ICES Journal of Marine Science, 60: 846–859. 2003
doi:10.1016/S1054–3139(03)00067-5
Acoustic Doppler current profiler observations
of herring movement
Len Zedel, Tor Knutsen, and Ranjan Patro
Zedel, L., Knutsen, T., and Patro, R. 2003. Acoustic Doppler current profiler observations
of herring movement. – ICES Journal of Marine Science, 60: 846–859.
Observations were made of over-wintering (December 1997) and migrating (January 1998)
Norwegian, spring-spawning herring (Clupea harengus) using a moored 307 kHz acoustic
Doppler current profiler (ADCP). The location of herring in ADCP data is identified by
regions of volume-backscatter strength greater than 60 dB re 1 m 1. The presence of
herring was verified using net trawls and 38 kHz, EK500 data. While the ADCP cannot
make speed measurements of individual fish, the system does provide a measure of the
swimming speed and direction of large herring schools. Herring were observed to move
both horizontally and vertically: horizontal speeds were from 0 to 50 cm s 1. Higher speeds
were observed during daylight hours for both deployments with somewhat increased
activity at both dawn and dusk. At night-time, over-wintering herring demonstrated no welldefined velocity.
Ó 2003 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.
Keywords: herring, ADCP, migration, swimming speed, swimming direction.
Received 18 October 2001; accepted 4 June 2002.
L. Zedel, and R. Patro: Department of Physics and Physical Oceanography, Memorial
University of Newfoundland, St. John’s, Newfoundland, Canada A1B 3X7; e-mail: ranjan@
physics.mun.ca. T. Kuntsen: Institute for Marine Research, Bergen, Norway; e-mail: tor@
imr.no. Correspondence to L. Zedel: e-mail: [email protected].
Introduction
Doppler sonar provides some attractive possibilities for application to the measurements of fish movement and activity. These systems should be able to detect both the
location of targets and determine their speed. An additional
important consideration is that they also have the potential
to provide information on fish movement in the context of
current velocity. If a practical system could be realized it
would have great potential for research into fish behavior as
well as for contributing to fisheries management.
There have been sporadic experiments at applying Doppler-sonar technology to the problem of measuring fish
movement that go back to the work of Bainbridge (1958) as
referenced in Holliday (1974). A fundamental problem that
surfaces in all of the earlier work is the trade-off between
spatial resolution and Doppler-speed resolution. In practice
it is impossible to achieve high accuracies in both the speed
and spatial domain at the same time. As a result, Doppler
sonar has only been applied in special circumstances where
the measurement of one of these two components can be
relaxed in some way.
Holliday (1974) reports on a 30 kHz sonar used to measure the collective motions of a fish school at distances of
1054–3139/03/080846þ14 $30.00
up to 1200 m. Using a 0.5 s duration pulse, speed measurements with a resolution of 0.01 m s 1 could be achieved.
The long pulse, however, provided no spatial-sampling
abilities (a 0.5 s pulse averages over about 375 m in range).
The poor spatial-sampling abilities were accepted in the
specific application where the distribution of velocities
associated with fish movements within a large school were
measured. The fish school was localized independently
with an 11 kHz sonar. Analysis of this data allowed the extraction of tail-beat frequencies as well as fish-school speed
relative to the water.
A domain of application in which the spatial-sampling
constraints are somewhat relaxed occurs in monitoring the
movement of fish in rivers. Pincock and Easton (1978)
identify the Doppler signature in the frequency domain as
a means of discriminating fish from various false targets. In
addition, they discuss what operating frequencies would be
suitable for either a continuous-wave Doppler system or
pulsed system: they conclude that for a pulsed sonar system
an operating frequency of the order 2.5 MHz would be required to achieve suitable speed and range resolution.
Some improvement in performance can be achieved by
adjusting the signal-analysis approach; Hendershot and
Acker (1984) describe a system where correlation between
Ó 2003 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.
Acoustic Doppler current profiler observations of herring movement
a transmit-pulse template and the received-backscatter signal are used to localize fish. Another technique is to avoid
undertaking complete spectral analysis of the backscattered
signal and only estimate the mean Doppler shift using the
autocorrelation technique described in Miller and Rochwarger (1972) as discussed by Waite and Belcher (1985).
This algorithm is used extensively in incoherent or narrowband, Doppler current profilers and provides for enhanced
range resolution.
Substantially greater speed and spatial resolution can be
achieved by using coherent or coded-signal processing used
in modern current-profiling systems as discussed in detail in
a later section. Doppler current-profiling systems are not
optimized to detect fish but there have been reports of current data being biased by fish presence (Freitag et al., 1992;
Wilson and Firing, 1992): the occurrence of such bias
clearly demonstrates that these instruments are capable of
measuring fish velocities. Demer et al. (2000) report on an
application of a ship-mounted, current-profiling system to
monitor the movements of a fish school. Using an approach
very similar to that used by Holliday (1974) they determined
details of the fish-school movement relative to the water.
They note many of the limitations of the current-profiling
system for this work but their results demonstrate the potential of the technique when the limitations are taken into
account.
In this paper we provide some background on the signal
processing required in Doppler, current-profiling systems.
We present observations of Norwegian herring made with
a self-contained instrument (an RDI Workhorse acoustic
Doppler current profiler (ADCP)). This instrument can only
sample at a fixed point and so relies on fish to transit its
location as in riverine applications of the technique and,
because it is self-contained, the data rate is somewhat limited. The advantage of the moored instrument is that it
eliminates in large part the need to correct for platform
motion and there would be no technical reason why the
instrument could not be deployed for several months. As
a result, such an instrument could be left to monitor fish
movements as a sentinel for an extended period of time in
situations where fish are known to aggregate in a restricted
area. The data presented provide an example of the capabilities of such instruments under circumstances when fish
densities are adequate to meet the sampling requirements of
the current-profiling ADCP geometry.
Doppler-sonar operation
The Doppler-sonar system tested in this trial is the RD
Instruments, 307 kHz Workhorse system. This unit operates
using the broad-band processing technique and is packaged
as a self-contained, internally recording instrument.
The broad-band system is a variation of the fully coherent
sonar, as described by Lhermitte and Serafin (1984), where
backscatter from successive, independent acoustic pulses is
analyzed. In broad-band sonars, a single pair of acoustic
847
pulses are transmitted and the combined backscatter is recorded. This signal is analyzed to determine the phase
change that has occurred between backscatter of the two
pulses from a given range. The phase change results from the
movement of the scattering particles and by knowing the
time between the two pulses the speed can be inferred. These
systems provide improved velocity accuracy over so-called
narrow-band Doppler sonars where the actual frequency
content of backscattered sound is used to estimate speed
(Brumley et al., 1991).
An important consideration in coherent and broad-band
systems is the occurrence of an ambiguity velocity. If scatterers move more than a quarter wavelength between the
passage of the two acoustic pulses, then the speed cannot be
resolved accurately; the ambiguity velocity is given by:
DV ¼ C=ð4ftÞ;
ð1Þ
where C is the speed of sound in water, f is the sonaroperating frequency and t is the time ‘‘lag’’ between the two
pulses. The value of t is usually determined by choosing
a reasonable ambiguity velocity for a given application.
There is also a range ambiguity with broad-band sonars because at any time, backscatter from the two pulses are being
received simultaneously. The range ambiguity effect degrades the data quality, as discussed by Brumley et al., 1991,
but the coherent sonar still provides accuracy advantages
over conventional Doppler sonar when considering volume
backscatter from which velocity profiles are derived. The
range-ambiguity effect will lead to data contamination close
to strong, discrete targets such as fish but the backscatter
from the fish target itself will still provide good velocity
estimates (see Zedel and Knutsen, 2000). The accuracy
of broad-band sonar is given by (Brumley et al., 1991)
1=2
rv ffi
ð1=q2 1Þ C
pffiffiffi
4 2ftp2 M1=2
a
ð2Þ
where q is the signal correlation coefficient and Ma is the
number of independent samples realized per averaging interval. An important component of Equation (2) is that accuracy is independent of the length of the transmitted
pulses. This characteristic is the important distinguishing
factor between narrow-band Doppler sonar where shortpulse lengths lead to poor velocity accuracy (Pinkel, 1979).
In Equation (2), aside from the set operating parameters,
the accuracy is limited by the correlation coefficient q. For
discrete targets the correlation can approach 1, but for volume scattering it is typically 0.5 because of interference
between the two transmitted pulses (Brumley et al., 1991;
Zedel and Knutsen, 2000). The correlation in returns between the successive pulses decreases with time and so
places a limit on the maximum-usable lag. For 307 kHz
systems this limit is about 20 ms as discussed by Brumley
et al. (1991). The correlation coefficient can be used to
discriminate the quality of data and a typical threshold
value is 0.25: 64 counts as specified by RD Instruments
848
L. Zedel et al.
documentation, RD Instruments (1999). No matter what
data-processing mode is used, the Doppler-profiling systems can only measure speeds radial to the acoustic
transducer. In a practical sense what this means is that
a particular transducer will measure the component of
velocity resolved along its beam axis: well-defined beams
are needed to get well-defined velocity components (see
discussion by Demer et al., 2000). However, if threedimensional velocity is of interest, it is necessary to use
multiple beams with different orientations to measure multiple components of velocity. The ADCP overcomes this
limitation by employing (typically) four beams oriented at
0, 90, 180 and 270 in the horizontal plane, and directed
70 below the horizontal. The four beams do not make
measurements at a single point but, if it can be assumed that
the flow field is uniform over the sample area, then the four
component measurements can be inverted to estimate the
flow velocity. Theriault (1986b) provides a detailed analysis of this sampling problem.
The RD Instruments 307 kHz WorkHorse ADCP used in
this study can profile velocities over a 100–150-m depth
range. Each of the beams has a 3 dB beam width of 2.2
(average side lobe levels are 41 dB relative to the main
lobe). This system is not a general-purpose sonar system
but rather a purpose-built, current-profiling unit and as such
it gives the user limited control over system operation. The
parameters that can be adjusted include speed sensitivity
(and consequently the ambiguity velocity), profile-bin size
and sample rate. The system has an operating bandwidth of
75 kHz which would suggest a range resolution of about
C/(75 000)/2 ¼ 1 cm (where C ¼ 1475 m s1 is the speed
of sound in water). Unfortunately, this bandwidth is only
realized for the code transmissions: the range resolution of
sample bins is limited to 20 cm in depth. In addition there is
a limit of 128 sample bins so that larger bins are required to
achieve greater maximum sample ranges. Speeds are measured along the four beams and these values are combined
by firmware inside the instrument to produce velocity profiles (corrected to an earth-coordinate system) and these are
provided as output to the user.
The ADCP also acquires backscatter-amplitude data
from the four beams and accumulates these into depth bins
as is done for the processed velocity data (bin sizes 20 cm
and larger). The backscatter data generated by the ADCP is
identical to the data that would be recorded using an echosounder and represents the range evolution of backscatter
along the acoustic beam. An important distinction is that
the ADCP generates a separate record for each of the four
diverging beams.
Herring in Ofoten
The ability of the ADCP to measure fish speeds required
a location where the fish behavior would best match its
operating constraints. In particular, in order for componentvelocity estimates from the diverging beams to be
combined to produce a fish-velocity estimate, it was essential that the fish be organized into schools that spanned the
distance between the diverging beams. In addition a good
understanding of the particular fish stock being observed
was desirable. Given these considerations, the herring that
over-winter in the Ofoten area of northern Norway provided an ideal study opportunity. As an example of the
concentrations of herring that occur in this area, Figure 1
shows 30 min of output from an EK500 echosounder observed on January 28, 1998: data from this area are discussed in greater detail later in the paper. A great deal of
information on the behavior of over-wintering Norwegian
spring-spawning herring is available (Vabø, 1999) offering
a framework within which the observations of herring motions could be gauged. The over-wintering herring stock is
also restricted to a limited geographical area that is shielded
from adverse-weather conditions and the admixture of other
species is insignificant (Huse and Korneliussen, 1995).
Large migrations between feeding grounds, over-wintering and spawning areas are common for herring. The Norwegian spring-spawning herring appear north of Iceland in
the summer months and they usually feed in the Norwegian
Sea until the end of July. It is likely that herring begin their
winter migration when the abundance of their primary-prey
organisms, the copepod Calanus finmarchicus, reduces significantly some time in autumn (Vabø, 1999). They approach
the Norwegian coast in September and enter the over-wintering areas in both the Ofotfjord and Tysfjord around the
end of October. The wintering season ends between the end
of December and the middle of January when the southwardspawning migration is initiated.
Throughout the over-wintering and migration periods, the
herring undertake a diurnal vertical migration, being close to
the surface at night and at greater depth during the day. The
vertical movement serves to conserve energy (Huse and
Ona, 1996), provides the fish with an opportunity to refill
their swim bladders (Blaxter and Batty, 1984), minimizes
predation risk (Huse and Ona, 1996), and also maintains
contact with possible navigational cues at the surface (Huse
and Korneliussen, 1995).
While over-wintering, if daytime illumination is insufficient for schooling, herring either swim slowly with
a significant upward tilt or alternate between swimming
upward and gliding down (Huse and Ona, 1996). While
schooling, the herring maintain relatively constant speed,
heading and individual distances (Blaxter and Hunter,
1982).
Experimental procedure
The 307 kHz Workhorse ADCP can provide profiles over
a depth range of up to 150 m. However, in Vestfjord herring can be located over a substantially greater depth range.
Thus a method of positioning the instrument in order to
allow herring schools to fall within the ADCP’s sampling
range was required. For this purpose, the instrument was
Acoustic Doppler current profiler observations of herring movement
849
Figure 1. An example of the echograms obtained by the Simrad EK500 at 38 kHz showing typical registrations of herring schools in
Vestfjord on 28 January 1998. These data were collected between 13:52 and 14:22 (CET) and correspond to data made during deployment
Dep9804. The numbers in the upper-right corner of each rectangle are the nautical-area-scattering coefficients (sA, m2 nmi 2) for that
rectangle. Volume-backscattering strength (Sv in dB re m 1) is indicated on the color bar at the right side of the display, times (here in
UTC) are shown along the bottom and depth in meters is indicated down the left edge. The herring school forms the strong backscatter
seen between 175- and 250-m depth.
deployed in a configuration suspended from the surface as
shown schematically in Figure 2. The length of the mooring
cable to the surface was adjusted so that the instrument
would be located at an appropriate depth.
Deployments were made during December 1997 and
January 1998 from the vessel R/V ‘‘Johan Hjort’’ during
cruises 1997216 and 1998201. Figure 3 identifies the times
and locations of the deployments. In each case, accumulations of herring were identified using the echosounder
systems on the ‘‘Johan Hjort’’ and the mooring geometry
was adjusted to position the ADCP above the herring. At the
time of the January cruise, the herring had started to migrate
southward and abundance had significantly decreased in
Ofotfjord and Tysfjord compared to December 1997. As
a result, observations during January 1998 were made in the
southeastern part of the Vestfjord.
Between ADCP deployments, trawl samples were undertaken to measure fish size distribution and weight. Abundance and vertical distribution of herring were monitored
continuously using the Simrad EK500 38 kHz echosounder
system. Profiles of water-density structure were also acquired using the Seabird model 911 CTD.
The ADCP configuration was essentially the same during
each deployment as indicated in Table 1. In particular, with
75 bins of 2-m length a total recorded-profile range of
150 m was possible. It is important to note that by choosing
a bin length of 2 m, the depth resolution of the instrument
is restricted to 2 m consistent with the transmit-pulse length
of 2.36 m (Table 1). One important distinction between the
1997 deployments and those of 1998 is that in the latter
year data were recorded for individual acoustic beams. This
was intended to allow the investigation of discrete target
movements but, unfortunately, depth averaging associated
with the comparatively large (2 m) depth bins made target
identification all but impossible.
Data processing
Figure 2. The geometry of an ADCP deployment.
The Workhorse ADCP provides profiles of velocity, backscatter intensity and additional data-quality indicators
arranged in discrete, depth-averaged bins. For precise
positioning of data with respect to the instrument it is necessary to take into account the exact instrument orientation. We do not make any such correction in the present
850
L. Zedel et al.
Figure 3. A map of the deployment area on the Norwegian coast. d indicates the location of deployment Dep9704 (03:00–12:30 CET
December 7, 1997) and r indicates the location of deployment Dep9804 (11:47–17:07 CET January 28, 1998).
analysis so that our observations are made with respect to
the vertical axis of the instrument in fact. Our data records
show that this axis is as much as 5 from the vertical and
so the profiles may be slightly off in depth. Slight misorientations might appear at first to cause significant errors
in velocity estimation but the four-beam, ‘‘Janus’’ configuration provides velocity estimates that are effectively
corrected to first order for this rotation (Theriault, 1986a).
In this study we focus on backscatter data and the velocity
data as represented by speed and direction profiles. In this
section we describe the data processing that we have used
for each of these data components and also comment on
how the data types are used in combination.
recover volume-backscattering strength (Sv) from the recorded data.
Backscatter data in the Workhorse ADCP is extracted
from the receiver circuit’s ‘‘Receive Signal Strength Indicator (RSSI)’’ output. This value is proportional to the
logarithm of the signal strength with a sensitivity (Kc) of
about 0.45 dB/LSB; the exact slope varies between different
receiver circuits. A calibration of this sensitivity can be
done with basic laboratory equipment: we no longer have
access to the instrument used for the present study and so
we must use the approximate value of 0.45 dB/LSB. Using
the recorded-backscatter signal, backscatter strength in dB
re 1 m 1 are estimated using the relation given by Deines
(1999),
Backscatter data
Sv ¼ c þ 10 log10 ðTx þ 273:16ÞR2
Backscatter data have been used to localize the presence of
fish and it is therefore an important component of the present study. Backscatter data recorded by the Workhorse
must be processed to convert observations into calibrated
results. We use an approach described by Deines (1999) to
Table 1. The ADCP configuration during the deployments Dep9704
(03:00–12:30 CET, 7 December 1997) at location 68 28.99N
17 24.89E, and Dep9804 (11:47–16:07 CET, 28 January 1998) at
location 67 57.069N 14 40.79E.
ADCP configuration
Number of bins
Bin size (m)
Transmit length (cm)
Ping interval (s)
Blank after transmit (cm)
Instrument depth (m)
Dep9704
Dep9804
75
2
237
5
176
50
75
2
236
10
176
150
LDBM PDBW þ 2aR þ Kc ðE Er Þ
ð3Þ
where c is a sonar-configuration scaling factor, (for the
Workhorse sentinel c ¼ 143:5 dB); this term includes the
system-source level, transducer directivity, transducer efficiency and the Boltzmann constant that is used in scaling
the thermal noise to an absolute level. Tx is the temperature
at the transducer in C, R is the (slant) range to the sample
bin in m, LLDM ¼ 10 log10 ðLÞ, L is the transmit-pulse
length in meters, PDBW ¼ 14 ¼ 10 log10 ðPÞ, P is the
transmit power in dB re 1 W, a is the sound-absorption
coefficient ¼ 0.0873 dB m 1 (at 4 C, 307 kHz), E is the
recorded-backscatter signal, and Er is the minimum (background) level recorded by the instrument. Calibration of
the sonar is achieved by comparing received levels to the
background thermal-noise level (represented by Er) and the
temperature scaling (Tx þ 273.16) adjusts for changes in
the thermal noise associated with temperature changes. Use
of the thermal noise as a calibration point is possible for
Acoustic Doppler current profiler observations of herring movement
851
higher-frequency sonar systems where there is little or no
naturally occurring sound. Values of Sv are computed for
each of the four ADCP beams separately and the maximum
of these four values was chosen as the representative value
for a given sample.
the purposes of averaging,
the resulting profile uncertainty
pffiffiffiffiffi
is reduced by 1= 20 ¼ 0:22 to give 1.3 cm s 1 in the present case.
Speed
The ADCP converts direct observations of speed along the
four acoustic beams into a single velocity profile referenced
to the earth through use of an internal compass. Direction
data suffers from the same spatial-sampling problems that
the speed estimates have and that data has been smoothed
using the same filters as used on the speed data. Direction
cannot be filtered directly, rather the data were converted
into northward- and eastward- velocity components, these
component values were filtered and then re-computed into
direction estimates.
Speed data were collected in this study using a beam radial,
ambiguity velocity of 170 cm s 1, 72.3 ms time lag between transmit codes and 2-m depth bins with no initial
averaging of profiles. This configuration can be converted
into a velocity uncertainty using Equation (2) by choosing
the appropriate parameter values: the correlation coefficient
for a broad-band ADCP is ideally 0.5, and the effective
number of independent samples is determined by the bandwidth limited range resolution,
Ma ¼
2Lb
¼ 432
C
Direction
Fish localization
ð4Þ
where L ¼ 2=cos 20 is the radial-bin length in meters,
b ¼ 150 000 Hz is the system bandwidth. Substituting these
values into Equation (2) gives a (radial) velocity uncertainty of 1 cm s 1. This single-beam uncertainty is converted into a resolved-velocity uncertainty using the
approach that gives Equation 27 in Theriault (1986a),
pffiffiffi
rvr ¼ 2rv =sinð20Þ
ð5Þ
where rv is the single-beam, radial-velocity uncertainty
(1 cm s 1) and the sin(20) accounts for the component of
horizontal velocity resolved. We find for the present configuration rvr ¼ 4.1 cm s 1. Brumley et al. (1991) note that
the accuracy is degraded by hardware limitations by a factor
of about 1.5 which, for the present case, gives an uncertainty of 6.2 cm s 1: this result is close to the uncertainty
predicted by RD Instruments deployment-preparation,
software-package ‘‘Plan’’ which predicts an uncertainty
of 5.9 cm s 1. This value represents an upper limit on the
single-ping accuracies that can be anticipated for the present application.
An additional factor that may be important in the present
context is the fact that these instruments do not make a point
measurement of velocity but rather include information
from the four diverging beams. At any depth d below the instrument information from points separated by 2d cos(20 )
in the horizontal are included in the measurement. It is
necessary to assume that the flow is homogeneous over
this interval in order to extract three-component, velocity
estimates. While temporal resolution is clearly lost by extensive averaging of the data, the assumption of flow homogeneity is somewhat more reasonable if a long averaging
time is considered. For the present analysis, a Butterworth
filter is used to smooth out variations that occur over intervals of 20 profiles: this corresponds to a cut-off frequency of 0.005 Hz for the 1997 data and 0.01 Hz for the
1998 data. Assuming that each profile is independent for
Normally Doppler-velocity profiles are based on background levels of volume-backscattering strength and no
distinction is made as to the presence or absence of targets.
The ideal signal is realized when there is little variation in
backscatter as a function of range. The quality of the velocity data does not provide an indication of fish presence.
As a result, the backscatter strength must be used to discriminate fish presence and that information can then be
used to interpret the velocity data. In the present study, we
have used a threshold-backscatter strength of 60 dB re
1 m 1 to identify the presence of fish based on inspection
of the data. At large ranges where the attenuation and
spherical-spreading terms in Equation (3) begin to dominate,
background (noise) levels are eventually amplified to the
point where they can exceed the threshold level. We have
restricted our analysis to a maximum range of 100 m to
avoid this difficulty.
Results
A total of 10 deployments were completed during the two
cruises being considered. Some of these deployments were
made to test the instrumentation or duplicate results more
clearly shown in other deployments while others were not
successful in locating fish. We will focus our discussion on
two of the deployments that demonstrate the abilities of
the ADCP system: deployment 4 during December 1997
(referred to here as Dep9704) demonstrates a transition
between the night-time and daytime behavior of overwintering fish, deployment 4 during January 1998 (referred
to here as Dep9804) demonstrates the transition between
night and day behavior for migrating fish.
Deployment Dep9704: December 7, 1997,
03:00–12:30 Central European Time
The deployment location (68 28.99N 17 24.89E) is indicated
by d in Figure 3 and with the ADCP positioned at a depth
852
L. Zedel et al.
Figure 4. (a) Temperature and (b) density profiles for deployment Dep9704. The profile was collected at 16:00 CET, December 7, 1997,
68 27.069N 17 18.069E.
of 50 m profile coverage over the depth interval from 50
to 150 m was achieved. Based on the time of year we
expect that the fish are over-wintering and not migrating
during these observations. The density and temperature
profiles collected for this deployment are shown in Figure
4. The profile reveals a complicated structure with three
distinct regions; a mixed layer of cold (5.2 C) fresh water
is found above 40 m, between 40 and 100 m there is
evidence of warmer (7.5 C), somewhat saltier water, and
below 100 m, there is relatively uniform water properties
at an intermediate temperature of ’6 C. Despite the
substantial temperature structure the density profile
(Figure 4b) does not show any abrupt pycnoclines.
The ADCP and corresponding 38 kHz EK500 data for
deployment Dep9704 are shown in Figure 5. The region of
ADCP-data coverage is indicated in the EK500 data by
lines at 50 and 150 m depth in Figure 5b. For the first four
hours of this deployment, the backscatter strength (Figure
5a) shows patches of backscatter at all depths with perhaps
increased bands of scattering at 70 and 90 m; the same
depths at which large temperature gradients are observed).
Beginning at 07:00, however, well-defined regions of scatterers become apparent at between 75 and 90 m depth. With
time, the scattering layer evolves into a single well-defined
layer which varies in depth reaching a minimum of 70 m
and a maximum of 130 m.
The EK500 data for Dep9704 (Figure 5b) show two welldefined bands of herring centered at 80- and 150-m depth
with evidence of fish at all depths between 50 and 150 m. At
around 09:00, the lower band appears to end but the bulk of
concentration that was initially at 80 m descends to about
150 m. The linking of these scattering layers with herring
was verified by trawl surveys undertaken at the same time as
the acoustic observations. A multi-sampler with three nets
mounted on the cod-end was used to take stratified samples
at 22:44 December 6 and 01:01 December 7, just prior to the
Doppler system deployment. The trawl catches identify
herring at depths of 45–58, 87–107 and 130–168 m. Size
distributions from these deployments are shown in Figure 6
for each of the three net depths.
Profiles of direction are shown in Figure 5c. There are
three bands of flow indicated: above 80 m, motion is
toward 50 true, at depths between 80 and 100 m motion
appears toward 200 to 250 and below 110 m, the motion is
toward 0 . The velocity structure is broken up after 10:00
by motions associated with the well-defined scattering
band.
Profiles of observed speed are shown in Figure 5e. Most
of the record shows a background low speed of less than
10 cm s 1 with somewhat distributed observations of
higher speed events. There are two regions where ‘‘higher
than background’’ speeds are seen consistently; between
06:00 and 08:00 at 90–100-m depth, and also after 10:00 at
depths that follow the region of enhanced scattering.
The occurrence of fish throughout the region sampled
by the ADCP—indicated by the black horizontal lines
in Figure 5b—makes it difficult to distinguish fish speeds
from water speeds. The observed speeds can be considered
as an upper limit on water speeds, assuming that the motion of herring would only lead to higher speeds. Using
this assumption, current speeds are generally very low
<10 cm s 1 and this is certainly consistent with the enclosed nature of the observation area with tidal-model
predictions for this location predicting current speeds less
than 1 cm s 1 (Moe et al., 2002). There is some structure
indicated in the current direction and it would appear to be
associated with the temperature intrusion that occurs between depths of 40 and 100 m.
Acoustic Doppler current profiler observations of herring movement
853
Figure 5. ADCP data observed during deployment Dep9704: (a) the backscatter strength in dB re m 1, (b) the nautical-area-scattering
strength (SA, dB re m2 nmi 2) from the EK500 system (lines are drawn at 50 and 150 m to identify the range of ADCP data), (c) the
direction of movement in degrees true, (d) the direction of movement in degree true of targets identified as fish, (e) the speed of movement
in cm s 1and (f ) the speed of movement in cm s 1 of targets identified as fish.
The EK500 data (Figure 5b) show that herring are present
over the entire range of the ADCP measurements until about
09:00 (Central European Time (CET)). Over this period of
time, strong backscatter is in fact seen at all depths in the
ADCP data (Figure 5a). After 09:00 CET, the EK500 data
indicates that the herring move into a thinner layer at about
100-m depth and the ADCP data mirror this showing a more
densely concentrated scattering layer that has significant
Figure 6. The length distribution of herring as determined from two trawl sets made at 22:44 CET December 6, 1997 and 01:01 CET
December 7, 1997 and corresponding to Dep9704: data from (a) 45–58 m, (b) 87–107 m and (c) 130–168 m.
854
L. Zedel et al.
vertical movements especially after 10:00 CET. In this case,
the motion of the herring can be isolated based on backscatter strength relative to background levels. Identifying all
targets for which Sv > 60 dB re 1 m 1 as being caused by
herring, Figure 5d, f shows the swimming direction and
speed of the herring while excluding all other data. In Figure
5d, f most of the targets identified as fish drift with the prevailing current until about 10:00 when their direction of
motion deviates substantially from that of the local currents
and changes constantly. Also at this time, the average speed
of detected targets increases to about 20 cm s 1 (Figure 5f ).
Deployment Dep9804: January 28, 1998,
12:00–16:07 CET
Deployment Dep9804 occurred at a position in the southeastern part of Vestfjord (67 57.069N 14 40.79E) as indicated by r in Figure 3. The water-property profiles
(shown in Figure 7) are similar to those observed during
deployment Dep9704 in December: there is a mixed layer
above 30 m and a strong thermocline at 75 m separating
4 C water from that of 7.5 C. At 150 m there is a temperature inversion going from 7.5 to 6.9 C. The density
structure shows the well-defined mixed layer and there is
a pycnocline at 75 m corresponding to the thermocline.
ADCP and EK500 data for this deployment are shown in
Figure 8: the ADCP was positioned at 150-m depth and
provided profile information over the depth interval 150–
250 m. Through most of the time the ADCP-backscatter
strength identifies a well-defined band (Figure 8a) that
corresponds with a similar structure in the synoptic 38 kHz
EK500 data (Figure 8b). This band of scatterers was verified
as being caused by herring through trawl catches made at
this time: the herring’s size distribution from trawl sets are
shown in Figure 9. Exact agreement between the ADCP
and EK500 data cannot be expected because the ADCP remained at a fixed location while the EK500 data were
collected while undertaking a survey in a region within
500 m east and west and 8 km north and south of the ADCP.
The herring rise from a depth of 240 m up to 170 m through
the deployment and then disperse or move above the
instrument at the end. The scatterer band becomes somewhat less well defined between 13:00 and 14:30.
ADCP-direction data for entire profiles are shown in
Figure 8c. The motion of the herring alone can be isolated
by identifying all targets for which Sv > 60 dB re 1 m 1;
based on this criterion, Figure 8d isolates the swimming
direction of the herring. An interpretation of the background current structure can now be made by considering
Figure 8c, d with reference to Figure 7. Prior to 14:30, the
background current is northwestward (300 true) above
175 m and northeastward (75 true) below 75 m. After
14:30, the entire water profile shifts to a direction of 275
true. The occurrence of velocity shear at 175 m is consistent
with the temperature inversion at that depth. After 14:30,
the uniform water motion may suggest that the water structure had changed but we have no CTD data to support
that speculation. Current speeds throughout appear to be
20 cm s 1 or less while tidal-model results for this area and
time indicate currents that are less than 10 cm s 1 (Moe
et al., 2002).
Speed profiles for this deployment (Figure 8e) show very
low speeds except in the area marked by the presence of the
herring. It is, in fact, possible to identify the herring school
by the anomaly in the speed profile. Using the same approach to isolate the speed of the herring as that used to
Figure 7. (a) Temperature and (b) density profiles for deployment Dep9804. These data were collected at 23:00 CET, 67 58.769N
14 48.379E.
Acoustic Doppler current profiler observations of herring movement
855
Figure 8. ADCP data observed during deployment Dep9804: (a) the backscattering strength in dB re m 1, (b) the nautical-area-scattering
strength (SA, dB re m2 nmi 2) from the EK500 system (only for depths corresponding to ADCP data), (c) the direction of movement in
degrees true, (d) the direction of movement in degrees true of targets identified as fish, (e) the speed of movement in cm s 1 and (f ) the
speed of movement in cm s 1 of targets identified as fish.
isolate their swimming direction, Figure 8f displays the
herring-swimming speed alone.
The motion of the herring school as a whole can be
extracted by averaging the direction and speed profiles
(Figure 8d, f ), the results of this extraction are shown in
Figure 10 as both time series of speed and direction
(Figure 10a, c), and also as histograms (Figure 10b, d). The
fish were moving with a speed of 20–30 cm s 1 in
a direction of 150 to 275 during the time interval of 12:00–
13:00 (Figure 10a). From 13:00 to 14:30, the school is
somewhat more dispersed (Figure 8a) and the speeds vary
considerably between 15 and 45 cm s 1 while the direction
becomes more uniform at around 200 . At 14:30, the speed
abruptly drops as the school becomes more consolidated
and this drop in speed coincides with a sudden change in
swimming direction (Figure 10c). The histogram of swimming direction for the entire Dep9804 deployment shows
that the distribution has a narrow peak at a direction of 200
(Figure 10d). The speeds range from a low of about
10 cm s 1 to a high of 45 cm s 1.
Discussion
Figure 9. The length distribution of herring as determined from
trawl sets made at 20:49 CET, January 26 at 200 m corresponding
to Dep9804.
Our observations demonstrate a clear signal from herring
schools in ADCP-backscatter data and velocity estimates
from these regions are distinct from those of the greater
water column (see Figs. 5 and 8). Doppler-sonar systems
provide a backscatter-intensity-weighted estimate of velocity (see the discussions of Ahn and Park, 1991). This means
that when scattering from fish is dominant the system
measures fish speed. When there are no fish present adequate
856
L. Zedel et al.
Figure 10. The speed and direction distribution of fish schools identified in Dep9804: (a) mean speed averaged over depth, (b) distribution
of observed speeds, (c) direction of motion averaged over depth and (d) distribution of observed directions.
backscatter is normally available from zooplankton (see, for
example, Flagg and Smith, 1989) and even from the temperature microstructure (Seim et al., 1995) so that the water
speed can be measured. Backscatter strength, then, provides
a means to discriminate velocity signals that might be
coming via fish targets from those coming from the water
per se. Here we have used a backscatter-strength threshold of Sv > 60 dB re 1 m 1 to identify herring. In the event
the backscatter from other sources approaches the level
we expect from herring, this will result in biased velocities.
However, background-volume backscatter has Sv < 70 dB
re 1 m 1 (see Figure 8) in the present data. There is,
therefore, a substantial margin in scattering strength between
that of herring as compared to that of the water alone. In the
present situation it is quite likely that water speeds are at
times biased by the presence of fish but it is unlikely that fish
speeds are biased.
With four separate acoustic beams the ADCP provides
four independent measurements of volume backscatter. For
the present analysis we have reduced the data by choosing
the highest of the four values. This approach was taken
because averaging in the logarithmic domain had the effect
of smearing the boundaries of the fish schools so that the
selection of a suitable backscatter threshold then became
less obvious. This smearing of boundaries does, however,
suggest differences in backscatter observed between the
four beams that we have not explored in the present paper
but intend to pursue in future studies.
If we accept the backscattering cross section as a means
of distinguishing the fish signal in the present data we can
generate statistics of the fish motion and direction. One of
the basic parameters we have considered is the distribution
of speeds (Figure 10). When evaluating these distributions
it is important to recall that the uncertainty in individual
velocity estimates is 1.3 cm s 1. The variance seen in
Figure 10b is therefore an indication of the range of velocities measured and not uncertainty in the measurement
itself.
One of the fundamental characteristics of Doppler-sonar
systems is the use of three or four separate acoustic beams
required to resolve various components of velocity. In order
to recover three-dimensional velocities it is necessary to
assume that the velocity measurements are homogeneous
over the combined footprint of the acoustic beam. For the
Workhorse ADCP being investigated here, at a range of
100 m this footprint is approximately 70 m across. As
a result, it is impossible to measure the three-dimensional
velocity of a single fish and only the velocity of large fish
schools can be determined. If individual fish could be identified in the acoustic beams some statistical description of
their motions could be extracted. In the present data we
explored this approach but found that with the 2-m depth
bins that were employed it was not possible to localize
individual (fish) targets.
A severe limitation with the present data set is that we
have no direct reference with which to compare the ADCPvelocity estimates. In fact, the direct-velocity measurements
of the ADCP are hard to reproduce with other measurement
techniques. Estimates of swimming speed can be inferred by
tracking the movement of entire fish schools if the outline
of the school can be delineated using repeated passes of
a survey vessel. Acoustic tags and tag-recovery schemes can
provide good estimates of mean swimming speeds but again
these are not instantaneous measurements like those of the
Acoustic Doppler current profiler observations of herring movement
ADCP. Split-beam sonar systems can track individual
swimming fish but this is not generally possible from a ship
at sea. In the present case we take advantage of the large
body of knowledge on over-wintering-herring behavior to
evaluate the consistency of the ADCP observations.
There have been many reports on the vertical-swimming
behavior of over-wintering herring (Huse et al., 1994; Huse
and Ona, 1996) because this behavior affects the yearly
abundance assessment (Røttingen et al., 1994; Vabø, 1999).
Several factors may affect the fish behavior, particularly
predator avoidance and the need to conserve energy during
over-wintering (Huse and Ona, 1996; Huse and Korneliussen, 2000). In this setting the diurnal change in illumination
is an important factor with regard to herring behavior as
many of its predators like cod, saithe and killer whales are
predominantly visual feeders. During this study, herring
were observed moving to deep water at sunrise (Figure 5)
and ascending at dusk (Figure 8). It is believed that this
migration allows the herring to conserve energy through an
increase in swim-bladder volume at shallow depths while
affording some protection from predators in the dark
(Blaxter and Batty, 1990). It is, however, important to note
that a proportion of the herring remain in deeper water
during the night as has been documented by Huse and
Korneliussen (2000).
Our observations from the inner part of Ofotfjord made
on 7 December 1997 represent a pure over-wintering situation. During the night the herring are seen near the surface at less than 150 m and the absence of any well-defined
swimming speed at this time suggests that the fish are more
or less drifting with the water current or at least not moving
in any systematic direction (Figure 5c). There is some indication of a second, deeper, herring layer, note the EK500
data shown in Figure 5b, but these two layers appear to
merge after 09:00. No prominent speed changes are seen
until about 10:00 when, with the arrival of dawn, the
swimming speed increases from 10 cm s 1 to a maximum
of 30 cm s 1 (Figure 5f ). On average, the herring migrate
to deeper water from about 08:00 onwards but rapid
changes in the vertical distribution suggest that there may
be some predator-avoidance activity with increasing
daylight.
The low (average) speeds of movement of the herring
during December suggest that the herring might be in a state
of hibernation. While in this state, a certain type of swimming behavior consisting of upward swimming and gliding
has been documented and is possibly an adaptation to decreased buoyancy at depth (Huse and Ona, 1996). In our
velocity data, this behavior would lead most likely to an
increase in velocity variance and perhaps this behavior
accounts for the frequent speed anomalies seen in the first
4 h of Figure 5f. Huse and Ona (1996) report that at intermediate depths herring will take on this swim-and-glide
behavior during low light levels. At higher light levels, they
report herring at 62–107-m depth having a near-horizontal
tilt angle and a mean swimming speed of 33–27 cm s 1.
857
Because of the range limitations of the ADCP, acoustic
recordings deeper than 150 m could not be obtained for
Dep9704 where the swim–glide behavior might have been
expected during the day. The EK500 data (Figure 5b) do
confirm that most of the herring population in this region
is located at a band around 60-m depth and a second,
deeper layer at around 150 m during the night. Huse and
Korneliussen (2000) also report occurrences of shallow
and deep layers of over-wintering herring in this area.
Based on the time of year and location, our January
deployments represent a situation where most likely the
herring have initiated their southward, spawning migration.
For 28 January (Dep9804) the sun is now above the horizon
for about 5 h, from approximately 09:40–14:50. Hence, the
herring are active for a longer period than during the December deployments. Prior to about 13:00 the herring
school has speeds of around 15–30 cm s 1 with a welldefined direction of movement between 200 and 300 , suggesting that they are heading south or south-west and
leaving the Vestfjord region. In daytime observations the
recorded speed was as high as 45 cm s 1 (Figure 10a).
Given the observed fish length of 30–35 cm (Figure 9) this
speed is consistent with a swimming speed of 1 BL s 1 as
reported by Huse and Ona (1996). Around sunset and
thereafter the herring migrate toward the surface but now
have a swimming direction that varies with time and a
somewhat lower absolute-swimming speed. The fish disappear from the ADCP recordings at around 15:45 as, in
all probability, they migrate above the instrument and disperse. The EK500 data from this time confirm that the
herring that were observed in deep waters below 200-m
depth during the daytime, migrate to the surface toward the
end of the deployment Dep9804 (Figure 8a).
Summary and conclusions
In this paper we have presented observations of herringschool motions made using a moored, 307 kHz, RDI
Workhorse ADCP. This deployment approach avoids interference from ship noise and can provide a time series when
fish are constrained to move in a well-defined geographic
area. Profiles were limited to a 100-m interval because of
the 307-kHz operating frequency of the system: a lower
frequency system would be able to achieve a greater range.
Velocity accuracy is calculated to be 1.3 cm s 1 in data
averaged over 100–200 s. Greater variation in observed
velocities appears to be representative of real variations in
fish-school movements. In finding these velocities it is
necessary to assume that school movement is homogeneous
over the footprint of the four acoustic beams used by the
ADCP and this is as much as 70 m in the present application. The requirement for this spatial homogeneity
restricts this approach only to large aggregations of fish.
Backscatter data recorded by the ADCP has been calibrated using the procedure described by Deines (1999). In
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L. Zedel et al.
this approach, measured receiver sensitivities are combined
with factory-specified, source-level settings and observed
background-noise levels to arrive at volume-backscatter
strength. This procedure made the backscatter values from
the four independent beams comparable. We do not have an
independent check on the resulting calibration but the observed-backscatter levels from the schools of herring are
consistent with the expected levels. A volume-backscattering strength of greater than 60 dB re 1 m 1 was used to
discriminate areas of herring in the ADCP data.
The behavior of schools of Norwegian, spring-spawning
herring were observed while over-wintering (November
1997) and during the spring migration (January 1998) and
have been compared to, and are found to be consistent with,
prior observations of this herring stock. Many of the fish
were seen to undertake a diurnal, vertical migration rising
close to the surface at night (i.e. to depths below 100 m)
and to prefer greater depths (200–300 m) during the day.
We must, however, stress that not all of the fish undertook
this migration. Well-defined schools were formed during
hours of daylight and dispersed at dusk. Swimming speeds
of no more than 20 cm s 1 during the over-wintering
period were less than the maximum of 45 cm s 1 observed
during the migration period. They were greater during the
day than at night. Some daytime observations were marked
by rapid changes in swimming speed and direction
suggesting, possibly, that these were attempts to evade
predators.
Acknowledgements
The following are thanked for their support of this work:
the EU through RTD-contract no. MAS3-CT95-0031
(BASS), the Norwegian Research Council through grant
no. 113809/122 and the Bergen Large-Scale Facility for
Marine Pelagic Food Chain Research. The support of Lucio
Calise through Convenzione IMC (IMC-Centro Marino
Internazionale, Loc. Sa Mardini, I-09072 Torregrande (OR),
Italy) Regione Autonoma Sardegna L.R. 2/94, art. 32
titolo 11.3.10/I, is gratefully acknowledged too. This is a
contribution to the Mare Cognitum Program at IMR.
Thanks are due to Kenneth G. Foote for the opportunity
to participate in herring-survey cruises and to Lee Gordon,
who made available the RD Instruments WorkHorse
Sentinel and FishMass 307 kHz systems for these experiments, and Mark Vogt of RD Instruments, who gave
technical assistance. The help of Bjørn Gjevik who
generated the tidal-model results for our field sites is much
appreciated also.
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