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ORIGINAL RESEARCH
published: 09 July 2021
doi: 10.3389/fanim.2021.679848
Frontiers in Animal Science | www.frontiersin.org 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
*Correspondence:
Pablo Arechavala-Lopez
pablo@fishethogroup.net
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
Citation:
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
Motion
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,
Netherlands
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
INTRODUCTION
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 | www.frontiersin.org 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-Bubi´
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.
MATERIALS AND METHODS
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 ±1◦C 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 20◦C, water temperature values during experiments were
20 ±1◦C. 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 ms−1per hour up
to 1 ms−1(Palstra et al., 2020) while accelerations, oxygen
consumption and locomotion were assessed within each interval.
Acoustic Accelerometer Transmitters and
Tagging
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 L−1). 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 mcots−2), averaged over all
samples in the sampling window, and were transformed into real
accelerations (in ms−2).
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 | www.frontiersin.org 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 s−1) 0.65 ±0.01 0.65 ±0.01 0.64 ±0.02 0.475 a
Uopt (BL s−1) 3.26 ±0.07 3.33 ±0.09 3.13 ±0.08 0.181 a
Ucrit (m s−1) 0.96 ±0.01 0.97 ±0.01 0.95 ±0.01 0.196 b
Ucrit (BL s−1) 4.78 ±0.06 4.92 ±0.07 4.64 ±0.08 0.016* a
COTmin (mg kg−1km−1) 179.2 ±9.7 189.6 ±11.1 160.4 ±16.3 0.155 a
Test: a, ANOVA; b, Kruskal-Wallis.
rate (MO2; in mgO2kg−1h−1) and cost of transport (COT; in
mg kg−1km−1) were calculated following the equations:
MO2= 1O2%DOmax ∗L
100
t!
COT =MO2
1d1000
where DOmax is the maximum amount of oxygen dissolved
in the water (9.47 mg O2L−1at a temperature of 20◦C 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 s−1and BL s−1), calculated according to Brett (1964); (ii)
Optimal speed (Uopt): the optimum swimming speed (m s−1
and BL s−1) where the cost of transport (COT, mgO2kg−1
km−1) reaches a minimum (Tucker, 1970); (iii) minimum cost of
transport (COTmin ): the cost of transport (mg O2 kg−1km−1) 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 | www.frontiersin.org 4July 2021 | Volume 2 | Article 679848
Arechavala-Lopez et al. Swimming Activity of Gilthead Seabream
Body Motion: Tail Beats and Head
Orientation
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 ms−1with increments of 0.2 ms−1). 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 | www.frontiersin.org 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).
RESULTS
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 ms−2), and minimum mean accelerations
at the highest speed (0.51 ±0.17 ms−2) (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).
Respirometry
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 ms−1(3.26 ±0.07 BL s−1) and a mean COTmin of 179.2
±9.7 mg kg−1km−1(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 ms−1
was considered. As a result, a mean Ucrit of 0.96 ±0.01 m
s−1(4.78 ±0.06 BL s−1) 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
Beats
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 s−1) and maximum values
at the highest speed (mean: 2.87 ±0.43 cycles s−1). No
significant differences were observed on HOF between tagged
Frontiers in Animal Science | www.frontiersin.org 6July 2021 | Volume 2 | Article 679848
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
distribution.
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 s−1) and the maximum TBF
values at the highest speed (mean: 4.13 ±0.34 cycles s−1).
There were no significant differences on TBF between tagged and
non-tagged seabream (GLMM, p=0.822).
Frontiers in Animal Science | www.frontiersin.org 7July 2021 | Volume 2 | Article 679848
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).
DISCUSSION
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 | www.frontiersin.org 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
Frontiers in Animal Science | www.frontiersin.org 9July 2021 | Volume 2 | Article 679848
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
speeds.
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 | www.frontiersin.org 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 | www.frontiersin.org 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.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The animal study was reviewed and approved by Dutch
Central Committee for Animal Experimentation (CCD
nr. AVD401002017817).
AUTHOR CONTRIBUTIONS
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
version.
FUNDING
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 | www.frontiersin.org 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.
ACKNOWLEDGMENTS
The authors thank T. Šegvi´
c-Bubi´
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.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fanim.
2021.679848/full#supplementary-material
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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
and that the original publication in this journal is cited, in accordance with accepted
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Frontiers in Animal Science | www.frontiersin.org 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.