Journal of Oceanography
https://doi.org/10.1007/s10872-019-00507-8

ORIGINAL ARTICLE

Moon jellyfish aggregations observed by a scientific echo sounder
and an underwater video camera and their relation to internal waves
Takashi Mano1 · Xinyu Guo2

· Naoki Fujii3 · Naoki Yoshie2 · Eisuke Tsutsumi4 · Rui Saito5

Received: 10 September 2018 / Revised: 19 February 2019 / Accepted: 21 February 2019
© The Oceanographic Society of Japan and Springer Nature Singapore Pte Ltd. 2019

Abstract
We found a good linear relation between the mean volume backscattering strength measured using a scientific echo sounder
and the distribution density of moon jellyfish recorded simultaneously by an underwater video camera in small bays (Hokezu
Bay and Mikame Bay) in Japan. This relation held in all of the surveys carried out in recent years (2013–2016), although theregression coefficient varied slightly from year to year due to the existence of ambient organisms. Using this relation andhigh-resolution echo sounder scan surveys across the bay, we obtained the spatial structures of moon jellyfish aggregations
and categorized them into either patchy, layered, or wavy aggregations. We also examined possible physical mechanisms
responsible for the moon jellyfish aggregations. In particular, passive particle tracking simulations of several idealized internal
wave fields suggest that the formation of wavy aggregations is closely related to the presence of internal waves. However,internal waves alone cannot induce patchy and layered aggregations, indicating that particular biological processes (e.g.,swimming behavior) are also necessary for such aggregations to form.
Keywords Moon jellyfish · Aggregations · Internal waves · Echo sounder · Underwater video camera · Passive particle
tracking

1 Introduction
Jellyfish blooms often occur in coastal areas around theworld, and can be categorized into two types: true and apparent blooms (Graham et al. 2001). A true bloom is a rapid
increase in the jellyfish population, while an apparent bloom
is accumulation toward a high distribution density without
Electronic supplementary material The online version of thisarticle (https​://doi.org/10.1007/s1087​2-019-00507​-8) contains
supplementary material, which is available to authorized users.
* Xinyu Guo
guoxinyu@sci.ehime‑u.ac.jp
1

Graduate School of Science and Engineering, Ehime
University, Matsuyama, Japan

2

Center for Marine Environmental Studies, Ehime University,
2‑5 Bunkyo‑cho, Matsuyama 790‑8577, Japan

3

Faculty of Agriculture, Saga University, Saga, Japan

4

Research Institute for Applied Mechanics, Kyushu
University, Kasuga, Japan

5

Seikai National Fisheries Research Institute, Japan Fisheries
Research and Education Agency, Nagasaki, Japan

population growth. The apparent blooms of jellyfish form
dense aggregations over a short period of time, typicallyone to several days. Such blooms are known to affect human
life; for example, they can clog seawater intakes for power
plants, break fishing nets, and cause the closure of swimming beaches due to the danger posed by jellyfish stings(e.g., Purcell et al. 2007). There are, however, both negative and positive impacts of jellyfish on marine ecosystems.
Large-scale jellyfish aggregations exert high predation pressure on zooplankton and fish larvae (e.g., Uye et al. 2003;
Uye 2011), but the jellyfish are consumed by predators suchas fish, sea turtles, birds, among many others (Hays et al.
2018).
Jellyfish abundance has been increasing in recent years
around the world (Brotz et al. 2012). This fact suggests thatthe frequency of occurrence and the scale of jellyfish aggregations are increasing, and it implies that the accidents anddamage caused by jellyfish and their impact on marine ecosystems are also increasing. If the formation mechanisms forjellyfish aggregations can be elucidated, it should be possible to make short-term predictions regarding the emergenceof severe jellyfish aggregations and to determine methods ofsuppressing the damage caused by jellyfish. Although many

13

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T. Mano et al.

studies have attempted to identify the cause of the increasein jellyfish abundance (e.g., Purcell et al. 2007), the formation mechanisms of dense aggregations of jellyfish are notwell known.
Previous studies have gradually revealed the distribution patterns of jellyfish aggregations. Jellyfishes aggregate
in a layered structure around a thermocline or halocline(e.g., Graham et al. 2001; Alvarez Colombo et al. 2009;
Suzuki et al. 2016) or in a patchy structure tens to hundredsof meters in size that has a spherical or elongated shape(Zavodnik 1987; Purcell et al. 2000; Graham et al. 2001;
Magome et al. 2007). In those studies, two-dimensional distributions of the jellyfish in the vertical layered and horizontal patchy structures were examined. Recently, using laser
imaging detection and ranging (LIDAR) equipment on an
airplane, Churnside et al. (2016) observed patchy aggregations of moon jellyfish and revealed that some patchy aggregations had “hollows” (regions with background concentrations of jellyfish). However, the mechanisms responsible forsuch jellyfish aggregations are rather unclear. Some studies
have ascribed the patchy structure to surface convergencearound ocean fronts (Graham et al. 2001; Magome et al.
2007). Other studies have suggested that the aggregations
are formed by interactions between the currents caused byLangmuir circulation and the active swimming behaviorof the jellyfish (e.g., Hamner and Schneider 1986; Larson
1992). Therefore, both physical processes and biologicalprocesses are important in the formation of jellyfish aggregations. The present lack of information on jellyfish aggregation structure impedes research into the mechanisms of
aggregation formation. Detailed information on jellyfish
aggregations, especially on their three-dimensional structures, is essential because such information should help us
to identify the factors responsible for aggregation formation.
Jellyfish abundance and distributions have conventionally been observed through net sampling (e.g., Hosia andBamstedt 2007), visual quantification from airplanes oraerial photography (Purcell et al. 2000; Uye et al. 2003;
Houghton et al. 2006), and video recording using an underwater video camera (Graham et al. 2003b). Since thesemethods are performed to achieve a general understandingof the jellyfish distribution over a wide area, their spatial
resolutions are usually insufficient to capture patchy aggregations. Acoustic methods using an echo sounder appear
to be more appropriate for observing small-scale structures
such as aggregations. Acoustic methods for jellyfish quantification have been investigated in both laboratory experiments(e.g., Hirose et al. 2009) and field observations (Mutlu 1996;
Toyokawa et al. 1997; Brierley et al. 2001, 2005; Alvarez
Colombo et al. 2003, 2009; Yoon et al. 2018). Those studies
have demonstrated the ability of scientific echo sounders to
distinguish various gelatinous zooplankton species, including jellyfish. Although most surveys have been conducted13

to retrieve general information on the jellyfish over a widearea, rapid sampling of a specific small area is also possibleusing acoustic methods. As an example, Alvarez Colombo
et al. (2009) conducted a high-resolution acoustic survey ofa lake to measure the distribution and abundance of jellyfish,
and they suggested that the observed presence of a layered
structure was linked to the dissolved oxygen concentration.
In the study reported in the present paper, we measured
the distribution and abundance of moon jellyfish (Aurelia
coerulea) using a scientific echo sounder and performed
underwater video camera recording in Hokezu Bay and
Mikame Bay of the Bungo Channel, Japan. The aim of ourstudy was to reveal the three-dimensional distributions ofjellyfish aggregations, especially patchy aggregations tens
to hundreds of meters in size, by performing high-resolution
observations using a scientific echo sounder. The acoustic
data were first compared with the jellyfish distribution density measured simultaneously using an underwater video
recording system in order to discern the relationship between
these two sets of data. Then the jellyfish distributions andabundances within the aggregations were estimated acrossthe entire bay using the scientific echo sounding data. Different forms of aggregation can be attributed to different
mechanisms. Therefore, we categorized the aggregations
into specific forms. We expect that information on the different jellyfish aggregation forms will aid studies of aggregation formation mechanisms, and that jellyfish abundance
could be used to estimate their impact on the ecosystem. We
also examined the possible relation of the observed aggregation type to the ambient currents associated with internal waves using particle tracking calculations. Comparison
between the particle tracking results and the observations
allowed us to infer the formation mechanisms of some jellyfish aggregations.
Our observations were conducted in Hokezu Bay
and Mikame Bay of the Bungo Channel, Japan, in summer because moon jellyfish blooms have frequently been
observed around the coastal areas of the Bungo channel
in recent years, particularly in summer (Uye et al. 2003;
Magome et al. 2007; Takeoka et al. 2009; Takahashi et al.
2010). Uye et al. (2003) reported that jellyfish aggregations
reached several hundred meters in horizontal extent, and thatthe maximum concentration was 70 individuals m­ −3. Previous studies investigated the appearance frequency and theseasonal variations of the jellyfish aggregations in Hokezu
Bay, and reported that moon jellyfish appear almost every
year in this bay (Takeoka et al. 2009; Takahashi et al. 2010).
This is why our field surveys were mainly carried out inHokezu Bay.

Moon jellyfish aggregations observed by a scientific echo sounder and an underwater video camera…

2 Methods
2.1 Study area and period
Observations were conducted in Hokezu Bay (Fig. 1b)
on 28–30 August 2013, 25–27 August 2014, and 20–23
August 2016. Hokezu Bay has a length of ~ 2.6 km, awidth of ~ 1.6 km, and a mean depth of ~ 23 m. During 4–5
September 2015, observations were conducted in Mikame
Bay (Fig. 1c), because moon jellyfish were not present in
Hokezu Bay when we searched on 2–3 September 2015.
Mikame Bay has a length of ~ 2.5 km, a width of ~ 0.6 km,

and a mean depth of ~ 20 m. Both bays are connected to
the Bungo Channel, southwestern Japan (Fig. 1a), and the
maximum water depth in the mouth of each bay is about
40 m.

2.2 Field observations
2.2.1 Acoustic scans of the whole bay using a scientificecho sounderIn order to determine the spatial distribution and abundanceof the moon jellyfish as well as the variations in these parameters over time, field observations were conducted onboard(a)MikameBay
HokezuBay
Bungo
ChannelShikoku

Kyushu(c)(b)
Video camera (2013)
Video camera (2014)
Video camera (2016)
CTD (2013)
CTD (2014)
CTD (2016)
Net sampling (2016)

Hokezu Bay

30m
40m

20m

10m

Fig. 1  a Locations of Hokezu Bay and Mikame Bay in the Bungo
Channel, Japan. b Positions at which underwater video camera
recording and acoustic measurements were performed simultaneously
in Hokezu Bay (dots). Contours show the bathymetry in the bay. Triangles depict the positions of CTD observations. Blue, red, and green
symbols indicate observations performed in 2013, 2014, and 2016,

Mikame Bay

Video camera (2015)

10m
20m

30m

respectively. The magenta dotted line is an example of the path of
the ship when an acoustic survey of the whole bay was conducted.
c Orange dots show positions at which simultaneous underwater
video camera recording and acoustic measurements were performedin Mikame Bay in 2015. Contours show the bathymetry of the bay(color figure online)

13T. Mano et al.
Table 1  Specifications of thescientific echo sounder (KCE300 with a T182 transducer)
used in the surveys

Parameter

Value

Frequency (kHz)
Pulse length (ms)Beam width (°)
Ping rate ­(s−1)
Beam type

120
0.6
8.5
5
Split beam

Underwater
video camera

Square frame
0.5 m (2013)
1.0 m (2014-2016)

Fig. 2  Schematic of the underwater video camera recording systemused in this study to count the number of moon jellyfish

the R/V Isana of the Center for Marine Environmental
Studies, Ehime University, Japan. Volume backscatteringstrength was measured using a 120-kHz split-beam echosounder KCE-300 (Sonic Corp.). The specifications of theecho sounder are listed in Table 1. The echo sounder wascalibrated near the study area before the surveys were performed each year. An acoustic survey was conducted acrossalmost all of Hokezu Bay in order to evaluate the continuous
backscattering strength on 30 August 2013, 25–27 August
2014, and 20–23 August 2016 (see Fig. 1b for an exampleof the path taken by the survey ship). During these surveys,
the R/V Isana cruised at a speed of less than 10 knots andtook about 1 h to finish a survey of the bay. In the surveys
performed in Mikame Bay, only small aggregations of moonjellyfish were observed, and many fishing boats often passed
through our observation area. Therefore acoustic scan of thewhole bay was not conducted in Mikame Bay.
2.2.2 Simultaneous underwater video camera recording
and scientific echo sounder observations
To estimate the moon jellyfish abundance from acoustic datataken across the bay, it was necessary to convert the acoustic
backscattering strength into the distribution density of moonjellyfish. For this purpose, we made a video recording system (Fig. 2) in which an underwater video camera was positioned above a square frame. This system was lowered to the13

sea bottom and then gradually pulled up to the surface. As
it was being raised, the system was used to record the passage of jellyfish across the frame. Because the square frame
tended to lean as it descended, making it difficult to obtainaccurate spatial density data for the jellyfish, only the recordings made during ascent phases were used for analysis. The
system was winched with a constant speed of ~ 0.2 m s−1.
The length of each side of the frame was 0.5 m in 2013, but
it was subsequently changed to 1 m. While video recording
was conducted, the ship engine was stopped, and scientificecho sounding was also performed. Therefore, simultaneous underwater video camera recording and scientific echosounding were realized.
Simultaneous measurements were carried out at the stations shown in Fig. 1b, c: total station numbers 67, 15, and
26 in Hokezu Bay in 2013, 2014, and 2016, respectively,
and total station number 45 in Mikame Bay in 2015. Thesestations were determined with an idea covering different jellyfish distribution density.
2.2.3 Bell diameter measurements, plankton net sampling,and hydrographic observations
The bell diameters of moon jellyfish were measured eachyear to confirm their growth status. We randomly captured
more than 100 moon jellyfish near the sea surface using ahand net. The jellyfish were placed on a flat tray and theirbell diameters were measured.
In order to probe the coexistence of zooplankton withjellyfish aggregations, we collected zooplankton at the outermargin of a jellyfish aggregation (Fig. 1b). These samples
were collected by performing vertical tows from 22 m tothe surface using a NORPAC net with a mouth diameter of
45 cm and a mesh size of 335 µm (Motoda 1957) in August
2016. The net towing speed was 0.5 m s−1. During eachsampling, the wire angle was extended until the net reached
the desired depth. Samples were immediately preserved in
5% formalin seawater with sodium tetraborate.
To confirm the stratification state, vertical measurementsof water temperature and salinity from the bottom to thesea surface were conducted at several stations in the bay(Fig. 1b). Conductivity–temperature–depth (CTD) sensors
were used with a multicylinder water sampler (JFE Advantech Co.) in 2013 and 2014, and a RINKO profiler (JFE
Advantech Co.) was used in 2015 and 2016.

2.3 Data analysis
2.3.1 Measuring the jellyfish distribution density by video
analysis
In order to derive distribution densities of the moon jellyfish,
the underwater movies recorded during the surveys were

Moon jellyfish aggregations observed by a scientific echo sounder and an underwater video camera…

analyzed using the Open Source Computer Vision Library(OpenCV). We analyzed many jellyfish pictures using a
Haar feature-based cascade classifier for object detection
and created a jellyfish discrimination program. This jellyfish discrimination program automatically detected moonjellyfish and counted the number of individuals that passed
through the square frame (Fig. 3a). We used this program
to count the number of jellyfish in the underwater camera
images. Counting was performed at depth intervals of 2 m.
The numbers of individuals were then converted into a vertical profile of the distribution density (in individuals m­ −3) foreach video recorded. One example of an estimated verticalprofile is shown in Fig. 3a, where the maximum distribution
density of nearly 20 individuals ­m−3 was located 8–10 m
below the surface. It must be noted that we ignored regionswith jellyfish distribution densities of less than 1 individual­m−3 because we were only interested in aggregations.
2.3.2 Comparison of the mean volume backscatteringstrength with the distribution density of the jellyfish
The Echo View 5.4 (Echoview Software Pty Ltd) software
was used to analyze the echo sounding data. The mean
volume backscattering strength (MVBS) was calculated
for comparison with the distribution density of jellyfish
obtained from the underwater video camera recordings. The
MVBS was derived by averaging the raw volume backscattering strength data in grid squares of size 2 m (depth) by
9 s (Fig. 3b). Acoustic data from depths shallower than 2 mwere removed because of high instrument error.
Ideally, the MVBS data and distribution density dataobtained simultaneously at the same depth would be(a)compared. However, the MVBS data obtained at the sametime and depth as a particular video recording were contaminated with the backscattering signal from the underwater
video recording system, so they could not accurately represent the backscattering signals from the jellyfish. To solve
this problem, we used MVBS data from the grid square corresponding to 9 s before the time at which the video recording was made, but at the same depth, for the comparison
with the distribution density of jellyfish evaluated using theunderwater video recording system (Fig. 3b).
2.3.3 Processing the acoustic data for the bay
The volume backscattering strength data collected during
acoustic surveys of the bay were averaged in grid squares ofsize 1 m vertically and 5 m horizontally. The MVBS for eachgrid square was then converted into a distribution density ofjellyfish using the regression equation derived from the dataobtained simultaneously with the underwater video camera
and the scientific echo sounder.
There are also other organisms (for example whitebait
and other fishes) besides moon jellyfish in Hokezu Bay.
Since the backscattering intensity from a school of fish is
usually stronger than that from a jellyfish aggregation, they
can be distinguished by the echo sounder. Differences inother features such as distribution depth and aggregation
shape also facilitate the identification of a particular sourceof backscattering signals. In Hokezu Bay, the most intense
backscattering signals that likely came from fishes were usually found near the sea bottom. We therefore eliminated thevolume backscattering strength data originating within 2 mof the sea bottom. In addition to eliminating the near-bottom

(b)
-40
-46

-58
-64

SV [dB]

Depth [m]

-52

-70
-76
-82

Density[ind. m-3]

Time (UTC) [hh:mm:ss]

Fig. 3a–b  An example of a comparison between distribution density data for the moon jellyfish and acoustic data. a Vertical profileof the distribution density of the moon jellyfish based on underwater
video camera recording and subsequent video analysis. b Echogram
obtained simultaneously with the underwater video camera recording. The grid is divided into 2-m intervals vertically and 9-s intervals
along the time axis. The ascending and descending strong backscat-tering signals are due to the trajectory of the frame and video camera
as it moves up and down through the water column. The collected raw
volume backscattering signal data were averaged (red squares) andoutput as mean volume backscattering strength (MVBS) data. The
MVBS and the jellyfish distribution density were subsequently compared at the same depth (color figure online)

13T. Mano et al.

echo sounding data, we also defined an upper threshold for
the MVBS data (− 60 dB and − 50 dB in 2014 and 2016,
respectively) because some of the fishes were distributed
not only near the sea bottom but also in the middle layer.

3 Results
3.1 Relationship between the MVBS
and the distribution density of the jellyfish
The scatter plot of the MVBS versus the distribution density of the jellyfish shown in Fig. 4 suggests the following
relation:(1)
where D is the distribution density of moon jellyfish (individuals ­m−3), and the slope a and the intercept b are determined by a least squares fit to the data. The values of a, b,
the correlation coefficient R, and the p value for the data ineach survey as well as the data in all the surveys are shownin Table 2. Since a p value < 0.05 was taken to indicate asignificant correlation, we obtained significant relations in
all the years considered. However, the slope a and intercept
b in regression equation (1) changed a little from year toyear, and the correlation coefficient also varied during eachyear. Our results show that the correlation coefficient tended

log10 (D) = a × MVBS + b,

Fig. 4  Scatter plot of the mean volume backscattering strength
obtained by acoustic measurements versus the distribution densityof moon jellyfish derived from underwater video camera recordings.
Blue, red, orange, and green dots represent data from 2013, 2014,2015, and 2016, respectively. Each line denotes the regression relation obtained for the dots of the same color. The black line is theregression relation for all of the data (color figure online)

13

Table 2  Slope (a), intercept (b), correlation coefficient (R), and pvalue associated with Eq. 1, i.e., l­og10(D) = a × MVBS + b, as calculated using the dataset for each year and the dataset for all years

All data
2013
2014
2015
2016

a

b

R

p value0.03580.03780.04440.03880.0432

3.31
3.49
3.81
3.49
3.74

0.64
0.66
0.57
0.54
0.80

5.16 × 10−46
1.71 × 10−29
4.36 × 10−4
3.87 × 10−8
2.41 × 10−7

D is the distribution density of moon jellyfish and MVBS is the mean
volume backscattering strength as measured by the scientific echosounder

to be larger when the distribution density data encompassed
a wide range of values.

3.2 Jellyfish distribution types
Three-dimensional moon jellyfish distributions obtained
from the MVBS data using Eq. 1 are presented in Figs. 5,8, and 10 as well as in the Electronic supplementary material (ESM). Aggregations of jellyfish can be seen in those
distributions. These aggregations were categorized into three
types: patchy, layered, and wavy.
3.2.1 Patchy type
Patchy aggregations of moon jellyfish were readily apparent
in the observations made on 30 August 2013 (Fig. 5). The
patchy type consisted of a number of condensed aggregations with various shapes, such as an elongated band, sphere,or ellipse. Some of the vertical cross-sections of the patchy
aggregations had a hollow structure, i.e., a low-density areasurrounded by a high-density area.
Figure 6 highlights three types of patchy aggregationsin three-dimensional space that were observed in 2013
and 2016. The first looks like a horizontal tube with a hollow structure inside (Fig. 6a). It also appears in Fig. 5 asaggregation 1. The entire patchy aggregation had a lengthof ~ 500 m, a width of ~ 70 m, a height of ~ 20 m, and a
depth of 10–30 m. The highest distribution density in theregion surrounding the hollow area exceeded ~ 40 individuals ­m−3, which is similar to the distribution density derived
from previous in situ observations of the same area (Uyeet al. 2003). The distribution density within the hollow, on
the other hand, is as low as several individuals m­ −3. The
total amount of jellyfish in the aggregation was calculated tobe 1.1 × 107 individuals. Although not very apparent in the
figures, this type of aggregation sometimes had a twin-cellstructure (Fig. 6a, middle cross-section).
The second patchy aggregation had a band-shaped structure without a hollow inside it (Fig. 6b). Its length wasMoon jellyfish aggregations observed by a scientific echo sounder and an underwater video camera…
North

West
(Baymouth)

3
Depth [m]Fig. 5  Three-dimensional
structure of the estimateddistribution density of the moonjellyfish on 30 August 2013 in
Hokezu Bay. Color is used toconvey the magnitude of the
distribution density of moonjellyfish (individuals ­m−3). Tenparallel vertical cross-sections,each oriented north–south, thatwere obtained in the acoustic
survey are shown. Three patchy
aggregations are indicated by
red circles labeled 1, 2, and 3(color figure online)

12

Distribution density [ind. m-3]

~ 300 m, its width was ~ 100 m, and its height was ~ 20 m.
The third (Fig. 6c) has a hollow structure but the hollow
region is only apparent in one vertical cross-section. Consequently, this type of aggregation resembles a flat sphere or
doughnut rather than a tube. Previous studies have reported
similar hollow structures, such as a single-cell structure(Toyokawa et al. 1997; Churnside et al. 2016) and a twincell structure (Churnside et al. 2016).
Churnside et al. (2016) reported that most of the jellyfish aggregations were distributed in the surface mixed
layer at depths < 13 m. In this study, however, no relationship between patchy aggregation depth and the pycnocline
was discerned. For instance, although the pycnocline wasat a depth of ~ 7 m in our study area, an aggregation wasobserved in the lower layer of the pycnocline (Fig. 7a), while
another aggregation was distributed across most levels of
the water column, ranging from the sea surface to near thesea bottom (Fig. 7b). Both aggregations occurred simultaneously and within close proximity (Fig. 5).
3.2.2 Layered type
Layered jellyfish aggregations appeared around southwestern
Hokezu Bay in 2014 (Fig. 8). On August 25, the first day of
our field observations in 2014, a high density of moon jellyfish was noted in two particular layers at depths of ~ 5 m and~ 20 m, respectively (Fig. 9). However, the highest distribution density was only 15 individuals ­m−3, which is lower thanthat of patchy aggregations. Two pycnoclines were found at
depths of ~ 2 m and ~ 20 m, and the upper pycnocline wasstronger than the lower one. The upper layered aggregation
was located beneath the upper pycnocline, while the lower layered aggregation was within the lower pycnocline. Although
we cannot explain why layered jellyfish aggregations form,
it is likely related to stratification. This two-layered jellyfish
aggregation gradually faded away over three days, with both

the distribution density and the abundance decreasing over that
period. We also found a similar layered structure in observations made in 2013 at depths of < 10 m in most survey sections(Fig. 5). In 2013, the pycnocline occurred at a depth of around
7 m (Fig. 7), which is close to the distribution depth of thelayered jellyfish aggregation. Although this layered aggregation coexisted with patchy aggregations in 2013, we did notobserve patchy aggregations in 2014.
3.2.3 Wavy typeIn this type of aggregation, a high-density layer of moon jellyfish formed a wavy structure at the bottom of or within thearea with a high distribution density (Figs. 10 and 11). Sucha wavy structure was observed on three consecutive days in
2016, but the wavelength, wave height, distribution density,and distribution depth varied with the day. On 21 August, there
was a wavy high-distribution-density region (~ 30 individuals ­m−3) surrounding a low-density one (about 8 individuals­m−3; see Fig. 11a). Its horizontal wavelength was ~ 30 m andits wave height was ~ 7 m. A thermocline occurred at a depthof ~ 2 m, while the wavy layer was localized at a depth of~ 20 m. Other aggregations of the same type were observed on
22 and 23 August (Fig. 11b, c). These aggregations had a wavy
structure at the bottom. The distribution density and distribution depth were similar on both days, i.e., ~ 7 individuals ­m−3
and ~ 15 m, respectively. However, the horizontal wavelengthand the wave height were ~ 30 m and ~ 4 m, respectively, on
22 August and ~ 60 m and ~ 7 m on 23 August. These wavy
aggregations tended to occur around the northern part of thebay during the 3-day period.

13

T. Mano et al.

(a)

West
(Baymouth)North

Depth [m]Fig. 6a–c  Three examples oftypical patchy aggregations.
The vertical cross-sections wereextracted from the entire dataset
obtained by the acoustic survey
in Hokezu Bay. a An elongatedband-shaped aggregation withvertical cross-sections that show
a hollow structure, as observedon 30 August 2013 (extracted
from Fig. 5). b An elongatedband-shaped aggregation
without a hollow structure, as
observed on 21 August 2016.
c A flat spherical aggregation
with a hollow structure, as
observed on 20 August 2016

Distribution density [ind. m-3]

(b)

North

Depth [m]EastDistribution density [ind. m-3]

(c)

North

Depth [m]EastDistribution density [ind. m-3]

4 Discussion
4.1 Variation of the regression equation
between the MVBS and the distribution densityof the jellyfish over time

13

The variation of the regression equation over time suggeststhat the target strength of the moon jellyfish changes overtime. It has been reported that the target strength of jellyfish varies with bell diameter and pulsation (Mutlu 1996;
Yoon et al. 2018). As the bell diameter increases, the target strength of the jellyfish increases. In our observations,

Moon jellyfish aggregations observed by a scientific echo sounder and an underwater video camera…

Depth [m]Distribution density[ind. m-3](a)Distance [m]Distribution density[ind. m-3](b)Depth [m]Fig. 7a–b  Extracted verticalcross-sections (a, b) of patchy
aggregations observed on 30
August 2013. Cross-sections
a and b were extracted fromaggregations 1 and 2 in Fig. 5,
respectively. Isopycnic lines (inkg m−3) obtained from CTD
observations are superimposedon the jellyfish distribution(color figure online)

Distance [m]Fig. 8  Similar to Fig. 5, but theresults of an acoustic survey
conducted on 25 August 2014 in
Hokezu Bay are shown instead

North

Depth [m]West
(Baymouth)

Depth [m]Distribution density[ind. m-3]

Distribution density [ind. m-3]

Distance [m]Fig. 9  A vertical cross-section of the layered aggregation observed
on 25 August 2014. This cross-section was extracted from Fig. 7. Inorder to emphasize the double-layered structure, the density range

covered by the color shading is different from that in Fig. 7. Isopycniclines (kg m−3) obtained via the CTD observations are superimposedon the jellyfish distribution (color figure online)

the mean bell diameter of the sampled moon jellyfish
was 16.0 ± 2.7 cm, 14.0 ± 2.3 cm, 15.3 ± 1.9 cm, and18.2 ± 2.0 cm in 2013, 2014, 2015, and 2016, respectively.

Although the mean bell diameter was largest in 2016 andsmallest in 2014, the coefficients in the regression equation
for those two years were very similar (Fig. 4 and Table 2).

13

T. Mano et al.
Fig. 10  Similar to Fig. 5, but
the results of the acoustic
survey conducted on 23 August
2016 in Hokezu Bay are shown
insteadNorth

Depth [m]West
(Baymouth)

Distribution density [ind. m-3]

Depth [m]Distribution density[ind. m-3](a)Distance [m]Distribution density[ind. m-3](b)Depth [m]Fig. 11a–c  Extracted vertical cross-sections of the wavy
aggregations observed on 21–23
August 2016 (a–c, respectively).
The bottom panel (c) was
extracted from the northernmost
cross-section in Fig. 9, but thedensity range covered by thecolor shading is different fromthat in Fig. 9. Isopycnic lines(kg m−3) obtained via the CTD
observations are superimposedon the jellyfish distribution

Distance [m]Depth [m]Distribution density[ind. m-3](c)Distance [m]Therefore, changes in the mean bell diameter are not considered to be a significant factor in the annual variation intarget strength in our study area. Since brief variations intarget strength due to jellyfish pulsations should disappear
when the results for many individuals are averaged, it isalso difficult to attribute the variation of the regression
equation to pulsation.

13

Another possible cause of the variation in regression
coefficients is the presence of other organisms. When thejellyfish coexist with other organisms, quantifying jellyfish abundance can be difficult (Brierley et al. 2001,
2005). Plankton net sampling around the margins of the jellyfish aggregation in 2016 showed the presence of many
planula and some zooplankton groups such as Copepoda,

Moon jellyfish aggregations observed by a scientific echo sounder and an underwater video camera…

Appendicularia, Mysidacea, Sagittoidea, and Doliolida.
In addition, other kinds of gelatinous zooplankton such as
Beroidea and Lobatea were observed at the sea surface orin underwater video recordings. Although we do not know
the extent to which these coexisting organisms affected theregression coefficient, they were likely one of the causes of
the annual variation in regression coefficients in our study.
Measurements can be performed in the laboratory to
determine the relation between target strength and one jellyfish (Hirose et al. 2009). However, most jellyfishes do nothave strong scattering sources in their bodies, unlike fishes,
which have air bladders. Consequently, the relation between
target strength and one jellyfish obtained in the laboratory
is easily affected by the presence of ambient coexistingorganisms in the ocean. Therefore, an in situ comparison isa more reliable way to obtain the relation between MVBS
and jellyfish abundance. The high correlation coefficientsobtained for each year in our study (Table 2) also support
the application of this method. Note that this method has aninherent disadvantage: the background of ambient coexistingorganisms changes over time, so a new comparison shouldbe performed with each new observation. In practice, however, this is not necessary because our results showed thatthe coefficients in the regression equation between distribution density and MVBS varied only slightly over time.

4.2 Effect of internal waves on aggregation
formation
Due to the weak swimming ability of moon jellyfish, theirdistribution is affected by the ambient flow. At first sight,
aggregation formation could easily be explained by thepresence of a convergence flow around a front. However,
we did not identify any tidal fronts in Hokezu Bay based on
the data collected using the ship-mounted water temperature
sensor. Because of the small spatial scale of a moon jellyfish
aggregation, it is also difficult to associate such aggregations
with tidal currents and subtidal currents induced by winds
and density gradients across the entire bay (i.e., these arebay-scale phenomena).
In Hokezu Bay, jellyfish appearance in the sea surface
has also been monitored since 2003 by taking photos of thesea surface every few minutes from a hill on the southerncoast of the bay (Takeoka et al. 2009; Takahashi et al. 2010).
These photos show that small-scale internal waves with aperiod of ~ 10 min and a wavelength of ~ 100 m are commonplace in Hokezu Bay (these waves present distinctive
striped slicks and ripple patterns on the sea surface; see also
Fig. 12). The period of ~ 10 min is a rough estimate of theinterval between consecutive sea surface slicks passing aparticular point. The wavelength of ~ 100 m is also a roughestimate obtained through comparison with the total lengthof the R/V Isana (17.5 m) from a photo in which both theship and the slick pattern appear. We therefore hypothesizethat these internal waves could be one reason for the formation of jellyfish aggregations.
To confirm this hypothesis, we carried out particle tracking calculations for some flow fields corresponding to linear
internal waves. In this calculation, we ignored the swimming
behavior of the jellyfish and regarded each jellyfish as a particle driven only by the specified ambient flows.
Assuming a constant vertical density gradient in a nonrotating plane, linear internal wave theory (e.g., Gerkema
and Zimmerman 2008) affords analytical solutions for the

Fig. 12  A photograph of the sea
surface in Hokezu Bay taken
at 14:12 on 23 August 2016.
Slicks and a ripple pattern
induced by internal waves canbe discerned. The acoustic scanobservations shown in Fig. 9
were conducted at that time, but
the survey ship was outside thearea included in the photo. The
left side of this picture is west
(bay mouth) and the right side
is east (bay head)13

T. Mano et al.

vertical and horizontal velocities, w and u, in a two-dimensional vertical plane:
)
(
∑(
(
))
n𝜋z
cos kn x − 𝜔tw=an sin
(2)
H
n

u=−

kn = ±

𝜔=

n

(

n𝜋
H

(

∑

an

)
(
)
(
)
n𝜋z
n𝜋
sin kn x − 𝜔t
cos
kn H
H

𝜔2
2
N − 𝜔2

2𝜋
,
T

)1∕2

(3)

(4)

(5)

where kn is the horizontal wavenumber for the nth mode
wave, ω is the angular frequency, H is the mean water depth(30 m in this study), N is the Brunt–Väisälä frequency, and T
is the wave period. In this calculation, we set N = 0.02 rad s−1
based on the results of CTD measurements. In fact, the meanvalue of N across all the layers was about 0.02 in each of theyears of interest (2013, 2014, and 2016). We considered only
the first-mode internal waves (n = 1).
The frequency of the internal waves (ω) is restricted by
the stratification condition, i.e., it should satisfy(6)where f is the Coriolis parameter. A Väisälä frequency N of
0.02 was indicated by our field observations, so the inverseof that frequency (the Väisälä period) is 314 s. Because thisVäisälä period is the minimum period of the internal waves,
we calculated some cases in which the period of the internal
wave was longer than 6 min. Using wave periods of 6, 10,15, and 20 min and amplitude of horizontal velocities (i.e.,
an kn𝜋H in Eq. 3) of 5, 10, 15, and 20 cm s−1, we performed a

N 2 > 𝜔2 > f 2 ,ntotal of 16 calculations. The domain considered in the calculations was 30 m in the vertical direction and 1300 m in
the horizontal direction, with the origin of the coordinates
taken to be at the sea surface and at the center of the horizontal axis. The particles were distributed over the region
with x = − 200 to + 200 m and z = − 30 to 0 m at 1 m intervals in both the horizontal and the vertical directions in theinitial state (t = 0). We calculated for five wave periods ineach case.
Figure 13 shows the results of the particle tracking calculations. In each panel, an internal wave propagates fromleft to right. The results in all of the cases indicate that theparticles in the upper and lower layers move in the same
direction as the direction of propagation of the internal wave,
while those in the middle layer move in the opposite direction due to the Stokes drift induced by the internal waves

13(e.g., Gil 2017). The effect of the Stokes drift strengthens as
the current amplitude increases. A significant wavy structurein the particle distribution can be identified when the amplitude of the current velocity exceeds 10 cm s−1 (Fig. 13). The
wavelength of the structure is estimated to be ~ 30, 100, 150,
and 200 m for wave periods of 6, 10, 15, and 20 min, respectively (Fig. 13). These are in good accord with the horizontal
wavelengths of internal waves (33, 97, 161, 223 m) given
theoretically by Eq. 4. On the other hand, the wavelengthsof the wavy aggregations of jellyfish observed in the waterin August 2016 were ~ 30 m or ~ 60 m. Substituting a wavelength (= 2πk−1) of 30 m or 60 m and a Brunt–Väisälä frequency N of 0.02 rad s−1 into Eq. 4 allows the period of theinternal waves that occurred at that time to be estimated.
The wave period was found to be 351 s (5.9 min) when
the wavelength was 30 m on 21 and 22 August, and 446 s(7.4 min) when the wavelength was 60 m on 23 August. Thisestimation is consistent with the fact that the internal waves
observed in Hokezu Bay were usually with a wave period of~10 min and a wavelength of ~100 m.
We then examined how the initial positions of the particles influence the generation of a wavy structure by placing the particles in the upper half layer, the surface layer,
the middle layer, the bottom layer, and the lower half layer,
respectively (Fig. 14). We introduced two types of internal
waves with velocity amplitude of 5 and 10 cm s−1, respectively, and a wave period of 6 min. Initially distributing theparticles in a particular layer resulted in the formation of awavy structure in that layer after five wave periods. Indeed,
the particle distributions obtained when the particles were
initially placed in the surface layer or the bottom layer weresimilar to the jellyfish aggregations observed in the surface
or bottom layer, respectively, in 2016 (Fig. 11). Therefore,
the Stokes drift induced by internal waves likely influenced
the formation of wavy jellyfish aggregations.

4.3 Patchy aggregations and their hollow structure
Our particle tracking calculations could not reproduce
patchy jellyfish distributions nor their hollow structure.
This indicates that their formation is not induced directly by
the first-mode linear internal wave. In fact, Churnside et al.
(2016) observed patchy aggregations with hollow structure
where internal waves did not occur. Hence, these types ofaggregations appear to be related to other physical processes
and/or the swimming behavior of jellyfish.
Since the Langmuir circulation is a characteristic phenomenon with a zonal and a circulation cell structure in thesea surface layer (Leibovich 1983; Thorpe 2004), Langmuir
circulation could induce the formation of zonal and hollow
structures such as the observed tube structure (Fig. 6a).
Indeed, Hamner and Schneider (1986) and Larson (1992)
reported elongated jellyfish aggregations at the sea surface

Moon jellyfish aggregations observed by a scientific echo sounder and an underwater video camera…

(b)
Depth [m]Depth [m](a)Depth [m]Depth [m]Depth [m]Distance [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Distance [m]Distance [m](d)Depth [m]Depth [m](c)Distance [m]Depth [m]Depth [m]Depth [m]Distance [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Distance [m]Distance [m]Fig. 13a–d  Spatiotemporal evolution of the particle distribution indifferent linear internal wave fields. a–d Results obtained with different wave periods (T = 6, 10, 15, and 20 min). Red dots indicate particles, and black vectors indicate the current speed and direction. The
uppermost panel in a–d represents the initial distribution of the par-Distance [m]ticles; the other panels show the particle distribution after five wave
periods with different amplitudes of the horizontal current velocity(5, 10, 15, and 20 cm s−1 from top to bottom, respectively) (color figure online)

13T. Mano et al.

(c)Distance [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m]Depth [m](b)Depth [m](a)Distance [m]Distance [m]Fig. 14a–c  Effect of changing the initial particle distribution on thespatiotemporal evolution of the particle distribution. a Six panelsfrom top to bottom show initial particle distribution over entire watercolumn and in the upper half, surface, middle, bottom, and the lower
half layers, respectively. b Consequential particle distribution after 5
wave periods in a linear internal wave with a wave period of 6 min

and a velocity amplitude of 5 cm s−1. Six panels from top to bottom
represent the results corresponding to different initial particle distributions shown in the same line in a. c Similar to b, but with a velocity amplitude of 10 cm s−1. Red dots indicate particles (color figure
online)that formed as a result of a combination of accumulation in
the zonal convergence area due to Langmuir circulation andupward swimming behavior. However, this is unlikely to be
the case in Hokezu Bay because of the following reasons.
First, an aggregation with a hollow structure was observed
at depths below the pycnocline (Fig. 7a), while the Langmuir circulation was strongest in the sea surface layer, and it
affected only the water above the pycnocline. Furthermore,
the observed elongated aggregations were not distributed ina uniform direction (Fig. 5), and the wind speed (~ 3 m s−1)
and wave height (< 0.5 m) during our observations were
not strong enough (Leibovich 1983) to produce Langmuir
circulation of the same scale as the observed hollow structure of jellyfish aggregations. Therefore, we concluded thatthe patchy aggregation with a hollow structure observed in
Hokezu Bay was not formed by Langmuir circulation.
The hollow structures may be formed due to the swimming behavior of jellyfish. Although studies of the swimming behavior of moon jellyfish in aggregations are rather
rare, Purcell et al. (2000) observed jellyfish swimming vertically in crowded regions of aggregations. Purcell et al.
(2000) concluded that the aggregations form due to a combination of swimming behavior and flow features. Churnside
et al. (2016) used that result to interpret the hollow structure of a moon jellyfish aggregation they observed. Churnside et al. (2016) suggested that the flow field triggers theformation of an aggregation, after which the swimming ofjellyfishes maintains the aggregation. In Hokezu Bay, theelongated aggregation had a hollow structure that reachedhundreds of meters in length. It is difficult to believe thatsuch a structure could be formed and maintained only by
the swimming behavior of jellyfish. Physical processes suchas convergence flow around a small-scale topographic front(Wolanski and Hamner 1988) and stretching by sheared flow
should also affect the formation of such a structure. Therefore, a reasonable explanation for formation of the elongated aggregation and its hollow structure is that swimming
behavior, physical processes, and their interactions all likely
contribute to the formation and retention of the aggregation.
The contributions of physical and biological factors to theformation of other patchy aggregation types, i.e., sphericalor elliptical forms, it is unclear. Nevertheless, the swimming
behavior of jellyfish must be investigated in order to elucidate the formation mechanisms of patchy aggregations.

13

4.4 Influence of the pycnocline on the vertical
distribution of the moon jellyfish
In areas with large amounts of freshwater discharge, strong
stratification due to low-salinity water in the sea surface
layer can restrict the vertical distribution of moon jellyfish because of the effect of buoyancy (Suzuki et al. 2016,

Moon jellyfish aggregations observed by a scientific echo sounder and an underwater video camera…

2017, 2018). Suzuki et al. (2017) estimated that the threshold value of Δ𝜎tmax , an index of the stratification strength,
was 0.7–1.1 kg m−3. However, such strong stratificationis unlikely to be present in our study area because there isno large river discharge around the Uwa Sea. In fact, thevalue of Δ𝜎tmax estimated from our CTD observations was
0.33 kg m−3 in 2013 and 0.46 kg m−3 in 2014, respectively.
Although the stratification was not strong enough to
restrict the vertical distribution of the jellyfish, the doublelayered structure observed in 2014 and the layered distribution observed in the sea surface layer in 2013 was related
to the distribution depth of the pycnocline. In this case, thepreference hypothesis is likely to be applicable (i.e., preyorganisms may have been present around the depth of thepycnocline or in a region shallower than the pycnocline),
rather than the buoyancy hypothesis. Since we did not collect data on the vertical distributions of prey organisms, wehave no direct evidence to support this hypothesis.
The facts mentioned above suggest that the swimming
behavior of jellyfish is an important factor in the formationof layered aggregations and patchy aggregations. Because
layered aggregations and patchy aggregations differ in form,
distribution density, and their relationship to the pycnocline,
the reason for aggregation formation may differ dependingon the type of aggregation considered—namely, layered
aggregations and patchy aggregations are formed as a resultof swimming behavior aimed at foraging and reproduction,
respectively. This, however, is only a hypothesis; more dataon the swimming behavior of jellyfish and the distribution
density and distribution depth of prey organisms are needed
to fully test this hypothesis.

5 Conclusions
In this study, we carried out field observations of the spatial
distributions of moon jellyfish in two small bays using ascientific echo sounder and an underwater video camera. The
good regression relation between the volume backscattering
signal given by the scientific echo sounder and the distribution density of jellyfish given by the underwater video
camera allowed us to examine the continuous distributionsof jellyfish in several cross-sections to a resolution of 1 m
in the vertical direction and 5 m in the horizontal direction.
The observed jellyfish distributions could be categorized into three types of aggregations. Patchy aggregations
took various forms, such as an elongated band, a sphere, an
ellipse, and even a hollow structure. Layered aggregations
were closely associated with the depth and shape of the pycnocline. Wavy aggregations had wave-like distributions withhorizontal wavelengths of tens of meters and wave heights ofseveral meters. Among these three types of aggregations, wefound that the highest distribution density of moon jellyfish
occurred in the patchy type and lowest in the layered type.
Passive particle tracking was carried out in current
fields with linear internal waves. The wave period wasvaried between 6 and 20 min and the amplitude of thehorizontal current between 5 and 20 cm s −1. The resultsof the particle tracking pointed to a possible mechanism
for the formation of wavy aggregation: the Strokes driftof internal waves causes particles in the middle layer tomove in the opposite direction to those in the surface andbottom layers, thus producing a wave-like distribution ofparticles. However, this mechanism is not responsible forthe other two types of aggregations. Further careful andwell-designed observations are needed to understand thebiological behaviors of moon jellyfish and their contributions to the formation of jellyfish aggregations.
Acknowledgement Special thanks to Professor Kazushi Miyashita,
Field Science Center for Northern Biosphere, Hokkaido University,
for his valuable advice on acoustic data analysis and interpreting theresults. We are very grateful to Mr. Hidejiro Ohnishi, Captain of the
R/V Isana, for his help with field observations, and to Professor Hidetaka Takeoka, South Ehime Fisheries Research Center, Ehime University, for his helpful comments. This work was supported by the Japan
Society for the Promotion of Science (JSPS) KAKENHI (grant no.
25550014) and a Sasakawa Scientific Research Grant from The Japan
Science Society.

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