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Swimming Activity of Gilthead Seabream (Sparus aurata) in Swim-Tunnels: Accelerations, Oxygen Consumption and Body Motion


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Acoustic accelerometry is considered a useful tool to study swimming activity, including energetics and movement patterns, of fish species in aquaculture and in nature. However, given the novelty of this technique, further laboratory-derived calibrations are needed to assess the characteristics and settings of transmitters for different species and specific environmental conditions. In this study, we compared acoustic accelerometer outputs with swimming performance and body motion of gilthead seabream (Sparus aurata L.) in swim-tunnels at different flow speeds, which allowed us to characterize the swimming activity of this fish species of high aquaculture interest. Tag implantation in the abdominal cavity had no significant effects on swimming performance and body motion parameters. Accelerations, cost of transport and variations on head orientation (angle with respect to flow direction) were negatively related to flow speed in the tunnel, whereas oxygen consumption and frequencies of tail-beat and head movements increased with flow speed. These results show that acoustic accelerometer mainly recorded deviations from sustained swimming in the tunnel, due to spontaneous and explorative swimming at the lowest speeds or intermittent burst and coast actions to cope with water flow. In conclusion, acoustic accelerometers provides a proxy for unsustained swimming activity, but did not contemplate the high-energy cost spent by gilthead seabream on sustained swimming, and therefore, it did not provide a proxy for general activity. Despite this limitation, accelerometers provide valuable insight in swim patterns and therefore may be a good strategy for advancing our understanding of fish swimming behaviour in aquaculture, allowing for rapid detection of changes in species-specific behavioural patterns considered indicators of fish welfare status, and assisting in the refinement of best management practices.
Content may be subject to copyright.
published: 09 July 2021
doi: 10.3389/fanim.2021.679848
Frontiers in Animal Science | 1July 2021 | Volume 2 | Article 679848
Edited by:
Martin Føre,
Norwegian University of Science and
Technology, Norway
Reviewed by:
Miguel Angel Ferrer,
University of Las Palmas de Gran
Canaria, Spain
Ryo Kawabe,
Nagasaki University, Japan
Pablo Arechavala-Lopez
Specialty section:
This article was submitted to
Precision Livestock Farming,
a section of the journal
Frontiers in Animal Science
Received: 12 March 2021
Accepted: 17 June 2021
Published: 09 July 2021
Arechavala-Lopez P, Lankheet MJ,
Díaz-Gil C, Abbink W and Palstra AP
(2021) Swimming Activity of Gilthead
Seabream (Sparus aurata) in
Swim-Tunnels: Accelerations, Oxygen
Consumption and Body Motion.
Front. Anim. Sci. 2:679848.
doi: 10.3389/fanim.2021.679848
Swimming Activity of Gilthead
Seabream (Sparus aurata) in
Swim-Tunnels: Accelerations,
Oxygen Consumption and Body
Pablo Arechavala-Lopez 1
*, Martin J. Lankheet 2, Carlos Díaz-Gil 3, Wout Abbink 4and
Arjan P. Palstra 4
1Fish Ethology and Welfare Group, Centro de Ciencias do Mar (CCMAR), Faro, Portugal, 2Experimental Zoology Group,
Animal Sciences, Wageningen University & Research, Wageningen, Netherlands, 3Xelect Ltd., St. Andrews, United Kingdom,
4Wageningen University & Research Animal Breeding and Genomics, Wageningen Livestock Research, Wageningen,
Acoustic transmitters equipped with accelerometer sensors are considered a useful
tool to study swimming activity, including energetics and movement patterns, of fish
species in aquaculture and in nature. However, given the novelty of this technique, further
laboratory-derived calibrations are needed to assess the characteristics and settings
of accelerometer acoustic transmitters for different species and specific environmental
conditions. In this study, we compared accelerometer acoustic transmitter outputs with
swimming performance and body motion of gilthead seabream (Sparus aurata L.) in
swim-tunnels at different flow speeds, which allowed us to characterize the swimming
activity of this fish species of high aquaculture interest. Tag implantation in the abdominal
cavity had no significant effects on swimming performance and body motion parameters.
Accelerations, cost of transport and variations on head orientation (angle with respect
to flow direction) were negatively related to flow speed in the tunnel, whereas oxygen
consumption and frequencies of tail-beat and head movements increased with flow
speed. These results show that accelerometer acoustic transmitters mainly recorded
deviations from sustained swimming in the tunnel, due to spontaneous and explorative
swimming at the lowest speeds or intermittent burst and coast actions to cope with water
flow. In conclusion, accelerometer acoustic transmitters applied in this study provided a
proxy for unsustained swimming activity, but did not contemplate the high-energy cost
spent by gilthead seabream on sustained swimming, and therefore, it did not provide
a proxy for general activity. Despite this limitation, accelerometer acoustic transmitters
provide valuable insight in swim patterns and therefore may be a good strategy for
advancing our understanding of fish swimming behavior in aquaculture, allowing for rapid
detection of changes in species-specific behavioral patterns considered indicators of fish
welfare status, and assisting in the refinement of best management practices.
Keywords: acoustic telemetry, oxygen consumption, head orientation, tail beats, swimming behavior, aquaculture
Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
Understanding the basic biology of fish species is crucial for
livestock production, and the integration of technological
solutions can help to improve accuracy, precision and
repeatability in farming operations, but also to improve the
decision making in aquaculture management plans. The ongoing
technological advances are rapidly expanding the possibilities of
using biotelemetry sensors to accurately assess fish swimming
activity and movements as a proxy for energy use in fish (Wilson
et al., 2013; Hussey et al., 2015). The accurate measurement
of animal acceleration should be a good proxy for energy
expenditure during activity, given that locomotion occurs when
animals spend energy to contract muscles which leads to body
acceleration (Halsey et al., 2009). A theoretically valid proxy of
energy expenditure is the acceleration of an animal’s mass due
to the movement of its body parts (Halsey et al., 2011). The
classic approach for assessing swimming energetics is to force the
fish to swim in a flow-tunnel respirometer and to measure the
relationship between swimming speed and oxygen uptake (Brett,
1964). Oxygen consumption (MO2) is usually well correlated
with swimming speed but also with fish movements, such as
tail beat frequency (TBF), which has been used as a proxy for
energy use in fish (Lowe et al., 1998). Locomotor performance
and associated metabolic costs are often coupled with life history
traits, which may involve trade-offs related to growth and energy
expenditure (Arnott et al., 2006; Rouleau et al., 2010). Knowledge
of the activity patterns and energetic requirements of marine
fish species is highly relevant not only for conservation and
management strategies (Wikelski and Cooke, 2006), but also
for fish aquaculture management (McKenzie et al., 2020). As a
result, there is a growing demand for continuously monitoring
swimming activity, both in fish farms and in the wild.
Accelerometer sensors can provide fine-scale information
on locomotion and body posture (Shepard et al., 2008), and
therefore, they are being positioned as an innovative tool for
measuring energy expenditure in fishes (Halsey et al., 2011).
Accelerometers may be either archival tags (loggers) that store
data in internal storage mediums that can only be accessed after
the fish (and tag) has been recaptured, or acoustic transmitter tags
that transmit data wirelessly and real-time to the user, collecting
the data by means of an acoustic receiver (Thorstad et al., 2013).
Loggers and transmitters can be used to record the acceleration
of animals both in the field and the laboratory, providing an
overall activity level indicator, which can be used to calculate
activity-specific energy use (Gleiss et al., 2010,Wilson et al.,
2013). Previous studies have explored the utility of accelerometer
loggers providing high resolution acceleration data (Shepard
et al., 2008) and successfully establishing correlations between
MO2and two dimensional (Partial Dynamic Body Acceleration)
or three dimensional (Overall Dynamic Body Acceleration)
acceleration in fishes (Clark et al., 2010; Gleiss et al., 2010;
Wright et al., 2014; Brownscombe et al., 2018). External tri-axial
accelerometer loggers has been also attached to the operculum
of two marine farmed fish to successfully assess the physical
activity by measurements of movement accelerations in x- and
y-axes, while records of operculum beats (z-axis) served as
a measurement of respiratory frequency (Martos-Sitcha et al.,
2019; Ferrer et al., 2020). However, acceleration data loggers
require retrieval of the logger to access the data, limiting their
applicability and battery-life. On the other hand, accelerometer
acoustic transmitters allow for transmission of data which is sent
to hydrophone receivers, rather than storing data that must be
later retrieved and downloaded, extending therefore the study
period and the use of these accelerometers to species and/or
environments where recaptures are difficult (Cooke et al., 2016).
In captive conditions, acoustic transmission also allows real-time
monitoring with cabled receivers (Føre et al., 2018). Therefore,
there is no one-size-fits-all method, and the selection of the
accelerometers to be used must be carefully considered and based
on the objectives of the study to be carried out.
Accelerometer acoustic transmitters have been used to
monitor individual and group swimming activity, behavior and
movement patterns of several farmed fish species (e.g. Føre et al.,
2011; Kolarevic et al., 2016; Gesto et al., 2020; Muñoz et al.,
2020; Palstra et al., 2021), but also to estimate the bioenergetics
in wild fish (e.g. Cooke et al., 2016; Metcalfe et al., 2016).
Without a lab-based calibration of energy expenditure, field-
based tag outputs can still be used to make within-individual
comparisons across times, places, or temperatures, but cannot
be directly used to provide estimates of energy budgets (Cooke
et al., 2016). To date, few studies have used a swim flume to
perform controlled calibrations between accelerometer output
and MO2in fish. For example, positive linear relationships
between accelerations and swimming speed, MO2and TBF
have been reported for sockeye salmon (Oncorhynchus nerka)
(Wilson et al., 2013) and lake trout (Salvelinus namaycush)
(Cruz-Font et al., 2016) in laboratory settings. Zupa et al. (2021)
calibrated tail-bait (2-axis) accelerometer acoustic transmitters
with MO2in rainbow trout (Oncorhynchus mykiss) during
swimming trials. Murchie et al. (2011) tested the utility of
acoustic tri-axial acceleration transmitters in combination with
ethogram and respirometry studies to quantify the activity
patterns and field metabolic rates of free-swimming bonefish
(Albula vulpes). Similarly, Brownscombe et al. (2017) used a
swim tunnel respirometer to calibrate accelerometer acoustic
transmitters implanted in bonefish and estimated their metabolic
rates and energy expenditure in the field. Brodie et al. (2016)
used accelerometer acoustic transmitters in conjunction with
laboratory respirometry trials to estimate active metabolic rates
of kingfish (Seriola lalandi) in the wild. Therefore, in cases
where the output of an activity-related tag has been calibrated
with oxygen consumption rates (and/or swimming speeds)
and locomotion using laboratory experiments, field-based
measurements could be used to better-understand the swimming
activity of fish species, but also to improve the accuracy
of bioenergetics models (Cooke et al., 2016; Metcalfe et al.,
2016). Therefore, the use of accelerometer acoustic transmitters
calibrated with MO2as a proxy of energy expenditure can be
seen as a promising tool for welfare assessment in the aquaculture
industry (Zupa et al., 2021). Nevertheless, more studies are
required to improve methodologies in this field of research,
with issues arising from the complex interpretation of data
acceleration, but also developing species- and life-stage-specific
Frontiers in Animal Science | 2July 2021 | Volume 2 | Article 679848
Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
relationships between acceleration, oxygen consumption and
body motion before acceleration transmitters can be extensively
applied to reliably assess fish swimming activity.
Gilthead seabream (Sparus aurata Linnaeus, 1758) is a
widely distributed species with high interest for both fisheries
and aquaculture, mostly throughout the Mediterranean Basin.
Previous studies used acoustic telemetry transmitters to monitor
fish movements of free-swimming seabream (Abecasis and
Erzini, 2008), farmed individuals in ponds (Bégout and
Lagardère, 1995) and escapees from rearing cages (Arechavala-
Lopez et al., 2012; Šegvi´
c et al., 2018). Lately, there is
a growing interest on using accelerometers as a tool to assess
swimming activity of seabream at farms in order to better-
understand the swimming performance and welfare conditions
in aquaculture (Muñoz et al., 2020; Palstra et al., 2021). However,
to support more in-depth research on free-swimming and reared
seabream using acoustic tags with accelerometer sensors, the
relationships between acceleration, oxygen consumption and
locomotion should be validated for use in this species. In
addition, there is a general concern about the potential effects
of implanted tags on swimming performance and behavior of
tagged fish. Apart from carrying a device internally, tagging
procedures include several operations (i.e. handling, anesthesia
and surgical procedures) that may induce stress that in turn
may lead to physiological and/or behavioral changes in the fish
(e.g. Thorstad et al., 2013). These potential changes must be at
least transient, which means that the fish may be considered
fully recovered once the response patterns return to those
expected from a non-tagged fish. Ideally, tags cannot influence
physiology or behavior in such a way that the tagged fish
differ significantly from non-tagged fish (e.g. Wright et al.,
2019), and the only way to test such effects is experimentally.
Therefore, the objectives of this study were to characterize
the swimming activity of gilthead seabream by assessing the
relationships between transmitted accelerations and swimming
speeds of tagged individuals, but also between accelerometer
outputs, oxygen consumption and body motion parameters
(i.e. head orientation and tail beats). In addition, we assessed
the potential surgical/tagging effects through the comparison
of oxygen consumption, cost of transport and body motion
parameters between tagged and non-tagged gilthead seabream.
Fish and Experimental Settings
Hatchery-reared gilthead seabream were provided by Aquanord
(Gravelines, France) and transported to the Wageningen
University and Research experimental facilities (CARUS,
Wageningen, The Netherlands). There, 50 seabream (about
20 cm in length and 200 g in weight) were held in a 600 L circular
tank supplied with well-aerated water at 20 ±1C from a
recirculating biofilter system, and fed every day with commercial
pellets (2% of body mass). They were allowed to acclimatize
in the quarantine tank for at least 2 weeks before use in the
experiment. Experimental swimming tests were performed in
four Bla˘
zka-type swim-tunnels (see Van Den Thillart et al., 2004
for a detailed description). Air temperature was maintained
at 20C, water temperature values during experiments were
20 ±1C. Tunnels were connected to a 400 L tank filled with
seawater, which was aerated to maintain high oxygen levels.
Water from the tanks was recirculated through each tunnel
using an EHEIM pump (Universal; EHEIM GmbH & Co. KG,
Deizisau, Germany). The water inlet could be closed by a valve
when oxygen measurements were performed (Figure 1). The
flow in the swim-tunnels was set at six different speeds during
the experiment, from the lowest speed (flushing only, propellers
not active) and increasing stepwise by 0.2 ms1per hour up
to 1 ms1(Palstra et al., 2020) while accelerations, oxygen
consumption and locomotion were assessed within each interval.
Acoustic Accelerometer Transmitters and
After a period of acclimatization after transport, acoustic
accelerometer transmitters were implanted to 10 seabream
individuals (see Table 1). Fish collected from the tanks using a
hand net were anesthetized by submersion in an aqueous solution
of Phenoxyethanol (0.25 ml L1). Once deeply anesthetized, the
fish standard length was measured, the fish was weighed, and
then placed with the ventral side up on a surgical table to
implant the transmitters. An incision (1 cm) was made on
the ventral surface, posterior to the pelvic girdle. An acoustic
transmitter equipped with accelerometer sensor (Thelma Biotel
Ltd., Trondheim, Norway; model A-LP7; sampling frequency:
5 Hz; transmission frequency: 71 kHz; weight in air: 1.9 g; weight
in water: 1 g; diameter size: 7.3mm; length: 17 mm; transmission
interval: 20–40 s) was introduced through the incision into the
body cavity above the pelvic girdle. The incision was closed
with one or two independent silk sutures. The fish was regularly
sprayed with water during the surgery (handling time: 2–3 min).
Before each incision, the surgical equipment was rinsed in
70% ethanol and allowed to dry. After tagging, the fish was
released back into a separate quarantine tank for recovery
and re-acclimatization for 5 days before the experiment. A
passive acoustic receiver (Thelma Biotel Ltd.; model TBR 700;
diameter: 75 mm; length: 230 mm) was positioned at the back
end inside each tunnel, to record the accelerometer transmitters
signals during the experiment (Figure 1). A mesh structure
was placed between the receiver and the swimming area of
the tunnel to avoid possible effects on the water flow and
on swimming behavior of the fish during the experiment.
The accelerometers provided root mean square values of the
three acceleration axes (ARMS, in mcots2), averaged over all
samples in the sampling window, and were transformed into real
accelerations (in ms2).
Respirometry and Swimming Performance
The swim-tunnel system included a bypass with an oxygen probe
in a four-channel respirometry system (DAQ-PAC-G4; Loligo
Systems Aps, Tjele, Denmark) to measure total oxygen content
of the water in percentage (Figure 1), which drops due to oxygen
consumption of the fish (1O2%). A low rate of background
(bacterial) respiration was always detected and subsequently
subtracted from fish oxygen consumption (Palstra et al., 2015).
From the decline of the O2concentration, the O2consumption
Frontiers in Animal Science | 3July 2021 | Volume 2 | Article 679848
Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
FIGURE 1 | Schematic drawing of a Blažka-type swim-tunnel (modified and adapted from Van Den Thillart et al., 2004) with the experimental setting for
accelerometry, respirometry and locomotion assessment of Sparus aurata at different swimming speeds in the present work.
TABLE 1 | Mean values (±SE) of body length (BL), body weight (BW), optimal swimming speed (Uopt), critical swimming speed (Ucrit ), and minimum oxygen cost of
transport (COTmin) for tagged and control seabream in the swim-tunnels, and significance of differences between tagged (N=10) and non-tagged fish (N=10) for all
parameters (ANOVA, significance level: p<0.05, indicated with an asterisk:*).
All Tagged Control pTest
BL (cm) 20.1 ±0.2 19.7 ±0.3 20.4 ±0.2 0.032* a
BW (g) 205.4 ±22.1 198.7 ±16.7 212.2 ±25.6 0.179 a
Uopt (m s1) 0.65 ±0.01 0.65 ±0.01 0.64 ±0.02 0.475 a
Uopt (BL s1) 3.26 ±0.07 3.33 ±0.09 3.13 ±0.08 0.181 a
Ucrit (m s1) 0.96 ±0.01 0.97 ±0.01 0.95 ±0.01 0.196 b
Ucrit (BL s1) 4.78 ±0.06 4.92 ±0.07 4.64 ±0.08 0.016* a
COTmin (mg kg1km1) 179.2 ±9.7 189.6 ±11.1 160.4 ±16.3 0.155 a
Test: a, ANOVA; b, Kruskal-Wallis.
rate (MO2; in mgO2kg1h1) and cost of transport (COT; in
mg kg1km1) were calculated following the equations:
MO2= 1O2%DOmax L
where DOmax is the maximum amount of oxygen dissolved
in the water (9.47 mg O2L1at a temperature of 20C in
seawater for seabream); Lis the volume of the swim-tunnel
(127 L); tthe time in minutes, and 1dis the covered distance
in m (estimated from selected flow speed and exposure time
during sampling). Additionally, three parameters were used to
characterize swimming endurance (Farrell, 2008; Palstra et al.,
2008): (i) Critical speed (Ucrit): the critical swimming speed
(m s1and BL s1), calculated according to Brett (1964); (ii)
Optimal speed (Uopt): the optimum swimming speed (m s1
and BL s1) where the cost of transport (COT, mgO2kg1
km1) reaches a minimum (Tucker, 1970); (iii) minimum cost of
transport (COTmin ): the cost of transport (mg O2 kg1km1) at
Uopt. The Uopt was determined by plotting a polynomial trend
line through COT values vs. swimming speeds per seabream
individual. The point on this trend line with the lowest COT
(COTmin ) was calculated by equalling the first derivative to zero
(Palstra et al., 2008).
Frontiers in Animal Science | 4July 2021 | Volume 2 | Article 679848
Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
Body Motion: Tail Beats and Head
Seabream locomotion was filmed with a Basler 2040-90um NIR
USB3 camera at a frame rate of 25 frames per second and 15 ms
exposure time. Pixels were binned 2×2 to improve sensitivity
by a factor of 4. The camera was positioned one meter below
the center of the tunnel (Figure 1). Final images were 1024×512
pixels at a resolution of 14.25 pixels per cm. A translucent back
projection screen was placed on top of the tunnel to disperse the
room lights into a homogeneous white background. The camera
view covered the full length and width of the tunnel, except
for the most upstream 5 cm and downstream 10 cm. Custom
software developed in Python, including the OpenCV image
analysis library was used to detect and save the fish contour
(Figure 2) in real-time. To detect the fish we used a median (3
pix) and Gaussian blur (5 pix) filter to reduce noise, followed
by a histogram normalization to improve the contrast in the
images, and by a luminance threshold that selected dark images
relative to a light background. The fish was selected from detected
objects (using the find_contours routine) based on size (surface
area) and length-width ratio of an ellipse fitted to the contour.
We used a standard Kalman filter in OpenCV with position,
speed and acceleration estimates, to obtain smoothed estimates
of fish tracks, quantified by the center of mass of the contour.
Timing information, xand ylocations and full body contours
were saved to disk for off-line analysis. The midline of each
fish was analyzed from the saved contours based on a distance
transform (quantifying for each pixel the nearest distance to
the contour). To this end, the head location and orientation
(HO) were determined by fitting a line to all points with a
distance larger than 80% of half the width of the body, yielding
a strip of ‘midline’ points in the anterior region of the fish
(Figure 2, black area). The snout (Figure 2, yellow dot) of the
fish was found by detecting the first point outside the contour,
on a line fitted to the midline points. To construct the full axis
of the fish, we tracked the ridge of maxima of the distance
transform, starting at the snout, in steps of 0.7 cm. To track
the maxima in the distance transform we iteratively find the
maximum on a circle (0.7 cm radius) around the previous point,
followed by clearing the values within the circle (thus preventing
direction reversals). Tracking stopped when the tip of the tail
was reached. The resulting axis was slightly smoothed using a
univariate spline (k=3, s=5) for xand ydata separately,
to minimize the effects of irregularities in the contour on the
distance transforms. To quantify tailbeat parameters we selected
a point in the tail at 14.0 cm distance from the snout, and we
determined the lateral excursion relative to the midline through
the head (Figure 2, projection of the red dot onto the gray line).
Tailbeat frequency (TBF) and amplitude (TBA) were obtained
by performing a spectral analysis on the tail excursion as a
function of time (Figure 2C). Spectrograms were calculated with
a temporal window size of 1.28 s (32 frames), shifted frame by
frame. The resolution in the frequency domain was increased by
padding the signal with zero values to a length 8 times the width
of the original signal. Frequency and amplitude (Figures 2D,E)
were determined based on the maximum in the spectrogram
at each frame. A similar calculation was performed on head
orientation to obtain frequency (HOF) and amplitude (HOA) of
head orientation changes.
Experimental Protocol and Data Analyses
Each experimental day, four individuals (tagged or non-tagged
control fish) were scooped out of the tank, anesthetized,
measured (length and weight) and placed individually in
the swim-tunnels, where they recovered within 10 min. After
acclimatization to the swim-tunnel overnight, the next day each
fish was exposed to six different swimming speeds (in the range
0–1 ms1with increments of 0.2 ms1). Each condition was
maintained for 1 h during which the oxygen decline in the tunnel
was measured over the first 0.5 h for all individuals (N=20).
After oxygen measurements, the tunnel was flushed for another
0.5 h while swimming speed was maintained. Accelerations were
recorded during the whole experimental procedure on tagged
fish (N=10). Swimming behavior (i.e. body movements) was
recorded during 0.25 h (15 min) within the first 0.5 h of interval
for a total of 10 fish (6 tagged and 4 control). Regression analyses
were performed to assess the relationships between swimming
speed and dependent variables (i.e. accelerations, MO2, COT,
HO, HOA, HOF and TBA, TBF) as well as paired relationships
between studied parameters. Distribution of recorded seabream
accelerations, TBF, HO and HOF were visually assessed by
plotting percentage of recorded observations (counts) at different
swimming speed (see Supplementary Figure 1).
Analyses of variance (ANOVA) were applied to assess
for differences between tagged and non-tagged fish regarding
body size (BL), body weight (BW) and estimated swimming
performance parameters (i.e. Uopt,Ucrit ,COTmin ). Homogeneity
of variances were tested in all cases using Bartlett’s test as
well as normality of the residuals. In the case of not following
the assumptions of the model, non-parametric Kruskal Wallis
tests were performed to assess differences between tagged and
control fish (Table 1). Similarly, difference between tagged and
non-tagged fish on respirometry and locomotion parameters
were tested using a generalized linear mixed model (GLMM),
with estimated parameters as the response variable, treatment
(tagged/non-tagged) and swimming speed as fixed effects, and
accounting for individual variability as random effect in all the
models. For accelerations a Gamma-distributed response variable
was applied, given that the values are positive defined and
positive-skewed, and it has been successfully used in previous
seabream accelerations modeling (Díaz-Gil et al., 2017). In
addition, the paired relationships between estimated parameters
were also tested using a GLMM, with accelerations or oxygen
consumption as the response variable, other dependent variables
and swimming speed as fixed effect and individual as a random
factor. R software (R Core Team, 2020) was used for statistical
analyses, and the R packages lme4 (Bates et al., 2015) and
lmerTest (Kuznetsova et al., 2017) were employed to fit models.
Results were considered significant at p<0.05. All values are
reported as means ±SE.
Ethical Approval
Animal manipulations for this experiment were carried out
strictly by trained and competent personnel, in accordance
Frontiers in Animal Science | 5July 2021 | Volume 2 | Article 679848
Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
FIGURE 2 | Schematic representation of the locomotion parameters
estimated in the present work. (A) bottom view of a fish silhouette with
analyzed data points. Yellow: tip of the snout. Blue: reference point in the head
region, determined as the center of mass for all points that are located further
from the contour than 80% of the width of the fish (the area marked in black).
Red: a reference point on the axis of the fish in the tail region, defined at a fixed
distance from the snout, along the central axis. Green dots indicate the central
axis. (B) Tail excursion as a function of time, measured as the distance of the
tail point (red mark) to midline through the head. (C) Frequency analysis of the
tail excursion. The spectrogram shows the amplitude of all frequencies as a
function of time. (D) The frequency of tail beats determined as the frequency at
which the spectrogram peaks. (E) The amplitude of tail excursions determined
at the tail beat frequency in (D). Marginal histograms in (D) and (E) show the
distribution of frequencies and amplitudes, from which mean values and
standard deviations were calculated. Similar frequency analyses were
performed for other parameters (see methods for further details).
with the European Directive (2010/63/UE), and approved by
the Dutch Central Committee for Animal Experimentation
(CCD nr. AVD401002017817) and by the Animal Experimental
Committee of Wageningen University (IvD experiment
nr. 2016.D-0039.004).
Acoustically Transmitted Accelerations
One accelerometer showed technical problems (i.e. no data
transmission) and therefore it was not considered in the
accelerometry study. For the rest of tagged individuals (n=9),
there was a significant negative relationship of accelerations with
swimming speed (exponential fit: R2=0.61, p<0.001). Gilthead
seabream exhibited maximum mean accelerations at the lowest
speed (1.26 ±0.21 ms2), and minimum mean accelerations
at the highest speed (0.51 ±0.17 ms2) (Figure 3A). Similarly,
the variability of accelerations recorded by tagged seabream
decreased as swimming speed increased (Figure 3B) (see more
detailed representation in Supplementary Figure 1).
There was a significant positive relationship between MO2and
swimming speed (polynomial fit: R2=0.50; GLMM, p<0.001)
for all individuals (n=20), where MO2did not significantly
differ between tagged fish (n=10) and non-tagged (n=10)
seabream (GLMM, p=0.779) (Figure 4A). COT was negatively
related to swimming speed (polynomial fit: R2=0.50; GLMM,
p<0.001) for all individuals, but no significant differences on
COT were observed between tagged and non-tagged seabream
(GLMM, p=0.757) (Figure 4B). Most of the seabream (70%; 9
tagged and 5 non-tagged fish) showed a clear Uopt of average 0.65
±0.01 ms1(3.26 ±0.07 BL s1) and a mean COTmin of 179.2
±9.7 mg kg1km1(Table 1). 50% of seabream individuals (4
tagged and 6 non-tagged fish) fatigued before the end of the
experiment, and for the rest of seabream an Ucrit of 1 ms1
was considered. As a result, a mean Ucrit of 0.96 ±0.01 m
s1(4.78 ±0.06 BL s1) was estimated for all fish although
this is an underestimation as half of the fish did not fatigue.
Although there were significant differences between tagged and
non-tagged fish regarding body length (BL; ANOVA, p=0.032)
and Ucrit (BL s-1; Kruskal-Wallis, p=0.016), no differences
between both fish groups were detected for body weight (BW)
and the rest of estimated swimming performance parameters
(ANOVA, p>0.05) (Table 1).
Body Motion: Head Orientation and Tail
Recorded seabream (n=10) exhibited the highest mean values of
HO at the lowest speed, where individuals were able to swim in all
directions (spontaneous movements), while at higher swimming
speeds the fish only swam against the flow and HO values were
minimal (Figure 5A). This resulted in significant differences
on HO regarding swimming speed (GLMM, p<0.001). In
addition, there were no differences between tagged and non-
tagged fish (GLMM, p=0.601). HO variability also decreased
as the swimming speed increased, remaining closer to an angle
of 1.57 rad above the Uopt, which corresponds to the straight
“upstream” position (Figure 5B) (See Supplementary Figure 1
for further details). HOA varied with swimming speeds (mean
HOA range: 0.10–0.40 rad) (Figure 6A), decreasing significantly
with swimming speed increase (exponential fit: R2=0.49;
GLMM, p=0.038), but no differences were observed between
tagged and non-tagged seabream (GLMM, p=0.9723). There
was a significant positive relationship between HOF and
swimming speed (linear fit: R2=0.68; GLMM, p<0.001),
although higher variation was observed at the highest speed
(Figure 6B) (See Supplementary Figure 1 for further details).
Minimum HOF mean values can be observed at the lowest
speed (mean: 1.67 ±0.23 cycles s1) and maximum values
at the highest speed (mean: 2.87 ±0.43 cycles s1). No
significant differences were observed on HOF between tagged
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Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
FIGURE 3 | Accelerations of tagged Sparus aurata at different water speeds. (A) Box-plot of mean values; (B) Inverse Gaussian lines fitted on proportional data
FIGURE 4 | (A) Best-fitted regression of: (A) oxygen consumption rates (MO2); and (B) oxygen cost of transport (COT), of tagged (orange; N=9) and control (green;
N=10) Sparus aurata in swim-tunnels at different swimming speeds.
and non-tagged seabream (GLMM, p=0.1403). There were
no significant differences on TBA regarding swimming speed
(exponential fit: R2=0.43; GLMM, p=0.266) (Figure 6C),
and no tagging effects were detected (GLMM, p=0.983). A
significant positive relationship between TBF and swimming
speed was observed (linear fit: R2=0.87; GLMM, p <0.001)
(Figure 6D) (See Supplementary Figure 1 for further details).
Seabream exhibited minimum mean TBF values at the lowest
speed (mean: 1.68 ±0.07 cycles s1) and the maximum TBF
values at the highest speed (mean: 4.13 ±0.34 cycles s1).
There were no significant differences on TBF between tagged and
non-tagged seabream (GLMM, p=0.822).
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Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
FIGURE 5 | Head orientation of Sparus aurata in swim-tunnels at different swimming speeds. (A) Box-plot of mean values; (B) Gaussian lines fitted on proportional
data distribution. Dotted line: HO =1.57 rad (straight upstream swimming position).
Acceleration and Oxygen Consumption vs
Body Motion
Gilthead seabream accelerations showed a significantly negative
relationship with MO2(exponential fit: R2=0.50; GLMM, p
<0.001) (Figure 7A) and a significantly positive relationship
with COT (exponential fit: R2=0.50; GLMM, p=0.004)
(Figure 7B). Variations of head orientation (SD-OH) were
significantly and positively correlated to accelerations (linear
fit: R2=0.68; GLMM, p<0.001) (Figure 8A) and significantly
and negatively related to MO2(exponential fit: R2=0.44;
GLMM, p<0.001) (Figure 8B). HOF showed a significantly
negative relationship with accelerations (exponential fit: R2=
0.49; GLMM, p<0.001) (Figure 8C), while its relationship with
MO2was significantly positive (lineal fit: R2=0.45; GLMM,
p<0.001) (Figure 8D). Similarly, TBF was significantly and
negatively related to accelerations (exponential fit: R2=0.51;
GLMM, p<0.001) (Figure 8E), while it showed a positive
relationship with MO2(linear fit: R2=0.56; GLMM, p=0.081)
(Figure 8F).
This study successfully characterized the swimming activity of
gilthead seabream through a multidisciplinary approach in swim-
tunnels, which also allowed testing the effects of tag implantation
at optimal and critical swimming speed. Tag implantation did not
have impact on oxygen consumption, swimming performance
and locomotion on seabream, since no differences were observed
between non-tagged and tagged individuals, and results agree
with values previously reported in wild and farmed seabream
(Basaran et al., 2007; Steinhausen et al., 2010; Martos-Sitcha
et al., 2018; Palstra et al., 2020). Carrying an internal device
and the associated tagging procedures may induce physiological
and behavioral stress responses on fish, in such a way that
the tagged fish differ significantly in performance from the
non-tagged fish (e.g. Cooke et al., 2011; Montoya et al., 2012;
Thorstad et al., 2013; Wright et al., 2019; Alfonso et al.,
2020). Previous studies on gilthead seabream also reported a
lack of mid-term effects of tag implantation on rhythmicity of
feeding and locomotor activity, stress physiology and growth
performance (Montoya et al., 2012; Alfonso et al., 2020),
supporting that tagged seabream can be considered as a
representative portion of the study population. However, some
additional research might be warranted to investigate potential
physiological-induced long-term effects on seabream (Montoya
et al., 2012), to ensure that the data generated are relevant
to non-tagged conspecifics and the surgical procedure does
not impair the health and welfare status of the tagged fish
(Cooke et al., 2011).
Gilthead seabream performed spontaneous swimming at
the lowest flow speed, whereas during forced swimming they
adopted a steady-state swimming where the fish gradually
increased speed as the flow speed increased to maintain its
position within the tunnel, but also decreased burst-coast actions
as they approached exhaustion. Acoustic accelerometer tags
transmitted a summarized acceleration value of a certain period
of time (20–40 s) that provides an index of activity, measuring
spontaneous movements and overall displacements due to burst-
actions in the tunnel. Therefore, this study pointed out that
applied accelerometer acoustic transmitters do not properly
measure sustained swimming, which entails high expenditure of
energy, but successfully reveal unsustained swimming activity
of seabream in swim-tunnels. Gilthead seabream exhibited
spontaneous swimming patterns with highly frequent changes in
directions in the absence of water flow, reflected on the highest
Frontiers in Animal Science | 8July 2021 | Volume 2 | Article 679848
Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
FIGURE 6 | Best-fitted regression of: (A) head orientation amplitude (HOA); (B) head orientation frequency (HOF); (C) tail beat amplitude (TBA); and (D) tail beat
frequency (TBF), of tagged (orange) and control (green) Sparus aurata in swim-tunnels at different swimming speeds.
transmitted accelerations and lowest oxygen consumption.
In addition, seabream exhibited burst-and-coast swimming
behavior in the swim-tunnels whereby fish would burst to the
front of the tunnel and fall back to the end of the swim-tunnel.
Burst actions occurred more often at lower swimming speeds,
which were represented by higher transmitted accelerations and
decreased in distance as the swimming speed increased, as
a result of reduced energy and locomotive efficiencies which
requires higher locomotion and oxygen consumption. Indeed,
when seabream were exposed to swim at the highest speed,
burst actions were practically inefficient showing a lack of
forward displacements, mostly remaining at the end of the tunnel
performing a highly forced sustained swimming, approaching to
exhaustion (Ucrit).
Continuous swimming is fuelled aerobically and can be
sustained by a fish for a relative long time, and locomotion is
mainly propelled by the red musculature which is powered by
oxidative phosphorylation; whereas the white musculature will
be used when a fish is exposed to strong or variable currents (Gui
et al., 2014). On the contrary, transient swimming (burst actions)
is performed anaerobically (utilization of phosphocreatine, ATP
and glycogen) and propelled by the white musculature. During
burst-and-coast swimming, alternating bouts of active swimming
movements (burst phase) with passive coasts or glides (coast
phase) conserves on the energy by reducing the amount of
muscular effort over a prolonged time period (Webb and
Weihs, 1983). Svendsen et al. (2015) demonstrated the energetic
importance of anaerobic metabolism during steady swimming
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Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
FIGURE 7 | Best-fitted regression accelerations with: (A) oxygen consumption rate (MO2), and (B) cost of transport (COT), of Sparus aurata at different swimming
on gilthead seabream, providing that anaerobic swimming costs
increase linearly with the number of bursts in this species.
Gilthead seabream is a body-caudal fin swimmer, presenting
a subcarangiform swimming mode, where the whole body
participates in large amplitude undulations, only limited at the
anterior side, and increasing toward the posterior half of the
body (Sfakiotakis et al., 1999; Lauder, 2015). The movements of
the entire body contribute to the overall swimming performance
(Akanyeti et al., 2017) but are particularly significant during
unsteady swimming actions characterized by quick turns, high
accelerations or burst actions (Sfakiotakis et al., 1999). Seabream
control speed by primarily modulating undulation frequency,
reflected in TBF and HOF, while maintaining a relatively low and
constant undulation amplitude (TBA and HOA). However, when
a steadily swimming fish accelerates forward, the speed is gained
not only by further increase of the TBF and HOF, but also by
bending the body to accelerate a large amount of fluid (Lauder,
2015) increasing the angle of the head (HO) with respect to the
rest of the body (Akanyeti et al., 2017).
Accelerometer acoustic transmitters used in this study
provided averaged measures over all samples (sampling
frequency: 5 Hz) in the sampling window (20–40 s)
corresponding to the mean absolute acceleration values
that mostly represent changes in swimming speed or direction.
Therefore, total swimming activity is the sum of steady
swimming at the flow speed and additional activity picked up by
the accelerometers (unsustained swimming due to spontaneous
or burst-coast actions). Oscillations due to vigorous swimming
movements, movement of restrictions and fatigue stress were
observed at highest swimming speeds. However, these increases
of yaw movements and body bendings may not have been
properly recorded by the accelerometer due to its location and
orientation in the fish gut cavity and next to the mass center.
Therefore, the increase of swimming speed revealed a sustained
swimming behavior of seabream, which involved an increase of
TBF and HOF, and a reduction of HO and burst actions together
with lower acceleration outputs. Cruz-Font et al. (2016) showed
that TBF transmitters implanted at the back of the body cavity of
lake trout were more sensitive to swimming movements, showing
higher values of acceleration at each speed tested. Although high
relationships between acceleration and speed for tags implanted
centrally in the body cavity were also reported, implanting a
tag at the back of the cavity may better describe the swimming
energetics, but it highly depends on the swimming mode of the
fish. Gesto et al. (2020) implanted acoustic accelerometer tags
with 12 s sampling period in farmed rainbow trout (O. mykiss)
that measured only on two spatial axes (X,Z; “tailbeat” mode),
ignoring the backward/forward acceleration (Yaxis). In this case,
authors only accounted for the undulation movements associated
to the movements of the tail, which are known to be directly
associated to fish’ swimming speed (Wilson et al., 2013; Cruz-
Font et al., 2016). Similarly, Zupa et al. (2021) calibrated tail-beat
(2-axis) accelerometer acoustic tags with MO2on rainbow trout
during swimming trials with a high sampling frequency of
10 Hz. Wilson et al. (2013) calibrated acceleration transmitters
with energy expenditure on sockeye salmon (O. nerka) across
a range of swimming speeds and water temperatures, showing
positive linear relationships of accelerations with swimming
speed, MO2and TBF. In this case, sockeye salmon maintained
steady-state swimming during the swim trials, performing
irregular tail-beats at lower swimming speeds and burst
actions only at high speeds, a swimming behavior completely
Frontiers in Animal Science | 10 July 2021 | Volume 2 | Article 679848
Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
FIGURE 8 | Best-fitted regression of Sparus aurata accelerations and oxygen consumption (MO2) with body motion parameters at different swimming speeds. (A)
variations of head orientation (SD-HO) and accelerations; (B) variations of head orientation (SD-HO) and oxygen consumption; (C) head orientation frequency (HOF)
and accelerations; (D) head orientation frequency (HOF) and oxygen consumption; (E) tail beat frequency (TBF) and accelerations; (F) tail beat frequency (TBF) and
oxygen consumption.
Frontiers in Animal Science | 11 July 2021 | Volume 2 | Article 679848
Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
opposite to that observed on seabream. Wilson et al. (2013)
used accelerometer acoustic transmitters operating at higher
sampling frequency (10 Hz) and lower sampling period (10 s)
than those used in the present study. Murchie et al. (2011)
tested acoustic tri-axial acceleration transmitters (5Hz sampling
frequency, 25 s sampling period) in combination with ethogram
and respirometry studies on bonefish. Author reported that their
accelerometers reflected low swimming speeds and intermittent
swimming behaviors, and given that most bursting activity lasts
on the order of seconds, it is not surprising that most of the
acceleration data collected consisted of low values. Similarly,
other studies also agreed that burst behaviors might exceed
the capacity of the tag to report an increase in the acceleration
at high swimming speeds (Murchie et al., 2011; Wilson et al.,
2013; Cruz-Font et al., 2016). Brownscombe et al. (2017) used
similar accelerometers (5Hz sampling frequency, 35 s sampling
period) on bonefish and showed positive relationships between
fish accelerations and oxygen consumption with swimming
speed in the swim-tunnel, but mentioned the limitation of
swim-tunnels of generating linear swimming only, and not more
complex and energetically-costly maneuvers which allow better
estimations of fish energy expenditure. Brodie et al. (2016) also
showed a positive relationship metabolic rate, swimming speed
and activity for kingfish using accelerometers (5 Hz sampling
frequency, 25 s sampling period) in swim-tunnels which were
used to estimate active metabolic rate in the wild. Comparing
with previous studies, our results suggest that body movements
and locomotion parameters can be used to accurately estimate
metabolic rates on gilthead seabream. However, a better selection
of appropriate accelerometers, position of the recording device,
and probably shorter recording period (higher frequency), must
be performed and validated in future studies (Shepard et al.,
2008; Broell et al., 2013; Brownscombe et al., 2018).
Adequate accelerometer acoustic transmitters setting will
ensure the monitoring of sustained aerobic swimming activity of
fish, a necessary element for estimating the energy consumption
of fish, highly relevant for fish farmers’ management. This
study reveals that selected accelerometer acoustic transmitters
successfully measured unsustained swimming activity of
seabream, namely spontaneous and burst-coast actions.
Accelerometer acoustic transmitters could represent a useful tool
in future fish farm management, allowing for rapid detection
of changes in species-specific behavioral patterns which are
considered indicators of fish welfare status in aquaculture
(Martins et al., 2012), and assisting in the refinement of best
management practices. In fact, acoustic telemetry is being
recently transferred to the aquaculture sector improving the
farmer’s ability to monitor, control and document biological
processes in fish farms (Føre et al., 2018). For example, Føre
et al. (2011) reported the successful implementation of depth-
and acceleration-based tags to observed changes in swimming,
activity and feeding behavior on farmed Atlantic salmon (Salmo
salar). Similarly, Kolarevic et al. (2016) used accelerometer
acoustic transmitters on farmed Atlantic salmon for real
time monitoring of swimming activity within recirculating
aquaculture production systems (RAS). Regarding gilthead
seabream, recent studies reported the successful application
of acoustic transmitters equipped with accelerometer sensor
in experimental sea cages to monitor daily swimming activity,
revealing higher seabream accelerations during feeding time
and morning and evening periods (Muñoz et al., 2020; Palstra
et al., 2021). Our results are consistent with these results, since
the greatest fish activity may reflect less sustained swimming
behavior (lack of shoaling behavior) and therefore more
individualism (e.g. feeding competition, aggressiveness, resting).
Nevertheless, there are some limitations when using acoustically
transmitted accelerations, and our results illustrate problems
associated with how accelerations and energy budgets estimates
can be compromised and misinterpreted, and must be handled
with caution. Besides the necessity to upscale telemetry studies in
aquaculture and free-swimming fish, further laboratory-derived
calibrations are needed in order to find the best characteristics
and settings of transmitters (sampling period, battery life,
etc.) for each species and specific environmental conditions.
It is therefore important to validate acceleration outputs with
physiological and behavioral parameters for each species of
interest, in order to ensure research or applied questions are
addressed effectively and appropriately answered.
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
The animal study was reviewed and approved by Dutch
Central Committee for Animal Experimentation (CCD
nr. AVD401002017817).
PA-L and AP: conceptualization and funding acquisition. PA-L,
AP, and ML: methodology. PA-L, CD-G, and ML: formal
analysis and data curation. WA, AP, and ML: resources.
PA-L: writing—original draft. PA-L, CD-G, ML, WA, and AP:
writing—review and editing. WA: project administration. All
authors contributed to the article and approved the submitted
This work is based upon work through a Short Term Scientific
Mission from the European Aquatic Animal Tracking Network
COST Action (ECOST-STSM-CA18102-020320-115323)
supported by COST (European Cooperation in Science and
Technology). This study is also part of a Trans National
Access project ACTIVEBREAM (AE160009) that has received
funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement no. 652831
(AQUAEXCEL2020). This output reflects only the author’s
view and the European Union cannot be held responsible
for any use that may be made of the information contained
Frontiers in Animal Science | 12 July 2021 | Volume 2 | Article 679848
Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
therein. This study also received Portuguese national funds
from FCT—Foundation for Science and Technology through
project UIDB/04326/2020.
The authors thank T. Šegvi´
c (Croatian Institute of
Oceanography and Fisheries) for sharing part of the telemetry
equipment, G. van den Thillart for the use of the swim-tunnels
and the staff of CARUS (research facility of the Department of
Animal Sciences, Wageningen University & Research) for their
assistance, excellent care of the fish and experimental facilities.
The Supplementary Material for this article can be found
online at:
Abecasis, D., and Erzini, K. (2008). Site fidelity and movements of gilthead sea
bream (Sparus aurata) in a coastal lagoon (Ria Formosa, Portugal). Estuar.
Coast. Shelf. Sci. 79, 758–763. doi: 10.1016/j.ecss.2008.06.019
Akanyeti, O., Putney, J., Yanagitsuru, Y. R., Lauder, G. V., Stewart, W. J.,
and Liao, J. C. (2017). Accelerating fishes increase propulsive efficiency
by modulating vortex ring geometry. Proc. Nat. Acad. Sci.U.S.A. 114,
13828–13833. doi: 10.1073/pnas.1705968115
Alfonso, S., Zupa, W., Manfrin, A., Fiocchi, E., Dioguardi, M., Dara, M., et al.
(2020). Surgical implantation of electronic tags does not induce medium-term
effect: insights from growth and stress physiological profile in two marine fish
species. Anim. Biotel. 8, 1–6. doi: 10.1186/s40317-020-00208-w
Arechavala-Lopez, P., Uglem, I., Fernandez-Jover, D., Bayle-Sempere, J. T., and
Sanchez-Jerez, P. (2012). Post-escape dispersion of farmed seabream (Sparus
aurata L.) and recaptures by local fisheries in the Western Mediterranean Sea.
Fish. Res. 121, 126–135. doi: 10.1016/j.fishres.2012.02.003
Arnott, S. A., Chiba, S., and Conover, D. O. (2006). Evolution of intrinsic
growth rate: metabolic costs drive trade-offs between growth and
swimming performance in Menidia menidia.Evolution 60, 1269–1278.
doi: 10.1111/j.0014-3820.2006.tb01204.x
Basaran, F., Ozbilgin, H., and Ozbilgin, Y. D. (2007). Comparison of the swimming
performance of farmed and wild gilthead sea bream, Sparus aurata. Aquacult.
Res. 38, 452–456. doi: 10.1111/j.1365-2109.2007.01670.x
Bates, D., Maechler, M., Bolker, B., and Walker, S. (2015). Fitting linear mixed-
effects models using lme4. J. Stat. Softw. 67, 1–48. doi: 10.18637/jss.v067.i01
Bégout, M. L., and Lagardère, J. P. (1995). “An acoustic telemetry study of seabream
(Sparus aurata L.): first results on activity rhythm, effects of environmental
variables and space utilization,” in Space Partition within Aquatic Ecosystems,
vol 104, Developments in Hydrobiology, eds G. Balvay (Dordrecht, Springer).
Brett, J. R. (1964). The respiratory metabolism and swimming performance
of young sockeye salmon. J. Fish. Res. Bd. Can. 21, 1183–1226.
doi: 10.1139/f64-103
Brodie, S., Taylor, M. D., Smith, J. A., Suthers, I. M., Gray, C. A., and Payne, N. L.
(2016). Improving consumption rate estimates by incorporating wild activity
into a bioenergetics model. Ecol. Evol. 6, 2262–2274. doi: 10.1002/ece3.2027
Broell, F., Noda, T., Wright, S., Domenici, P., Steffensen, J. F., Auclair, J.
P., et al. (2013). Accelerometer tags: detecting and identifying activities in
fish and the effect of sampling frequency. J. Exp. Biol. 216, 1255–1264.
doi: 10.1242/jeb.088336
Brownscombe, J. W., Cooke, S. J., and Danylchuk, A. J. (2017). Spatiotemporal
drivers of energy expenditure in a coastal marine fish. Oecology 183, 689–699.
doi: 10.1007/s00442-016-3800-5
Brownscombe, J. W., Lennox, R. J., Danylchuk, A. J., and Cooke, S.
J. (2018). Estimating fish swimming metrics and metabolic rates with
accelerometers: the influence of sampling frequency. J. Fish Biol. 93, 207–214.
doi: 10.1111/jfb.13652
Clark, T. D., Sandblom, E., Hinch, S. G., Patterson, D. A., Frappell, P.
B., and Farrell, A. P. (2010). Simultaneous biologging of heart rate and
acceleration, and their relationships with energy expenditure in free-swimming
sockeye salmon (Oncorhynchus nerka). J. Comp. Physiol. B 180, 673–684.
doi: 10.1007/s00360-009-0442-5
Cooke, S. J., Brownscombe, J. W., Raby, G. D., Broell, F., Hinch, S. G., Clark, T.
D., et al. (2016). Remote bioenergetics measurements in wild fish: opportunities
and challenges. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 202, 23–37.
doi: 10.1016/j.cbpa.2016.03.022
Cooke, S. J., Woodley, C. M., Eppard, M. B., Brown, R. S., and Nielsen, J. L. (2011).
Advancing the surgical implantation of electronic tags in fish: a gap analysis
and research agenda based on a review of trends in intracoelomic tagging effects
studies. Rev. Fish Biol. Fish. 21, 127–151. doi: 10.1007/s11160-010-9193-3
Cruz-Font, L., Shuter, B. J., and Blanchfield, P. J. (2016). Energetic costs
of activity in wild Lake Trout: a calibration study using acceleration
transmitters and positional telemetry. Can. J. Fish. Aquat. Sci. 73, 1237–1250.
doi: 10.1139/cjfas-2015-0323
Díaz-Gil, C., Cotgrove, L., Smee, S. L., Simón-Otegui, D., Hinz, H., Grau,
A., et al. (2017). Anthropogenic chemical cues can alter the swimming
behaviour of juvenile stages of a temperate fish. Mar. Environ. Res. 125, 34–41.
doi: 10.1016/j.marenvres.2016.11.009
Farrell, A. P. (2008). Comparisons of swimming performance in rainbow trout
using constant acceleration and critical swimming speed tests. J. Fish. Biol. 72,
693–710. doi: 10.1111/j.1095-8649.2007.01759.x
Ferrer, M. A., Calduch-Giner, J. A., Díaz, M., Sosa, J., Rosell-Moll, E., Abril, J. S.,
et al. (2020). From operculum and body tail movements to different coupling
of physical activity and respiratory frequency in farmed gilthead sea bream and
European sea bass. Insights on aquaculture biosensing. Comput. Electron. Agric.
175:105531. doi: 10.1016/j.compag.2020.105531
Føre, M., Alfredsen, J. A., and Gronningsater, A. (2011). Development of two
telemetry-based systems for monitoring the feeding behaviour of Atlantic
salmon (Salmo salar L.) in aquaculture sea-cages. Comput. Electron. Agric. 76,
240–251. doi: 10.1016/j.compag.2011.02.003
Føre, M., Frank, K., Norton, T., Svendsen, E., Alfredsen, J. A., Dempster,
T., et al. (2018). Precision fish farming: A new framework to
improve production in aquaculture. Biosyst. Eng. 173, 176–193.
doi: 10.1016/j.biosystemseng.2017.10.014
Gesto, M., Zupa, W., Alfonso, S., Spedicato, M. T., Lembo, G., and Carbonara, P.
(2020). Using acoustic telemetry to assess behavioralresponses to acute hypoxia
and ammonia exposure in farmed rainbow trout of different competitive ability.
Appl. Anim. Behav. Sci. 230:105084. doi: 10.1016/j.applanim.2020.105084
Gleiss, A. C., Dale, J. J., Holland, K. N., and Wilson, R. P. (2010).
Accelerating estimates of activity-specific metabolic rate in fishes: testing the
applicability of acceleration data-loggers. J. Exp. Mar. Biol. Ecol. 385, 85–91.
doi: 10.1016/j.jembe.2010.01.012
Gui, F., Wang, P., and Wu, C. (2014). Evaluation approaches of fish swimming
performance. Agric. Sci. 5:42552. doi: 10.4236/as.2014.52014
Halsey, L. G., Green, J. A., Wilson, R. P., and Frappell, P. B. (2009). Accelerometry
to estimate energy expenditure during activity: best practice with data loggers.
Physiol. Biochem. Zool. 82, 396–404. doi: 10.1086/589815
Halsey, L. G., Shepard, E. L., and Wilson, R. P. (2011). Assessing the
development and application of the accelerometry technique for estimating
energy expenditure. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 158,
305–314. doi: 10.1016/j.cbpa.2010.09.002
Hussey, N. E., Kessel, S. T., Aarestrup, K., Cooke, S. J., Cowley, P. D., Fisk,
A. T., et al. (2015). Aquatic animal telemetry: a panoramic window into the
underwater world. Science 348:1255642. doi: 10.1126/science.1255642
Kolarevic, J., Aas-Hansen, Ø., Espmark, Å., Baeverfjord, G., Terjesen, B. F., and
Damsgård, B. (2016). The use of acoustic acceleration transmitter tags for
monitoring of Atlantic salmon swimming activity in recirculating aquaculture
systems (RAS). Aquac. Eng. 72, 30–39. doi: 10.1016/j.aquaeng.2016.03.002
Frontiers in Animal Science | 13 July 2021 | Volume 2 | Article 679848
Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
Kuznetsova, A., Brockhoff, P. B., and Christensen, R. H. B. (2017). lmerTest
package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26.
doi: 10.18637/jss.v082.i13
Lauder, G. V. (2015). Fish locomotion: recent advances and new directions. Ann.
Rev. Mar. Sci. 7, 521–545. doi: 10.1146/annurev-marine-010814-015614
Lowe, C. G., Holland, K., and Wolcott, T. G. (1998). A new
acoustic tailbeat transmitter for fishes. Fish. Res. 36, 275–283.
doi: 10.1016/S0165-7836(98)00109-X
Martins, C. I., Galhardo, L., Noble, C., Damsgård, B., Spedicato, M. T., Zupa,
W., et al. (2012). Behavioural indicators of welfare in farmed fish. Fish Physiol.
Biochem. 38, 17–41. doi: 10.1007/s10695-011-9518-8
Martos-Sitcha, J. A., Simó-Mirabet, P., Piazzon, M. C., de las Heras, V.,
Calduch-Giner, J. A., Puyalto, M., et al. (2018). Dietary sodium heptanoate
helps to improve feed efficiency, growth hormone status and swimming
performance in gilthead sea bream (Sparus aurata). Aquac. Nutr. 24,
1638–1651. doi: 10.1111/anu.12799
Martos-Sitcha, J. A., Sosa, J., Ramos-Valido, D., Bravo, F. J., Carmona-Duarte, C.,
Gomes, H. L., et al. (2019). Ultra-low power sensor devices for monitoring
physical activity and respiratory frequency in farmed fish. Front. Physiol.
10:667. doi: 10.3389/fphys.2019.00667
McKenzie, D. J., Palstra, A. P., Planas, J., MacKenzie, S., Bégout, M. L.,
Thorarensen, H., et al. (2020). Aerobic swimming in intensive finfish
aquaculture: applications for production, mitigation and selection. Rev. Aquac.
13, 1–18. doi: 10.1111/raq.12467
Metcalfe, J. D., Wright, S., Tudorache, C., and Wilson, R. P. (2016). Recent
advances in telemetry for estimating the energy metabolism of wild fishes. J.
Fish Biol. 88, 284–297. doi: 10.1111/jfb.12804
Montoya, A., López-Olmeda, J. F., Lopez-Capel, A., Sánchez-Vázquez,
F. J., and Pérez-Ruzafa, A. (2012). Impact of a telemetry-transmitter
implant on daily behavioral rhythms and physiological stress indicators
in gilthead seabream (Sparus aurata). Mar. Environ. Res. 79, 48–54.
doi: 10.1016/j.marenvres.2012.05.002
Muñoz, L., Aspillaga, E., Palmer, M., Saraiva, J. L., and Arechavala-Lopez,
P. (2020). Acoustic telemetry: a tool to monitor fish swimming behavior
in sea-cage aquaculture. Front. Mar. Sci. 7:645. doi: 10.3389/fmars.2020.
Murchie, K. J., Cooke, S. J., Danylchuk, A. J., and Suski, C. D. (2011).
Estimates of field activity and metabolic rates of bonefish (Albula vulpes)
in coastal marine habitats using acoustic tri-axial accelerometer transmitters
and intermittent-flow respirometry. J. Exp. Mar. Biol. Ecol. 396, 147–155.
doi: 10.1016/j.jembe.2010.10.019
Palstra, A. P., Arechavala-Lopez, P., Xue, Y., and Roque, A. (2021). Accelerometry
of seabream in a seacage: is acceleration a good proxy for activity? Front. Mar.
Sci. 8:144. doi: 10.3389/fmars.2021.639608
Palstra, A. P., Kals, J., Böhm, T., Bastiaansen, J. W., and Komen, H. (2020).
Swimming performance and oxygen consumption as non-lethal indicators of
production traits in Atlantic salmon and Gilthead seabream. Front. Physiol.
11:759. doi: 10.3389/fphys.2020.00759
Palstra, A. P., Mes, D., Kusters, K., Roques, J. A., Flik, G., Kloet, K., et al.
(2015). Forced sustained swimming exercise at optimal speed enhances
growth of juvenile yellowtail kingfish (Seriola lalandi). Front. Physiol. 5:506.
doi: 10.3389/fphys.2014.00506
Palstra, A. P., van Ginneken, V., and van den Thillart, G. (2008). Cost of transport
and optimal swimming speed in farmed and wild European silver eels (Anguilla
anguilla). Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 151, 37–44.
doi: 10.1016/j.cbpa.2008.05.011
R Core Team (2020). R: A Language and Environment for Statistical Computing.
Available online at:
Rouleau, S., Glémet, H., and Magnan, P. (2010). Effects of morphology on
swimming performance in wild and laboratory crosses of brook trout ecotypes.
Funct. Ecol. 24, 310–321. doi: 10.1111/j.1365-2435.2009.01636.x
c, T., Arechavala-Lopez, P., Vuˇ
c, I., Talijanˇ
c, I., Grubiši´
c, L., ŽuŽul,
I., et al. (2018). Site fidelity of farmed gilthead seabream Sparus aurata escapees
in a coastal environment of the Adriatic Sea. Aquac. Environ. Interact. 10,
21–34. doi: 10.3354/aei00251
Sfakiotakis, M., Lane, D. M., and Davies, J. B. C. (1999). Review of fish
swimming modes for aquatic locomotion. IEEE J. Ocean Eng. 24, 237–252.
doi: 10.1109/48.757275
Shepard, E. L., Wilson, R. P., Quintana, F., Laich, A. G., Liebsch, N., Albareda,
D. A., et al. (2008). Identification of animal movement patterns using tri-axial
accelerometry. Endanger. Species. Res. 10, 47–60. doi: 10.3354/esr00084
Steinhausen, M. F., Fleng Steffensen, J., and Gerner Andersen, N. (2010).
The effects of swimming pattern on the energy use of gilthead
seabream (Sparus aurata L.). Mar. Fresh. Behav. Physiol. 43, 227–241.
doi: 10.1080/10236244.2010.501135
Svendsen, J. C., Tirsgaard, B., Cordero, G. A., and Steffensen, J. F. (2015).
Intraspecific variation in aerobic and anaerobic locomotion: gilthead sea bream
(Sparus aurata) and Trinidadian guppy (Poecilia reticulata) do not exhibit a
trade-off between maximum sustained swimming speed and minimum cost of
transport. Front. Physiol. 6:43. doi: 10.3389/fphys.2015.00043
Thorstad, E. B., Rikardsen, A. H., Alp, A., and Økland, F. (2013). The use of
electronic tags in fish research—an overview of fish telemetry methods. Turk. J.
Fish. Aquat. Sci. 13, 881–896. doi: 10.4194/1303-2712-v13_5_13
Tucker, V. A. (1970). Energetic cost of locomotion in animals. Comp. Biochem.
Physiol. 34, 841–846. doi: 10.1016/0010-406X(70)91006-6
Van Den Thillart, G. E. E. J., Van Ginneken, V., Körner, F., Heijmans, R., Van der
Linden, R., and Gluvers, A. (2004). Endurance swimming of European eel. J.
Fish Biol. 65, 312–318. doi: 10.1111/j.0022-1112.2004.00447.x
Webb, P. W., and Weihs, D. (eds.). (1983). Fish Biomechanics. New York, NY:
Praeger Publishers.
Wikelski, M., and Cooke, S. J. (2006). Conservation physiology. Trends Ecol. Evol.
21, 38–46. doi: 10.1016/j.tree.2005.10.018
Wilson, S. M., Hinch, S. G., Eliason, E. J., Farrell, A. P., and Cooke, S. J. (2013).
Calibrating acoustic acceleration transmitters for estimating energy use by wild
adult Pacific salmon. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 164,
491–498. doi: 10.1016/j.cbpa.2012.12.002
Wright, D. W., Stien, L. H., Dempster, T., and Oppedal, F. (2019). Differential
effects of internal tagging depending on depth treatment in Atlantic salmon:
a cautionary tale for aquatic animal tag use. Curr. Zool. 65, 665–673.
doi: 10.1093/cz/zoy093
Wright, S., Metcalfe, J. D., Hetherington, S., and Wilson, R. (2014). Estimating
activity-specific energy expenditure in a teleost fish, using accelerometer
loggers. Mar. Ecol. Progr. Ser. 496, 19–32. doi: 10.3354/meps10528
Zupa, W., Alfonso, S., Gai, F., Gasco, L., Spedicato, M. T., Lembo, G., et al. (2021).
Calibrating accelerometer tags with oxygen consumption rate of rainbow trout
(Oncorhynchus mykiss) and their use in aquaculture facility: a case study.
Animals 11:1496. doi: 10.3390/ani11061496
Conflict of Interest: CD-G was employed by the company Xelect Ltd. (St.
Andrews, United Kingdom).
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
Copyright © 2021 Arechavala-Lopez, Lankheet, Díaz-Gil, Abbink and Palstra. This
is an open-access article distributed under the terms of the Creative Commons
Attribution License (CC BY). The use, distribution or reproduction in other forums
is permitted, provided the original author(s) and the copyright owner(s) are credited
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Frontiers in Animal Science | 14 July 2021 | Volume 2 | Article 679848
Supplementary Figure S1: Distribution of main estimated parameters on Sparus aurata
during swim-tunnel trials at different swimming speeds. LEFT: accelerations and fitted
Gaussian lines; CENTER-LEFT: tail beat frequency (TBF) observations and fitted
Gaussian lines; CENTER-RIGHT: head orientation values (HO) and fitted Gaussian lines
(Note that the exact swimming position against the current is in HO = 1.57 rad); RIGHT:
head orientation frequency (HOF) and fitted Gaussian lines.
... The gilthead sea bream (Sparus aurata Linnaeus, 1758) plays an ecological key role and is of primary importance for European marine aquaculture [34]. However, little information is known about the swimming performance and energy expenditure of this species [35][36][37][38][39][40]. The aim of this study was to correlate the acceleration recorded by accelerometer tags with MO 2 and the activity patterns of the red and white muscles to later estimate the energetic costs of different life activities in free-swimming tagged fish. ...
... The species is characterised by a carangiform swimming locomotion, in which the thrust is produced by the rear third of the body length, while the anterior part is relatively inflexible, and a rigid caudal fin that accommodates the fish's turning and accelerating abilities [54,56]. In the present study, the values of relative U crit ranged from 2.02 to 4.79 BL/s and were consistent with the values reported in previous studies for this species [35,38]. In addition, the swimming performance (either absolute or relative U crit ) of the sea bream during the critical swimming speed test was reduced with the increase in fish body mass. ...
... To account for this difference in swimming performance between the fish under different conditions (untagged, tagged, and EMG), when further analysing the muscle activity pattern, the EMG signal was studied as a function of the relative percentage of U crit (Figure 6; instead of the water velocity step), and EMG implanted fish were not used to model the MO 2 during the U crit test and to estimate the metabolic traits. However, the accelerometer tag insertion in the body cavity (5 days before the test) did not trigger any significant changes in the swimming performance of the tested fish, showing that (i) the tag implantation (5 days before the trial) did not impact the swimming performance of the fish, which suggests the low invasiveness of the surgery, as also shown by previous studies [38,49,58]. Additionally, it showed that (ii) the reliability of the measurements performed on the fish implanted with a transmitter can be expected to be similar to that of the measurements performed on the fish without a transmitter, which supports their use as a tool for remote health/welfare monitoring in aquaculture conditions. ...
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Measurement of metabolic rates provides a valuable proxy for the energetic costs of different living activities. However, such measurements are not easy to perform in free-swimming fish. Therefore, mapping acceleration from accelerometer tags with oxygen consumption rates (MO2) is a promising method to counter these limitations and could represent a tool for remotely estimating MO2 in aquaculture environments. In this study, we monitored the swimming performance and MO2 of 79 gilthead sea bream (Sparus aurata; weight range, 219–971 g) during a critical swimming test. Among all the fish challenged, 27 were implanted with electromyography (EMG) electrodes, and 27 were implanted with accelerometer tags to monitor the activation pattern of the red/white muscles during swimming. Additionally, we correlated the acceleration recorded by the tag with the MO2. Overall, we found no significant differences in swimming performance, metabolic traits, and swimming efficiency between the tagged and untagged fish. The acceleration recorded by the tag was successfully correlated with MO2. Additionally, through EMG analyses, we determined the activities of the red and white muscles, which are indicative of the contributions of aerobic and anaerobic metabolisms until reaching critical swimming speed. By obtaining insights into both aerobic and anaerobic metabolisms, sensor mapping with physiological data may be useful for the purposes of aquaculture health/welfare remote monitoring of the gilthead sea bream, a key species in European marine aquaculture.
... Remote physiological data can be valuable for understanding the limits of sustained exercise for animals in the wild and their allocation of aerobic and anaerobic pathways on daily or seasonal bases. Transmitted acceleration data calibrated to respirometry have successfully provided new insights to swimming performance and energy expenditure of free-ranging aquatic animals, in many cases (but see [2]. For example, Payne et al. [53] estimated the swimming speed and oxygen consumption of giant Australian Cuttlefish (Sepia apama) aggregating at spawning grounds in the northern Spencer Gulf, South Australia, by calibrating transmitted acceleration data to observed swimming speeds and oxygen consumption. ...
... Elasmobranchs were categorised separate from fish for additional resolution semi-captive juvenile lemon sharks (Negaprion brevirostris) in a mesocosm had highest swimming velocities and energy expenditure during diurnal periods and flooding tides; these experimental approaches are effective for better understanding costs of life for animals in their environment. Some variation can be expected depending on the body plan and swimming mode of the fish, and Arechavala-Lopez et al. [2] did not find that accelerometer transmitters effectively identified states of sustained high activity swimming in sea bream (Sparus aurata). Calibration experiments are useful for enabling comparisons between studies by transforming sensor output to common units such as swimming speed and energy expenditure, or by establishing direct relationships between output from different sensor types, tagging methods, or accelerometer-derived metrics. ...
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There has recently been great interest in the use of accelerometers onboard electronic transmitters to characterise various aspects of the ecology of wild animals. We review use cases and outline how these tools can provide opportunities for studying activity and survival, exercise physiology of wild animals, the response to stressors, energy landscapes and conservation planning tools, and the means with which to identify behaviours remotely from transmitted data. Accelerometer transmitters typically send data summaries to receivers at fixed intervals after filtering out static acceleration and calculating root-mean square error or overall dynamic body action of 2- or 3-axis acceleration values (often at 5–12.5 Hz) from dynamic acceleration onboard the tag. Despite the popularity of these transmitters among aquatic ecologists, we note that there is wide variation in the sampling frequencies and windows used among studies that will potentially affect the ability to make comparisons in the future. Accelerometer transmitters will likely become increasingly popular tools for studying finer scale details about cryptic species that are difficult to recapture and hence not suitable for studies using data loggers. We anticipate that there will continue to be opportunities to adopt methods used for analysing data from loggers to datasets generated from acceleration transmitters, to generate new knowledge about the ecology of aquatic animals.
... The wild adult chinook (Oncorhynchus tshawytscha) and coho salmon (Oncorhynchus kisutch) were among the first fish species used in such studies (Trefethen, 1956). Since then, acoustic transmitters have been incorporated in studies of a wide range of marine species (Brownscombe et al., 2019;Klinard and Matley, 2020) such as sharks (Espinoza et al., 2021), the salmonid smolt (Huusko et al., 2016), the Atlantic cod (Gadus morhua; Meager et al., 2009), the European seabass (Dicentrarchus labrax; Anras et al., 1997;Stamp et al., 2021), and the gilthead seabream (Sparus aurata; Arechavala-Lopez et al., 2012;Arechavala-Lopez et al., 2021). In addition, studies have benefited from the use of acoustic transmitters (Meager et al., 2009;Brownscombe et al., 2019;Muñoz et al., 2020); for example, studies on fish locomotion (Espinoza et al., 2021), the estimation of energetic costs (Wright et al., 2014;Zupa et al., 2015;Zupa et al., 2021;Alfonso et al., 2021a;Alfonso et al., 2022), residency patterns and habitat usage (Espinoza et al., 2020;Zhang et al., 2020;Lippi et al., 2022;Marques et al., 2022), intra-species/inter-species interactions (Barkley et al., 2020;Lees et al., 2020), feeding behavior (Føre et al., 2011), ecophysiology, and reproductive behavior (Klinard and Matley, 2020). ...
... Telemetry sensors are explored as a tool for health and welfare monitoring of E. seabass farming (Alfonso et al., 2020a;Carbonara et al., 2021;Alfonso et al., 2022) in assessing optimal stocking density and diet regimes (Begout and Lagardere, 1995;Anras et al., 1997). Furthermore, acoustic transmitters have been used to study the swimming activity, space utilization, response of fish to variations in stocking density (Carbonara et al., 2019), and physiological response of gilthead seabream in aquaculture conditions (Alfonso et al., 2021b;Arechavala-Lopez et al., 2021). ...
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The usefulness of acoustic telemetry on the study of movements, interactions, and behaviors has been revealed by many field and laboratory studies. The process of attaching acoustic tags on fish can, however, impact their physiological, behavioral, and growth performance traits. The potential negative effects are still unknown for several species and behavioral attributes. Previous studies have attempted to shed light on the effects of tag implantation on fish, focusing mainly on fish growth and physiological parameters, and one or two behavioral properties mainly on the individual level. However, the effect of this procedure could also be expressed at the group level. This study investigated the short-term effects of dummy and active body-implanted acoustic tags on the group-level swimming performance of adult European seabass ( Dicentrarchus labrax ) using optical flow analysis. We studied four main swimming performance properties—group speed, alignment (polarization), cohesion, and exploratory behavior. To help in the interpretation of any detected differences, physiological stress-related parameters were also extracted. The results show that the tag implantation procedure has variable effects on the different swimming performance attributes of fish. Group cohesion, polarization, and the group’s exploratory tendency were significantly impacted initially, and the effect persisted but to a lesser extent two weeks after surgery. In contrast, group speed was not affected initially but showed a significant decrease in comparison with the control group two weeks post-surgery. In addition, the physiological parameters tested did not show any significant difference between the control and the treated group 14 days after the onset of the experiment. The findings suggest that the effect of tagging is non-trivial, leading to responses and response times that could affect behavioral studies carried out using acoustic telemetry.
... Low sampling frequencies are also convenient for a realtime transmission of the averaged accelerations. Thus, the use of low frequency sampling devices is adequate for a continuous fish monitoring in sea cages of unsteady movements and to transmit low bit rate data in real time via acoustic communication channels, though they are less informative about other subtler events that can be of importance for the assessment of the overall welfare status (Murchie et al., 2011;Wilson et al., 2013;Arechavala-Lopez et al., 2021). Indeed, the capture of sustained movement with an accelerometer requires not only an adequate tag implantation location , but also high sampling rates that have been usually established to be higher than 30 Hz (Broell et al., 2013). ...
... Such approach provides the opportunity to experience new situations typical of the species in the wild, and it is generally accepted that well-designed EE (e.g., stones, roots, logs, plants, algae, sand, sessile animals, ice, artificial objects, etc.) improves behavioral flexibility and cognitive ability of fish by changes in physical activity. Indeed, modifications in swimming behavior reflect how a fish is sensing and responding to its environment, which is reflected by changes in spatial distribution and ventilation activity to ensure the supply of O 2 at the exact rate required by the organism (Martins et al., 2012;Zupa et al., 2015;Arechavala-Lopez et al., 2021). Thus, video recording analysis highlighted that physical EE promoted the increase of antioxidant enzyme activity, exploratory behavior and learning abilities of gilthead sea bream juveniles reared in tank-based systems . ...
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Behavioral parameters are reliable and useful operational welfare indicators that yield information on fish health and welfare status in aquaculture. However, aquatic environment is still constraining for some solutions based on underwater cameras or echo sounder transmitters. Thus, the use of bio-loggers internally or externally attached to sentinel fish emerges as a solution for fish welfare monitoring in tanks- and sea cages-rearing systems. This review is focused on the recently developed AEFishBIT, a small and light data storage tag designed to be externally attached to fish operculum for individual and simultaneous monitoring of swimming activity and ventilation rates under steady and unsteady swimming conditions for short-term periods. AEFishBIT is a tri-axial accelerometer with a frequency sampling of 50–100 Hz that is able to provide proxy measurements of physical and metabolic activities validated by video recording, exercise tests in swim tunnel respirometers, and differential operculum and body tail movements across fish species with differences in swimming capabilities. Tagging procedures based on tag piercing and surgery procedures are adapted to species anatomical head and operculum features, which allowed trained operators to quickly complete the tagging procedure with a fast post-tagging recovery of just 2.5–7 h in both salmonid (rainbow trout, Atlantic salmon) and non-salmonid (gilthead sea bream, European sea bass) farmed fish. Dual recorded data are processed by on-board algorithms, providing valuable information on adaptive behavior through the productive cycle with the changing environment and genetics. Such biosensing approach also provides valuable information on social behavior in terms of adaptive capacities or changes in daily or seasonal activity, linking respiratory rates with changes in metabolic rates and energy partitioning between growth and physical activity. At short-term, upcoming improvements in device design and accompanying software are envisaged, including energy-harvesting techniques aimed to prolong the battery life and the addition of a gyroscope for the estimation of the spatial distribution of fish movements. Altogether, the measured features of AEFishBIT will assist researchers, fish farmers and breeders to establish stricter welfare criteria, suitable feeding strategies, and to produce more robust and efficient fish in a changing environment, helping to improve fish management and aquaculture profitability.
... The flow in the swim-tunnel was set at five different speeds during the experiment, starting at the lowest speed of 0.1 m.s −1 and then increasing stepwise with 0.1 m.s −1 per hour up to a maximum speed of 0.5 m.s −1 . Oxygen consumption and locomotory behavior were assessed at each interval using a galvanic oxygen probe and a Basler 2040-90um NIR USB3 high-speed camera, respectively (see also Arechavala-Lopez et al., 2021). ...
Full-text available
Swimming capacity plays a crucial role in the fitness of fish, crucial for their survival and reproductive success. Origin and early life experiences may have important consequences for swimming capacity later in life (Zambonino-Infante et al., 2017; Vandeputte et al., 2019). In this study, we investigated the influence of origin and early life exercise training on the swimming economy and locomotory behaviour during later life in the European seabass (Dicentrarchus labrax).
... 157 To date, acoustic telemetry techniques have been widely applied in assessing swimming behaviour in sea-cage aquaculture of Chinook salmon (Oncorhychus tshawytscha), 158 Atlantic cod (Gadus morhua), 159 Atlantic salmon (Salmo salar) 160 and Gilt-head seabream. 147 The development of some acoustic tags integrates depth sensors and acceleration sensors, which can provide the exact location and swimming activity information of marked fish. 32 The acoustic telemetry system has an excellent performance in distinguishing feeding behaviour and other activity modes, 161 and exploring the influence of different breeding densities on swimming behaviour. ...
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Acoustic technology has great application prospects in aquaculture. In particular, two indispensable, critical technologies for the future aquaculture industry are multi‐sensor acquisition that can achieve multi‐scale information fusion, collection and establishment of a global acoustic fish database and highly developed deep learning intelligent algorithms that can establish a correlation mechanism between fish acoustic and behaviour characteristics. Acoustic technology offers remarkable advantages in large and turbid water bodies for studying spatial and temporal distribution patterns of aquatic organism populations, developing on‐demand feeding systems and estimating biomass. This article reviews the development of acoustic technology and its application in aquaculture over the last 30 years. It further analyses, in detail, the advantages and disadvantages of acoustic technology in evaluating aquatic organism biomass and morphological and physical indicators, aquatic organism behaviour and welfare improvement. Challenges of acoustic technology in acquiring dynamic target data accurately, building a global acoustic fish database and establishing connections between fish behaviour and acoustic characteristics are also discussed. In brief, this article aims to help researchers and practitioners better understand the current state‐of‐the‐art acoustic technologies, which can provide strong support for smart aquaculture applications.
... Therefore, measuring U crit and understanding the swimming capability in key aquaculture species are of primary importance to make a correct farming site selection while avoiding stress and/or high energetic costs due to high currents exposure (Oppedal et al., 2011;Remen et al., 2016;McKenzie et al., 2021). In addition, the implantation of the accelerometer tag in the fish body cavity has been found not to trigger any significant disruption of the swimming performance, suggesting low invasiveness for tag implantation, as previously shown in various studies (Alfonso et al., , 2021aArechavala-Lopez et al., 2021;Føre et al., 2021), and so, the trust that the measures carried out in implanted fish can be expected to be similar to fish without, as long as a few conditions are met (Jepsen et al., 2005(Jepsen et al., , 2011Macaulay et al., 2021). In conclusion, the tagged fish can be considered as highly representative of the entire monitored fish population (i.e., both tagged and untagged) . ...
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Physiological real-time monitoring could help to prevent health and welfare issues in farmed fishes. Among physiological features that can be of interest for such purposes, there is the metabolic rate. Its measurement remains, however, difficult to be implemented in the field. Thus, mapping the fish acceleration recorded by tag with the oxygen consumption rate (MO2) could be promising to counter those limitations and to be used as a proxy for energy expenditure in the aquaculture environments. In this study, we investigated the swimming performance (Ucrit) and the swimming efficiency (Uopt, COTmin), and we estimated the metabolic traits (standard and maximum metabolic rates, SMR and MMR, as well the absolute aerobic scope, AS) of European sea bass (Dicentrarchus labrax; n = 90) in swimming tunnel. Among all tested fish, 40 fishes were implanted with an acoustic transmitter to correlate the acceleration recorded by the sensor with the MO2. In this study, the mean SMR, MMR, and AS values displayed by sea bass were 89.8, 579.2, and 489.4 mgO2 kg−1 h−1, respectively. The Uopt and COTmin estimated for sea bass were on average 1.94 km h−1 and 113.91 mgO2 kg−1 h−1, respectively. Overall, implantation of the sensor did not alter fish swimming performance or induced particular stress, able to increase MO2 or decrease swimming efficiency in tagged fish. Finally, acceleration recorded by tag has been successfully correlated with MO2 and fish mass using a sigmoid function (R2 = 0.88). Overall, such results would help for real-time monitoring of European sea bass health or welfare in the aquaculture environment in a framework of precision livestock farming.
Aerobic swimming exercise in fish has been shown to improve robustness of some species. However, the optimal conditions to be applied and the mechanisms underlying remain unknown. We investigated the effects of 6 h of induced swimming on the immune response of gilthead seabream (Sparus aurata), by analysing markers related to immune status in plasma, skin mucus, gills, heart and head-kidney. Forty fish were individually exercised in swim tunnels by applying different water currents: steady low (SL, 0.8 body lengths (BL) s-1), steady high (SH, 2.3 BL s-1), oscillating low (OL, 0.2/0.8 BL s-1) and oscillating high (OH, 0.8/2.3 BL s-1) velocities, including a non-exercised group with minimal water flow (MF, <0.1 BL s-1). Swimming conditions did not trigger a stress response or anaerobic metabolism, suggested by similar levels of cortisol, lactate, and glucose in plasma among groups. Blood haemoglobin and innate immune parameters in plasma and skin mucus also remained unaltered. However, decreased blood haematocrit was observed in fish swimming on the OL condition. Interestingly, gene expression analysis revealed that the OL condition led to the up-regulation of pro-inflammatory mediators (nfκb1 and mapk3) and cytokines (tnfα, il1β and il6) in gills. A similar response occurred in heart, with an up-regulation of nfκb1, tnfα, il6 and cox2 in the OL condition. Gene expression of these cytokines was unaltered in the head-kidney. The inflammatory response in gills and heart of gilthead seabream triggered by the OL condition highlights the importance of establishing suitable rearing conditions to improve welfare of cultured fish.
Feed conversion ratio (FCR) is an important trait to target in fish breeding programs, and the aim of the present study is to underline how the genetic improvement of FCR in gilthead sea bream (Sparus aurata) drives to changes in transcriptional and behavioural patterns. Groups of fish with high (FCR+) and low (FCR-) individual FCR were established at the juvenile stage (161–315 dph) by rearing isolated fish on a restricted ration. Fish were then grouped on the basis of their individual FCR and they grew up until behavioural monitoring and gene expression analyses were done at 420 dph. The AEFishBIT datalogger (externally attached to operculum) was used for simultaneous measurements of physical activity and ventilation rates. This allowed discrimination of FCR+ and FCR- groups according to their different behaviour and energy partitioning for growth and locomotor activity. Gene expression profiling of liver and white muscle was made using customized PCR-arrays of 44 and 29 genes, respectively. Up to 15 genes were differentially expressed in liver and muscle tissues highlighting a different metabolic scope of FCR+ and FCR- fish. Hepatic gene expression profile of FCR- fish displayed a lower lipogenic activity that was concurrent with a down-regulation of markers of mitochondrial activity and oxidative stress, as well as a reallocation of body fat depots with an enhanced flux of lipids towards skeletal muscle. Muscle gene expression profile of FCR- fish matched with stimulatory and inhibitory growth signals, and an activation of energy sensors and antioxidant defence as part of the operating mechanisms for a more efficient muscle growth. These new insights contribute to phenotype the genetically mediated differences in fish FCR thanks to the combination of transcriptomic and behavioural approaches that contribute to better understand the mechanisms involved in a reliable FCR improvement of farmed gilthead sea bream.
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Metabolic rates are linked to the energetic costs of different activities of an animal’s life. However, measuring the metabolic rate in free-swimming fish remains challenging due to the lack of possibilities to perform these direct measurements in the field. Thus, the calibration of acoustic transmitters with the oxygen consumption rate (MO2) could be promising to counter these limitations. In this study, rainbow trout (Oncorhynchus mykiss Walbaum, 1792; n = 40) were challenged in a critical swimming test (Ucrit) to (1) obtain insights about the aerobic and anaerobic metabolism throughout electromyograms; and (2) calibrate acoustic transmitters’ signal with the MO2 to be later used as a proxy of energetic costs. After this calibration, the fish (n = 12) were implanted with the transmitter and were followed during ~50 days in an aquaculture facility, as a case study, to evaluate the potential of such calibration. Accelerometer data gathered from tags over a long time period were converted to estimate the MO2. The MO2 values indicated that all fish were reared under conditions that did not impact their health and welfare. In addition, a diurnal pattern with higher MO2 was observed for the majority of implanted trout. In conclusion, this study provides (1) biological information about the muscular activation pattern of both red and white muscle; and (2) useful tools to estimate the energetic costs in free-ranging rainbow trout. The use of acoustic transmitters calibrated with MO2, as a proxy of energy expenditure, could be promising for welfare assessment in the aquaculture industry.
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Activity assessment of individual fish in a sea-cage could provide valuable insights into the behavior, but also physiological well-being and resilience, of the fish population in the cage. Acceleration can be monitored continuously with internal acoustic transmitter tags and is generally applied as a real-time proxy for activity. The objective of this study was to investigate the activity patterns of Gilthead seabream (Sparus aurata) by transmitter tags in a sea-cage and analyze correlations with water temperature, fish size and tissue weights. Experimental fish (N=300) were transferred to an experimental sea-cage of which thirty fish (Standard Length SL= 18.3 ±1.7 cm; Body Weight BW= 174 ± 39 g) were implanted with accelerometer tags. Accelerations were monitored for a period of 6 weeks (Nov.-Dec.) and were analyzed over the 6 weeks and 24 hours of the day. At the end of the experimental period, tagged fish were again measured, weighed and dissected for tissue and fillet weights, and correlations with accelerations were analyzed. Daily rhythms in accelerations under the experimental conditions were characterized by more active periods from 6 to 14 h and 18 to 0 h and less active periods from 0 to 6 h and 14 to 18 h. This W-shaped pattern remained over the experimental weeks, even with diurnal accelerations decreasing which was correlated to the dropping temperature. The increase in activity was not during, but just before feeding indicating food-anticipatory activity. Activity patterning can be useful for timing feeding events at the start of active periods, in this study between 6 and 11 h, and between 18 and 22 h. Acceleration was negatively correlated to heart and mesenteric fat mass, which was the exact contrary of our expectations for sustainedly swimming seabream. These results suggest that acceleration is a proxy for unsteady swimming activity only and research is required into the accelerations occurring during sustained swimming of seabream at various speeds.
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Acoustic telemetry techniques are very useful tools to monitor in detail the swimming behaviour and spatial use of fish in artificial rearing environments at individual and group levels. We evaluated the feasibility of using passive acoustic telemetry to monitor fish welfare in sea-cage aquaculture at an industrial scale, characterizing for the first time the diel swimming and distribution patterns of gilthead seabream (Sparus aurata) at fine-scale. Ten fish were implanted with acoustic tags equipped with pressure and acceleration sensors, and monitored in a commercial-size sea-cage for a period of one month. Overall, fish exhibited clear differences in day vs night patterns both on swimming activity and vertical distribution throughout the experiment. Space use increased at night after the implementation of structural environmental enrichment in the sea-cage. Acoustic telemetry may represent an advancement to monitor fish farming procedures and conditions, helping to promote fish welfare and product quality.
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The aim of this study was to investigate swimming performance and oxygen consumption as non−lethal indicator traits of production parameters in Atlantic salmon Salmo salar L. and Gilthead seabream Sparus aurata L. A total of 34 individual fish of each species were subjected to a series of experiments: (1) a critical swimming speed (Ucrit) test in a swim-gutter, followed by (2) two starvation-refeeding periods of 42 days, and (3) swimming performance experiments coupled to respirometry in swim-tunnels. Ucrit was assessed first to test it as a predictor trait. Starvation-refeeding traits included body weight; feed conversion ratio based on dry matter; residual feed intake; average daily weight gain and loss. Swim-tunnel respirometry provided oxygen consumption in rest and while swimming at the different speeds, optimal swim speed and minimal cost of transport (COT). After experiments, fish were dissected and measured for tissue weights and body composition in terms of dry matter, ash, fat, protein and moist, and energy content. The Ucrit test design was able to provide individual Ucrit values in high throughput manner. The residual Ucrit (RUcrit) should be considered in order to remove the size dependency of swimming performance. Most importantly, RUcrit predicted filet yield in both species. The minimal COT, the oxygen consumption when swimming at Uopt, added predictive value to the seabream model for feed intake.
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Background Telemetry applied to aquatic organisms has recently developed greatly. Physiological sensors have been increasingly used as tools for fish welfare monitoring. However, for the technology to be used as a reliable welfare indicator, it is important that the tagging procedure does not disrupt fish physiology, behaviour and performance. In this communication, we share our medium-term data on stress physiological profile and growth performance after surgical tag implantation in two important marine fish species for European aquaculture, the sea bream ( Sparus aurata ) and the European sea bass ( Dicentrarchus labrax ). Results Blood samples after surgical tag implantation (46 days for the sea bream and 95 days for the sea bass) revealed no differences between tagged and untagged fish in cortisol, glucose and lactate levels, suggesting that the tag implantation does not induce prolonged stress in these species. Moreover, the specific growth rates were similar in the tagged and untagged fish of both species. Conclusion Surgical tag implantation does not have medium-term consequences for the stress physiology and growth performance of these two marine fish species in a controlled environment. These observations support the use of accelerometer tags as valuable tools for welfare monitoring in aquaculture conditions. This study also shows that tagged fish can be sampled during experiments and considered a representative portion of the population, as they display growth and physiological parameters comparable to those of untagged fish.
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Integration of technological solutions aims to improve accuracy, precision and repeatability in farming operations, and biosensor devices are increasingly used for understanding basic biology during livestock production. The aim of this study was to design and validate a miniaturized tri-axial accelerometer for non-invasive monitoring of farmed fish with re-programmable schedule protocols. The current device (AE-FishBIT v.1s) is a small (14 x 7 x 7 mm), stand-alone system with a total mass of 600 mg, which allows monitoring animals from 30-35 g onwards. The device was attached to the operculum of gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax) juveniles for monitoring their physical activity by measurements of movement accelerations in x- and y-axes, while records of operculum beats (z-axis) served as a measurement of respiratory frequency. Data post-processing of exercised fish in swimming test chambers revealed an exponential increase of fish accelerations with the increase of fish speed from 1 body-length to 4 body-lengths per second, while a close relationship between oxygen consumption (MO2) and opercular frequency was consistently found. Preliminary tests in free-swimming fish kept in rearing tanks also showed that device data recording was able to detect changes in daily fish activity. The usefulness of low computational load for data pre-processing with on-board algorithms was verified from low to submaximal exercise, increasing this procedure the autonomy of the system up to 6 h of data recording with different programmable schedules. Visual observations regarding tissue damage, feeding behaviour and circulating levels of stress markers (cortisol, glucose, lactate) did not reveal at short term a negative impact of device tagging. Reduced plasma levels of triglycerides revealed a transient inhibition of feed intake in small fish (sea bream 50-90 g, sea bass 100-200 g), but this disturbance was not detected in larger fish. All this considered together is the proof of concept that miniaturized devices are suitable for non-invasive and reliable metabolic phenotyping of farmed fish to improve their overall performance and welfare. Further work is underway for improving the attachment procedure and the full device packaging.
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Electronic tags are widespread tools for studying aquatic animal behaviour, however tags risk behavioural manipulation and negative welfare outcomes. During an experiment to test behavioural differences of Atlantic salmon Salmo salar in different aquaculture cage types, including ones expected to elicit deeper swimming behaviour, we found negative tagging effects depending on whether cages were depth-modified. In the experiment, data storage tags implanted in Atlantic salmon tracked their depth behaviour and survival in unmodified sea-cages and depth-modified sea-cages that forced fish below or into a narrow seawater- or freshwater-filled snorkel tube from a 4 m net roof to the surface. All tagged individuals survived in unmodified cages, however survival was reduced to 62% in depth-modified cages. Survivors in depth-modified cages spent considerably less time above 4 m than those in unmodified cages, and dying individuals in depth-modified cages tended to position in progressively shallower water. The maximum depth that fish in our study could attain neutral buoyancy was estimated at 22 m in seawater. We calculated that the added tag weight in water reduced this to 8 m, and subtracting the tag volume from the peritoneal cavity where the swim bladder reinflates reduced this further to 4 m. We conclude that the internal tag weight and volume affected buoyancy regulation as well as the survival and behaviour of tagged fish. Future tagging studies on aquatic animals should carefully consider the buoyancy-related consequences of internal tags with excess weight in water, and the inclusion of data from dying tagged animals when estimating normal depth behaviours.
As other vertebrates, fish differ on an individual basis in their responses to disturbance (i.e. stress) and in their capacity for adaptation to environmental change. This individual stress-coping style (SCS) might have an impact on the individual welfare in aquaculture facilities. However, most of the studies about the behavioral and physiological stress responses of fish with different SCS were performed under conditions very unlike usual rearing conditions. Therefore, we aimed in the current study at investigating the behavioral and physiological responses of rainbow trout (Oncorhynchus mykiss) of different SCS to common aquaculture stressors (exposure to hypoxia or high ammonia) under realistic conditions. We first screened the fish according to their competitive ability, as a proxy of SCS, separating the best and worst competitors (winners, W, and losers, L, respectively). Then, we evaluated the behavioral (using both telemetric and video-monitoring approaches ) and physiological response of both groups upon exposure to increasing levels of hypoxia and ammonia. Overall, increasing hypoxia induced a slight but progressive decrease in fish activity, independently of the fish SCS. High concentrations of total ammonia induced an increase of certain behavioral displays, such as swimming bursts or approaches to the surface, the latter being overall more frequent in W than in L fish. At the specific stress levels tested (hypoxia: 50 % oxygen saturation; total ammonia: 2.91 mM), the physiological stress markers showed that the behavioral response to ammonia was probably driven by stress, while the behavioral response of the fish to hypoxia was just a behavioral adjustment to accommodate the decrease in oxygen availability. In conclusion, our data show that fish of different competitive ability showed similar activity patterns in routine conditions or when adapting to non-stressful conditions (50 % oxygen saturation) in an aquaculture-like setup, but differed in their behavioral response when exposed to stress (high water ammonia). In addition, our data support the use of acoustic accelerometer transmitters as a promising tool for real-time monitoring of fish welfare in commercial fish farms.
We review knowledge on applications of sustained aerobic swimming as a tool to promote productivity and welfare of farmed fish species. There has been extensive interest in whether providing active species with a current to swim against can promote growth. The results are not conclusive but the studies have varied in species, life stage, swimming speed applied, feeding regime, stocking density and other factors. Therefore, much remains to be understood about mechanisms underlying findings of ‘swimming‐enhanced growth’, in particular to demonstrate that swimming can improve feed conversion ratio and dietary protein retention under true aquaculture conditions. There has also been research into whether swimming can alleviate chronic stress, once again on a range of species and life stages. The evidence is mixed but swimming does improve recovery from acute stresses such as handling or confinement. Research into issues such as whether swimming can improve immune function and promote cognitive function is still at an early stage and should be encouraged. There is promising evidence that swimming can inhibit precocious sexual maturation in some species, so studies should be broadened to other species where precocious maturation is a problem. Swimming performance is a heritable trait and may prove a useful selection tool, especially if it is related to overall robustness. More research is required to better understand the advantages that swimming may provide to the fish farmer, in terms of production, mitigation and selection.
The AEFishBIT tri-axial accelerometer was externally attached to the operculum to assess the divergent activity and respiratory patterns of two marine farmed fish, the gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax). Analysis of raw data from exercised fish highlighted the large amplitude of operculum aperture and body tail movements in European sea bass, which were overall more stable at low-medium exercise intensity levels. Cosinor analysis in free-swimming fish (on-board data processing) highlighted a pronounced daily rhythmicity of locomotor activity and respiratory frequency in both gilthead sea bream and European sea bass. Acrophases of activity and respiration were coupled in gilthead sea bream, acting feeding time (once daily at 11:00 h) as a main synchronizing factor. By contrast, locomotor activity and respiratory frequency were out of phase in European sea bass with activity acrophase on early morning and respiration acrophase on the afternoon. The daily range of activity and respiration variation was also higher in European sea bass, probably as part of the adaptation of this fish species to act as a fast swimming predator. In any case, lower locomotor activity and enhanced respiration were associated with larger body weight in both fish species. This agrees with the notion that selection for fast growth in farming conditions is accompanied by a lower activity profile, which may favor an efficient feed conversion for growth purposes. Therefore, the use of behavioral monitoring is becoming a reliable and large-scale promising tool for selecting more efficient farmed fish, allowing researchers and farmers to establish stricter criteria of welfare for more sustainable and ethical fish production.