P-A, Dempson JB, Jonsson B, Jonsson N,
O’Connell MF, Mortensen E. Atlantic salmon Salmo salar L., brown trout
Salmo trutta L. and Arctic charr Salvelinus alpinus (L.): a review of aspects
of their life histories. Ecol Freshw Fish. 2003;12(1):1–59. https:// doi. org/ 10.
1034/j. 1600- 0633. 2003. 00010.x.
44. Koed A, Birnie-Gauvin K, Sivebæk F, Aarestrup K. From endangered to
sustainable: multi-faceted management in rivers and coasts improves
Atlantic salmon (Salmo salar) populations in Denmark. Fish Manag Ecol.
2019;27:64–76. https:// doi. org/ 10. 1111/ fme. 12385.
45. L’Abée-Lund JH, Langeland A, Sægrov H. Piscivory by brown trout Salmo
trutta L. and Arctic charr Salvelinus alpinus (L.) in Norwegian lakes. J Fish
Biol. 1992;41:91–101. https:// doi. org/ 10. 1111/j. 1095- 8649. 1992. tb031 72.x.
46. Lennox RJ, Pulg U, Malley B, Gabrielsen SE, Hanssen SM, Cooke SJ, Birnie-
Gauvin K, Barlaup BT, Vollset KW. The various ways that anadromous sal-
monids use lake habitats to complete their life history. Can J Fish Aquat
Sci. 2021;78(1):90–100. https:// doi. org/ 10. 1139/ cjfas- 2020- 0225.
47. Lowe CG, Holland KN, Wolcott TG. A new acoustic tailbeat transmitter
for fishes. Fish Res. 1998;36(2):275–83. https:// doi. org/ 10. 1016/ S0165-
7836(98) 00109-X.
48. Lunde R. Lake-habitat use of post-juvenile sea trout over time and
space—An acoustic telemetry study in a regulated river. Master’s thesis.
Norwegian University of Life Sciences, Ås;2014.
49. Mathes MT, Hinch SG, Cooke SJ, Crossin GT, Patterson DA, Lotto AG, Farrell
AP. Effect of water temperature, timing, physiological condition, and
lake thermal refugia on migrating adult Weaver Creek sockeye salmon
(Oncorhynchus nerka). Can J Fish Aquat Sci. 2010;67(1):70–84. https:// doi.
org/ 10. 1139/ F09- 158.
50. McMichael GA, McKinstry CA, Vucelick JA, Lukas JA. Fall chinook salmon
spawning activity versus daylight and flow in the tailrace of a large
hydroelectric dam. N Am J Fish Manag. 2005;25:573–80. https:// doi. org/
10. 1577/ M04- 044.1.
51. Mulder IM, Morris CJ, Dempson JB, Fleming IA, Power M. Overwinter ther-
mal habitat use in lakes by anadromous Arctic char. In Canadian Journal
of Fisheries and Aquatic Sciences 2018; (Vol. 75, Issue 12, pp. 2343–2353).
Canadian Science Publishing. https:// doi. org/ 10. 1139/ cjfas- 2017- 0420
52. Mulder IM, Dempson JB, Fleming IA, Power M. Diel activity patterns
in overwintering Labrador anadromous Arctic charr. Hydrobiologia.
2019;840:89–102. https:// doi. org/ 10. 1007/ s10750- 019- 3926-7.
53. Newell JC, Quinn TP. Behavioral thermoregulation by maturing adult
sockeye salmon (Oncorhynchus nerka) in a stratified lake prior to spawn-
ing. Can J Zool. 2005;83(9):1232–9.
54. Nilsen CI, Vollset KW, Velle G, Barlaup BT, Normann ES, Stöger E, Lennox
RJ. Atlantic salmon of wild and hatchery origin have different migration
patterns. Can J Fish Aquat Sci. 2022;80(4):690–9. https:// doi. org/ 10. 1139/
cjfas- 2022- 0120.
55. Ovidio M, Baras E, Goffaux D, Giroux F, Philippart JC. Seasonal variations of
activity pattern of brown trout (Salmo trutta) in a small stream, as deter-
mined by radio-telemetry. Hydrobiologia. 2002;470(1):195–202. https://
doi. org/ 10. 1023/A: 10156 25500 918.
56. Palstra A, Kals J, Böhm T, Bastiaansen JW, Komen H. Swimming perfor-
mance and oxygen consumption as non-lethal indicators of produc-
tion Traits in Atlantic Salmon and Gilthead Seabream. Front Physiol.
2020;11:759–759. https:// doi. org/ 10. 3389/ fphys. 2020. 00759.
57. Pedersen EJ, Miller DL, Simpson GL, Ross N. Hierarchical generalized addi-
tive models in ecology: an introduction with mgcv. PeerJ. 2019;7:876.
58. Peiman KS, Birnie-Gauvin K, Midwood JD, Larsen MH, Wilson ADM, Aare-
strup K, Cooke SJ. If and when: intrinsic differences and environmental
stressors influence migration in brown trout (Salmo trutta). Oecologia.
2017;184(2):375–84. https:// doi. org/ 10. 1007/ s00442- 017- 3873-9.
59. Poff NL, Allan JD, Bain MB, Karr JR, Prestegaard KL, Richter BD, Sparks RE,
Stromberg JC. The natural flow regime. Bioscience. 1997;47(11):769–84.
https:// doi. org/ 10. 2307/ 13130 99.
60. Poff NL, Hart DD. How dams vary and why it matters for the emerging
science of dam removal. Bioscience. 2002;52(8):659–68. https:// doi. org/
10. 1641/ 0006- 3568(2002) 052[0659: HDVAWI] 2.0. CO;2.
61. Pulg U, Barlaup BT, Sternecker K, Trepl L, Unfer G. Restoration of spawning
habitats of brown trout (Salmo trutta) in a regulated chalk stream. River
Res Applic. 2011;29:172–82. https:// doi. org/ 10. 1002/ rra. 1594.
62. Pulg U, Stranzl S, Olsen EE, Postler C. Vanndekt areal, habitatkvalitet og
vannføring i Vassbygdelva (2020). NORCE LFI rapport 379. NORCE LFI,
Bergen. (In Norwegian).
63. Pulg U, Lennox RJ, Stranzl S, Espedal EO, Gabrielsen SE, Wiers T, Velle G,
Hauer C, Dønnum BO, Barlaup BT. Long-term effects and cost-benefit
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

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analysis of eight spawning gravel augmentations for Atlantic salmon and
Brown trout in Norway. Hydrobiologia. 2022;849:485–507. https:// doi. org/
10. 1007/ s10750- 021- 04646-2.
64. R Core Team. R: A language and environment for statistical computing.
R Foundation for Statistical Computing, Vienna, Austria;2021. https://
www.R- proje ct. org/.
65. Reid AJ, Carlson AK, Creed IF, Eliason EJ, Gell PA, Johnson PTJ, Kidd KA,
MacCormack TJ, Olden JD, Ormerod SJ, Smol JP, Taylor WW, Tockner K,
Vermaire JC, Dudgeon D, Cooke SJ. Emerging threats and persistent con-
servation challenges for freshwater biodiversity. Biol Rev. 2019;94:849–73.
https:// doi. org/ 10. 1111/ brv. 12480.
66. Roni P, Hanson K, Beechie T. Global review of the physical and biological
effectiveness of stream habitat rehabilitation techniques. N Am J Fish
Manag. 2008;28(3):856–90. https:// doi. org/ 10. 1577/ M06- 169.1.
67. Saltveit SJ. Økologiske forhold i vassdrag: konsekvenser av vannføring-
sendringer: en sammenstilling av dagens kunnskap (in Norwegian). Oslo:
Norges vassdrags- og energidirektorat; 2006.
68. Schloerke B, Cook D, Larmarange J, Briatte F, Marbach M, Thoen E, Elberg
A, Crowley J. GGally: Extension to ’ggplot2’. R package version 2.1.2; 2021.
https:// CRAN.R- proje ct. org/ packa ge= GGally
69. Schwinn M, Aarestrup K, Baktoft H, Koed A. Survival of migrating sea trout
(Salmo trutta) smolts during their passage of an artificial lake in a danish
lowland stream. River Res Appl. 2017;33:558–66. https:// doi. org/ 10. 1002/
rra. 3116.
70. Simpfendorfer CA, Huveneers C, Steckenreuter A, Tattersall K, Hoenner
X, Harcourt R, Heupel MR. Ghosts in the data: false detections in VEMCO
pulse position modulation acoustic telemetry monitoring equipment.
Anim Biotelem. 2015;3:55. https:// doi. org/ 10. 1186/ s40317- 015- 0094-z.
71. Simpson G. gratia: Graceful ggplot-Based Graphics and Other Functions
for GAMs Fitted using mgcv. R package version 0.6.0; 2021. https:// gavin
simps on. github. io/ gratia/.
72. Skoglund H, Vollset KW, Barlaup B, Lennox R. Gytefisktelling av laks
og sjøaure på Vestlandet status og utvikling i perioden 2004–2018.
NORCE LFI rapport 357. Norwegian Research Centre Laboratorium for
ferskvannsøkologi og innlandsfiske; 2019a.
73. Skoglund H, Wiers T, Normann ES, Stranzl S, Landro Y, Pulg U, Velle G,
Gabrielsen SE, Lehman GB, Barlaup BT. Gytefisktelling av laks og sjøaure
og uttak av rømt oppdrettslaks i 49 elver på Vestlandet høsten 2018.
NORCE LFI rapport 359. Norwegian Research Centre Laboratorium for
ferskvannsøkologi og innlandsfiske; 2019b.
74. Skoglund H, Vollset KW, Lennox R, Skaala Ø, Barlaup BT. Drift diving: a
quick and accurate method for assessment of anadromous salmonid
spawning populations. Fish Manag Ecol. 2021;28(5):478–85. https:// doi.
org/ 10. 1111/ fme. 12491.
75. Sortland LK, Lennox RJ, Velle G, Vollset KW, Kambestad M. Impacts of
predation by Eurasian otters on Atlantic salmon in two Norwegian rivers.
Freshw Biol 2023. https:// doi. org/ 10. 1111/ fwb. 14095
76. Smircich MG, Kelly JT. Extending the 2% rule: the effects of heavy internal
tags on stress physiology, swimming performance, and growth in brook
trout. Anim Biotelem. 2014;2:16. https:// doi. org/ 10. 1186/ 2050- 3385-2- 16.
77. Stanford JA, Ward JV, Liss WJ, Frissell CA, Williams RN, Lichatowich JA,
Coutant CC. A general protocol for restoration of regulated rivers. Regul
Rivers Res Mgmt. 1996;12:391–413. https:// doi. org/ 10. 1002/ (SICI) 1099-
1646(199607) 12:4/ 5% 3c391:: AID- RRR436% 3e3.0. CO;2-4.
78. Strand E, Jørgensen C, Huse G. Modelling buoyancy regulation in
fishes with swimbladders: bioenergetics and behaviour. Ecol Model.
2005;185(2):309–27. https:// doi. org/ 10. 1016/j. ecolm odel. 2004. 12. 013.
79. Thorstad EB, Økland F, Kroglund F, Jepsen N. Upstream migration of
Atlantic salmon at a power station on the River Nidelva, Southern
Norway. Fish Manag Ecol. 2003;10:139–46. https:// doi. org/ 10. 1046/j. 1365-
2400. 2003. 00335.x.
80. Ugedal O, Pulg U, Skoglund H, Charmasson J, Espedal EO, Jensås JG,
Stranzl S, Harby A, Forseth T. Sjøaure og laks i Aurlandsvassdraget
2009–2018. Reguleringseffekter, miljødesign og tiltak (No. NINA Rapport
1716). Norsk institutt for naturforskning;2019.
81. Van Dijk J, Kambestad M, Carss DC, Hamre Ø. Kartlegging av oterens
effekt på bestander av laks og sjøørret—Sunnmøre. NINA Rapport 1780.
Norsk institutt for naturforskning; 2020.
82. Vannote RL, Minshall GW, Cummins KW, Sedell JR, Cushing CE. The river
continuum concept. Can J Fish Aquat Sci. 1980;37(1):130–7.
83. VRL (Vitenskapelig råd for lakseforvaltning). (2019). Klassifisering av
tilstanden til 430 norske sjøørretbestander. Temarapport fra Vitenskapelig
råd for lakseforvaltning nr 7, 150 s.
84. VRL (Vitenskapelig råd for lakseforvaltning). (2022). Klassifisering av
tilstanden til sjøørret i 1279 vassdrag. Temarapport fra Vitenskapelig råd
for lakseforvaltning nr 9, 170 s.
85. Werner EE, Anholt BR. Ecological consequences of the trade-off between
growth and mortality rates mediated by foraging activity. Am Nat.
1993;142(2):242–72.
86. Westrelin S, Roy R, Tissot-Rey L, Bergès L, Argillier C. Habitat use and
preference of adult perch (Perca fluviatilis L.) in a deep reservoir:
variations with seasons, water levels and individuals. Hydrobiologia.
2017;809(1):121–39. https:// doi. org/ 10. 1007/ s10750- 017- 3454-2.
87. Wickham H. ggplot2: elegant graphics for data analysis. New York:
Springer-Verlag; 2016.
88. Wickham H, François R, Henry L, Müller K. dplyr: a grammar of data
manipulation. R package version 1.0.9;2022. https:// CRAN.R- proje ct. org/
packa ge= dplyr
89. Wilson DS, Coleman K, Clark AB, Biederman L. Shy-bold continuum in
pumpkinseed sunfish (Lepomis gibbosus): An ecological study of a
psychological trait. In Journal of Comparative Psychology (Vol. 107, Issue
3, pp. 250–260);1993. American Psychological Association (APA). https://
doi. org/ 10. 1037/ 0735- 7036. 107.3. 250
90. Wood SN. Generalized Additive Models: An Introduction with R (2nd edi-
tion). Chapman and Hall/CRC;2017.
91. WWF. Living Planet Report 2020—Bending the curve of biodiver-
sity loss. In: Almond REA, Grooten M, Petersen T (Eds). WWF, Gland,
Switzerland;2020.
92. Zuur AF, Ino EN, Walker NJ, Saveliev AA, Smith GM. Mixed effects models
and extensions in ecology with R. New York: Springer; 2009.
93. Young MK. Summer diel activity and movement of adult brown trout
in high-elevation streams in Wyoming, USA. J Fish Biol. 1999;54:181–9.
https:// doi. org/ 10. 1111/j. 1095- 8649. 1999. tb006 21.x.
94. Økland F, Jensen AJ, Johnsen BO. Vandring hos radiomerket ørret i
Aurlandsvassdraget.- Vandrer sjøørret inn i Vangen kraftverk? NINA Opp-
dragsmelding. 1995;337:1–19.
95. Økland F, Erkinaro J, Moen K, Niemelä E, Fiske P, McKinley R, Thorstad EB.
Return migration of Atlantic Salmon in the River Tana: phases of migra-
tory behaviour. J Fish Biol. 2001;59:862–74. https:// doi. org/ 10. 1111/j.
1095- 8649. 2001. tb001 57.x.
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