RESEARCH ARTICLE

A new way to understand migration routes of oceanic
squid (Ommastrephidae) from satellite data
Fei Ji1& Xinyu Guo2

1

Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
Centre for Marine Environmental Studies, Ehime University, Matsuyama, Japan

2

Keywords
Age spectrum, fishing ground distribution,
habitat temperature, japanese flying squid,
migration route, VBD satellite data
Correspondence
Fei Ji, Second Institute of Oceanography,
Ministry of Natural Resources, Hangzhou
310012, China. Tel: +86 81968507; E-mail:jfmy@sio.org.cn

Funding information
This research is supported by the National
Natural Science Foundation of China(42206114).
Editor: Kylie Scales
Associate Editor: David Curnick
Received: 20 January 2023; Revised: 30 July
2023; Accepted: 6 September 2023
doi: 10.1002/rse2.368
Remote Sensing in Ecology andConservation 2024;10 (2):248–263

Abstract
Visible Infrared Imaging Radiometer Suite (VIIRS) Boat Detection (VBD) data
have been widely used to study the patterns of fishing grounds and their linking
to fishery targets, particularly species mainly caught by jiggers. In line withmost species in the Ommastrephidae family, the population of Todarodes pacificus is made up of various splinter cohorts concerning the timing and locationof hatching. Therefore, the satellite-recorded fishing grounds consist of groups
with complex age structures and different migration directions within cohorts.
This study examined the age composition of harvestable stocks (age spectrum)of T. pacificus in the Japan Sea based on an early life history individual-basedmodel of T. pacificus and VBD data. Using the age spectrum, we analysed therelationship between fishery effort and the age of the target group. It was foundthat jiggers most prefer individuals around 310  20 days. Furthermore, thecorrelation between ambient water temperature and fishing effort revealed thatT. pacificus migrated to colder waters, reaching the coldest waters at
250  7.5 days before moving back towards warmer waters. We discussed apossible way to use the age-temperature relationship to analyse the flow of
VBD distributions to record the movements related to the migration of thefishing target. The results show migration-like trajectories, which are initiallyparallel to the isotherm, gradually deflect towards lower temperature sides over
several months, sharply turn for about a month and then move back with aslight angle to the isotherms. The method provides a potential framework to
improve our understanding of the active migration of oceanic squid.

Introduction
Many species of the family Ommastrephidae, also known
as oceanic squids, are commercially important to the
deep-sea squid jigging and trawling industry. These
include Dosidicus gigas, T. pacificus, Ommastrephes bartramii and Illex argentinus (Chen et al., 2008; Fukuda &Okazaki, 1998; Yatsu et al., 1999). In 2020, exports ofcephalopods reached USD 10.2 billion worldwide, withmore than 1/3 comprising species in Ommastrephidae(FAO, 2022). These species have large-scale migrations
encompassing thousands of kilometres in their lifespan of
1–2 years (Arkhipkin et al., 2015; Murata, 1990; Nigmatullin et al., 2001). They exhibit a high level of plasticity
in their habitat distribution and migration routes, along248

with changes in their life history, such as diet, age atmaturity and timing of spawning. Therefore, environmental changes have strongly influenced the abundance, distribution and regeneration of fishery stocks, which has
resulted in substantial interannual fluctuations in stock
size (Rodhouse et al., 2014).
The migration route of oceanic squids is important for
efficient fishing operations and fishery stock management.
For fishery operations, understanding migration patterns
are important in predicting potential fishing grounds. For
stock management, the migration route is associated withthe growth environment of schooling oceanic squids and is
key information in predicting stock size and regeneration.
The establishment of banned fishing areas and exclusive
economic zones has increased stakeholder focus on changesª 2023 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use anddistribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

in migration routes (Kidokoro et al., 2010; Mokrin
et al., 2002). The long migration of oceanic squids is also
known for its role in nutrient flow in marine ecosystems(Arkhipkin, 2013; Piatkowski et al., 2001).
Various tracking approaches have been used to examine the migration patterns of oceanic squid. This has
included a wide variety of tags (Kidokoro et al., 2010;
Rigby & Sakurai, 2005) and biomarkers, such as statolithchemistry analysis, parasite analysis and genetic marker
methodologies (Ikeda et al., 2003; Liu et al., 2016; Yamaguchi et al., 2018). Semmens et al. (2007) have systematically reviewed these methods.
In addition to tracking methods based on tagging andnature makers, methods using night-time visible images
such as the Defence Meteorological Satellite Program/
Operational Linescan System and Visible Infrared Imaging
Radiometer Suite Day/night band (VIIRS/DNB) images
have increasingly been used in resolving the distribution
and migration pattern of harvestable stocks of squid.
Most fisheries of Ommastrephidae use light to attract
squid, which is convenient for remote monitoring of the
migration of Ommastrephidae in oceanic waters throughsquid fishing fleets (Rodhouse et al., 2001; Semmens
et al., 2007). Representative studies include Kiyofuji and
Saitoh (2004), Choi et al. (2008), Wei et al. (2018) andTian et al. (2022), who extracted the migration routes ofT pacificus and O. bartramii from the centroid transfer ofsatellite imagery-recorded fishing lights (SIRFL). Waluda
and Rodhouse (2006), Alabia et al. (2016), Zhang
et al. (2017) and Oh et al. (2020) measured the distribution of SIRFL concerning environmental conditions andestimated the habitat conditions of squid during migration. They reported that water temperature was the mostcritical environmental factor affecting habitat selection
and migration routes.
The SIRFL pattern was assumed to be the approximate
distribution of the fishery stock in those studies. The fishing lights were assumed to be a continuous moving set oflight spots in satellite images whose trajectory reflects the
migration of a squid population. However, someresearchers have raised questions regarding this assumption as follows:
1. It is common for multiple groups of squid to migrate
simultaneously inside the study area. T. pacificus has
a distinct autumn-spawning population and a less distinct non-autumn-spawning population dominated bya winter-spawning cohort (Katugin, 2002; Kidokoro
et al., 2010). The non-autumn-spawning population
comprises splinter cohorts and micro cohorts withvariable hatching dates, spawning grounds and migration routes (Takayanagi, 1993; Sakurai et al., 2000).
The O. bartramii population in the North Pacificencompasses two seasonal cohorts, namely the

VBD Data and Migration Pattern of Oceanic Squid

winter–spring and autumn-spawning groups (Yatsu
et al., 1997). Even if these populations or cohorts
have the same migratory strategy, they may show different migratory trends owing to different hatchingdates and life stages. Complex motion patterns makeit difficult to extract migration patterns at large scales,such as sea basin levels migration trajectories from
SIRFL, especially for high temporal resolution data
encompassing a relatively large study area. Therefore,
Kiyofuji and Saitoh (2004) classified the Japan Sea
into seven regions according to temporal variation infishery lights with the assumption that the fishingactivities in each region have been targeted to a specific school of squid. Wei et al. (2018) reduced theresearch area to ensure that one continuous cohort offishery targets passed through the research area withina specific timeframe. However, none of these studies
has examined how SIRFL corresponds to the diverse
selection of multiple age groups. In this study, groups
with similar locations, ages and migration trends arereferred to as ‘batches’, which constitute a seasonalcohort, to match the high spatiotemporal resolutionof satellite data.
2. SIRFL reflects the fishing location of the fleet, that is,
the indirect distribution and abundance of fisheryeffort, and not that of the fishery stock directly (Semmens et al., 2007). Knowledge of fishing and social
interaction also affects the distribution of fisheryefforts (Barbier & Watson, 2016; Wijermans
et al., 2020). Therefore, fishing grounds with equivalent resource abundance may exhibit varying levels offishery attraction because of the characteristics of targeted species and environmental conditions (Masuda
et al., 2014). This study examined how fishermen’s
preferences for specific targets such as size influence
the distribution of fishing grounds.
3. SIRFL should not move continuously forming a trajectory related to migration. The fishing vessel’s
movement is linked to the migration of squid only
during the ‘chasing and catching phase’. Therefore,
tracking should be interrupted when the chasingphase concludes and transitions into the search ortransfer phase between fishing grounds. Otherwise,
those movement unrelated to migration may be incorrectly recorded in the trajectories. However, identifying the working phase of a fishing vessel from the
movement of the SIRFL is challenging as it is a timediscrete and anonymous record of jigging work location (Oh et al., 2020).
In response to these questions, this study used the ageof the harvestable stock as the core variable linking theenvironmental conditions and the amount of fisheryeffort, so that fleets can be clustered in respect of theª 2023 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.

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F. Ji & X. Guobatch of their fishing target. A similar concept has been
well applied to framing the kernel spawning ground of
chokka squid (Loligo reynaudii) resources in South Africa
by utilizing the age distribution to establish a correspondence between hatching events and fishing date (Lipiński
et al., 2020). Building upon this concept, we propose several new assumptions for the analysis of SIRFL.1

Assumption
All batches of T. pacificus share the same migratory
behaviour strategy.2

Assumption
The preference of fishery in catching T. pacificus is related
to the size of squid. Following Kidokoro et al. (1999) andArkhipkin and Roa-Ureta (2005), the size of squid is simplified as a unary function of age. For this reason, we
assumed the preference of fishery is also a unary functionof age.3

Assumption
The movement pattern of the fleets is reflected as a trajectory, only when they chase the schools of a batch ofsquid. In this scenario, the surrounding environment of
the fleet is related to the habit and behaviour of the batchof squid. Otherwise, the vessels in the fleet disperse andenter the search and transfer phase.
This study selected T. pacificus as a target, which is themost studied Ommastrephidae species (Rodhouse
et al., 2014) with a total annual catch exceeding 300 thousand tons from the 1950s to the 2010s (FAO, 2022).
However, this dropped sharply after 2013 and to 22.8
thousand tons in 2018 (Fisheries Agency of Japan,
FAJ, 2020). T. pacificus has a 1-year life span, which
begins with spawning and hatching in the East China Sea
and the Japan Sea by following the environmental conditions of 100–500 m in water depth and 19.5–23°C in SST(Rosa et al., 2011; Sakurai et al., 2000). Theoretically,
spawning and hatching occur year-round. However,according to the peak hatching abundance, the autumn
and winter-spawning cohorts were regarded as the primary fishery stocks (Kasahara, 1978). In the early stage,
approximately
140 daysafter
hatching(Ikedaet al., 2003), poor swimming ability allows paralarvae(\45 days) and juvenile (45–140 days) T. pacificus tomigrate passively with horizontal currents and an ontogenetic vertical migration (Kim et al., 2015). Paralarvae cansurvive and grow at 15–23°C and the optimum temperature for the juveniles is 10–23°C (Sakurai et al., 1998;
Watanabe et al., 1996).

250

F. Ji & X. Guo

To synthesize these core concepts, an individual-basedmodel (IBM) of the early life stages of T. pacificus wasdeveloped (Ji et al., 2020). The IBM was verified to beable to estimate the stock size of each batch (Jiet al., 2020) so that we could examine the relationshipbetween multiple batch harvestable stock and SIRFL. This
study considered the Japan Sea as the research area
because T. pacificus resources in the Japan Sea have beenstrongly exploited (Kasahara, 1978). T. pacificus is thedominant species in the Japan Sea, and the traditional
fishing method used in the Japan Sea is jigging with apowerful light to attract the squid, leading them to be themajor target of fishing boats that use lights in the Japan
Sea (Kim et al., 2005; Kiyofuji & Saitoh, 2004). Therefore,
selecting the Japan Sea could minimize the underrepresentation of harvestable stock in SIRFL.

Materials and Methods
Given the link between habitat environmental conditions
and growth stages, the ambient environment may imply
the age of the stock at the specific location (Kishi
et al., 2009; Liu et al., 2016). Therefore, the SIRFL data
could be analysed according to age information. The conversion of fishery effort into fishery stock and analysis of
age-dependent environmental preferences required the age
spectrum, which was calculated based on IBM results.

Estimating the age spectrum
IBM (Ji et al., 2020) has been developed to simulate theearly life stage of T. pacificus from 0 to 140 days for individuals before that age that have poor swimming ability(Ikeda et al., 2003) and can be simulated by our passive
migration model. In this model, batches of new individuals
were released into the spawning area every 10 days. Consequently, the number and date of each batch of squid surviving for 140 days, that is, joining juvenile stock, could beobtained. The FAJ (2020) reported that the mortality of
individuals after the early stage ( [ 140 days) was relatively
constant, that is, z* = 0.00582 day1. We assumed thatthe stock from each batch was reduced according to themortality rate for [ 140 days.
Todarodes pacificus aged between 180 days to the days
before spawning, which was assumed to be 360 days were
defined as harvestable individuals (FAJ, 2020). We considered all the surviving individuals within this age range into
account as the ‘harvestable stock’. We used rðt, τÞ which
denotes the amount of harvestable stock at the time tbatched by their hatching time τ (Fig. 1A) and haverðt, τÞ ¼ rðτ þ 140, τÞez ðtτ140Þ for t > τ þ 140,(1)

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VBD Data and Migration Pattern of Oceanic SquidVBD Data and Migration Pattern of Oceanic Squid

Figure 1. (A) The time series of harvestable stock in 2016 given by the IBM model, which is arranged by hatching dates using different colours.
(B) The age spectrum was calculated from (A). The colour represents the stock size (tails of squid) at a specific age (y-axis) on a specific day (xaxis), and the unit is an individual number (result of the model) per day in age per day by date.

where rðτ þ 140, τÞ is the stock size of batch τ at 140 days
after hatching, and its value is derived from IBM. In themodel calculation, rðτ þ 140, τÞ is output every 10 days toreduce the computational cost. We assumed that theamount of hatching per unit time varied linearly within
the 10 days of each interval and used linear interpolation
to obtain rðt, τÞ between the two model outputs.
At any instant t, the total amount of harvestable stock
contains multiple batches with different hatching times,
denoted by τ. Then, the age spectral distribution Sðt, ϑÞ
can be defined as:
Z tt CZ tL
Sðt, ϑÞdϑ ¼
Sðt, tτÞdτ
(2)Rðt Þ ¼
tC

tt L

where ϑ stands for age (days after hatching), t C denotes thecatchable age of squid, which equals 180 days (FAJ, 2020).
t L denotes the lifespan of T. pacificus, which is assumed to

be 360 days. Rðt Þ is the amount of harvestable stock. It isrelatively simple to obtain the age spectrum Sðt, ϑÞ fromthe IBM result because of Sðt, tτÞ ≡ rðt, τÞ, which can becalculated using Equation (1).

Fishing preference
For fishing grounds with the same abundance, the difference in fishery effort invested reflects the preference offishers. We used Attraction of Stock Per Unit Abundance(ASPA) to quantify the preference of fishermen for apotential fishing ground. ASPA may be influenced by several factors, including economic reasons, quality reasonsand individual size. The growth environment may affect
the quality of squids, while the season of sale may affectprices. However, as with Assumption 2, for simplicity,
ASPA is defined as F ðϑÞ, a single-valued function of theª 2023 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.

251

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F. Ji & X. Guo

F. Ji & X. Guomean age of stock, where ϑ denotes the age of the stock.
Without loss of generality, we used a Gaussian formal
function F ðϑÞ:


2 
(3)
F ðϑÞ ¼ B1 exp B2 ϑϑopt
B1 reflects the ratio of stock size to fishery effort, B2
reflects the breadth of fishing preference in age and ϑopt
reflects the optimal age of fishing preference. We conducted a regression analysis to find the undeterminedcoefficients using age data and fishery effort data.
The fishing attraction in the study area, that is, theJapan Sea, is
Z tL
Sðt, ϑÞF ðϑÞdϑ
(4)
Aðt Þ ¼
tC

Given that the resources of T. pacificus in the Japan Sea
have been strongly exploited (Kasahara, 1978), theamount of fishery effort shown by SIRFL is assumed to
be proportional to the attraction in the Japan Sea.
Eðt Þ ¼ KAðt Þ(5)Here, Eðt Þ represents the fishery effort. We used thenumber of boats reported by the VBD in the Japan Sea as
the value of Eðt Þ. K is the scale factor between attraction
and fishery effort, which can be combined into coefficient
B1 . Then we have:
Z tL


2 
E ð t Þ ¼ B1Sðt, ϑÞexp B2 ϑϑopt dϑ(6)variable T h as the habitat temperature, described using alinear model of temperature preference and environmental temperature as follows:
T h ðϑ, t, xÞ ¼ αT adapt ðϑÞ þ βT Sea ðt, xÞ þ β0

(7)

T adapt ðϑÞ represents the temperature preference during
the growth of individuals. Based on Assumption 1, different squid batches may behave differently. However, theirmigration patterns, that is, the reaction to surrounding
environmental conditions, remain consistent within thesame life stage. Therefore, T adapt is a univariate functionof age (ϑ). According to the Ca: Sr. ratio analysis of statoliths by Ikeda et al. (2003), the ambient temperatureexperienced by T. pacificus in the Japan Sea can be modelled as a quadratic function of age, with C 0 , C 1 and C 2
denoting the 0th-, 1st- and 2nd-order coefficients of age
concerning growth temperature, respectively.
T adapt ðϑÞ ¼ C 2 ϑ2 þ C 1 ϑ þ C0

(8)

βT Sea ðt, xÞ þ β0 in Equation (7) represents the impact
of environmental changes on individuals within theregion, where x represents the location of the habitat and
β and β0 are the affine type parameters of the environmental temperature condition of the local habitat.
Applying a spatial average encompassing the Sea ofJapan to Equation (7), we obtainT h ðϑ, t Þ ¼ T adapt ðϑÞ þ βT Sea ðt Þ

(9)

tC

Therefore, we established the relationship between the
known time-series fishery effort Eðt Þ and the age spectrum Sðt, ϑÞ. There are three constants (B1 , B2 and ϑopt )to be determined. Owing to the large interannual variation in stock size, we determined the set of constantsannually. Using the daily VBD data, 365 or 366 samples
were collected from the 2012 to 2019 fishing year todetermine the three coefficients for that year using thenonlinear regression method. In this way, the ASPA
expression F ðϑÞ is obtained for each year, which quantitatively describes the change in fishery preference with theage of the target squid. If the age structure in a certainfishing ground is known, F ðϑÞ gives the transformation
relationship between the stock abundance and fisheryeffort.

Age-dependent most-preferred habitat
temperature (APHT)
Habitat temperature is influenced by two factors, namely
how squid schools select habitat environments throughpositive migration and local environmental change causedby oceanographic processes (Liu et al., 2016). We defined252

where the overbar indicates the spatial average across theJapan Sea. This is to eliminate the influence of the local
temperature. The average habitat temperature selected by
the T. pacificus of age ϑ at time t is represented by T h ðϑ, t Þ,which is linked to its temperature preference during the
growth of individuals across the Japan Sea (T adapt ðϑÞ) andthe spatial average of the upper water temperature in theJapan Sea. In this calculation, α is incorporated into C 2 , C 1 ,C0 and β0 are incorporated into C 0 .
To determine the parameters of the APHT model for
the Japan Sea, we used the model to fit the average ambient water temperature link to the SIRFL-indicated fishingfleets. Given that the selection of fishing grounds isaffected by fishing preferences, the average water temperature of the fishing ground needs to consider the weightingof the fishing preferences by age. This should correspondto the ASPA (F(ϑ) in 2.2), which is the function of theage spectrum (S(t,ϑ)) and ASPA (F(ϑ)):
R tLt Sðt, ϑÞF ðϑÞT h ðϑ, t ÞdϑT ðt Þ ¼ C(10)E ðt Þ
where Sðt, ϑÞ, F ðϑÞ and Eðt Þ have been given in subsections 2.1 and 2.2 respectively, which are knownª 2023 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.

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VBD Data and Migration Pattern of Oceanic Squid

conditions. T ðt Þ is the average temperature of the fishingground which was averaged from the distribution of the
SIRFL and water temperature data at those locations. We
perform regression analysis on the time-dependent average ambient temperature linked to SIRFL to determine
the undetermined coefficients, C 0 , C 1 , C2 and β in
Equation 10.

Habitat temperature-associated chasing
patterns extraction from SIRFL
The schools of squid migrated continuously and theirambient environmental conditions gradually changed.
Therefore, we used a continuous function (Equation 7) toreflect the relationship between habitat temperature andsquid age. Based on Assumption 3, the ambient temperature of fleets reflects the environmental conditions of thetargeted batch of squid during their chasing and catchingphase. In contrast, abrupt changes in environmental conditions suggest a switch of target batch.
We performed a temporal analysis of the vessels’ distribution in VBD and their correlation with ambient temperature. Based on proximity within the temperature–
time state space, vessels were clustered into fleets, which
exhibited distinct ridge patterns as they conducted chasing and catching activities. By identifying persistent ridges
over time, vessels in the VBD dataset were labelledaccording to their respective temperature bands, whichindicated membership within specific fleets.
We assessed the ridges as chasing and caught the movement of the fleet by analysing the gradient of the crestline within a range of 0.2°C/day. The threshold wasdetermined through Equation 7 using parameters from
Table 2 and considered the typical gradient of the polar
front in the Japan Sea (0.03°C/km Park et al., 2004) as
well as the representative migration speed of Ommastrephidae (30 km/day, Gilly et al., 2006). By geographicallymapping the labelled fleets and adjusting with geographic
distance, we obtained the movement pattern of the fleet
during the duration of chasing a batch. The trajectory of
the fleet was approximate to the migratory trajectory of
the fishery targets.

Data
We used the VBD product offered by NOAA’s earth observation group as the data source for our batch-by-batch
analysis of SIRFL (Elvidge et al., 2015). This dataset has
been validated against the Automatic Identification System
and vessel monitoring system data (Elvidge et al., 2018,2022). Within the Japan Sea, the abundance and distribution of VBD have been shown to match effectively withthose of the jigging vessels (Oh et al., 2020). We used the

VBD Data and Migration Pattern of Oceanic Squiddaily VBD data from April 2012 to March 2020 and only
high-feasibility detections (whose quality flag = 1 in
Elvidge et al., 2015) were included in our analysis.
The CPUE data acquired by the fishing synchronous
survey are presented in FAJ (2020).
The temperature or temperature gradient (front
strength) is the most important environmental variable
controlling the migration of Ommastrephidae (Alabia
et al., 2016). We used water temperature data provided
by the JCOPE2M (Japan Coastal Ocean Predictability
Experiment) assimilation system, which uses a multi-scale
3-dimensional variational method (Miyazawa et al., 2017;
Sil et al., 2012). Given the ontogenetic vertical migration,
T. pacificus stays at a depth of approximately 100 m during the daytime and reaches the surface to prey at night(Yamamoto et al., 2007). Therefore, we determined thevertical-averaged temperature at 0–100 m in depth as areference condition for migration.

Results
Age spectrum
Figure 1A shows the harvestable stock size in the 2016
fishing year from 1 April 2016 to March 31, 2017,
arranged by hatching date. We have shown data from2016 as an example year, with other data available in the
supplementary materials. From April to the end of June,the harvestable stock size increased steadily with the addition of multiple batches of individuals. July to September
was the peak period in terms of stock size, during which
the decline and replenishment of stocks were balanced.
From October to the end of December, the stock size
steadily decreased as the autumn spawning cohort entered
the spawning stage and then died. By the end of December, the amount of harvestable stock was approximately
one-quarter of its peak. From January to March of thefollowing year, the winter-spawning cohort entered thespawning stage and then the stock decreased to the minimum size for the entire fishing year. This seasonal variation was consistent with the annual variation in CPUE
obtained from a synchronous fishing experiment by
FAJ (2020) (Fig. 2A).
The age spectrum (Fig. 1B) was created by counting
the squid stock size based on their age, which shows aridge of high value with a slope close to one because ofsynchronous increase in age and time. Given the stock
size decreased proportionally with time, its value waslower on the higher age side than on the lower age sidealong the ridge. New batches were added to the harvestable stock intensively from May to June (Fig. 1B), withthe batch added in June being the largest. These batches
were from autumn-hatched cohorts from November toª 2023 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.

253

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F. Ji & X. Guo

F. Ji & X. Guo

Figure 2. (A) Time series of stock size in the Japan Sea derived from the IBM model (Ji et al., 2020) and that of CPUE (FAJ, 2020). (B) Time seriesof harvestable fishery efforts reflected by SIRFL. (C) Scatter plot between the harvestable stock size from the IBM model and the fishery effort
from SIRFL.

December of the previous year. The batch size of the
newly added harvestable stock from July to October gradually decreased to half of the peak value, which corresponded to the batch size of the winter-hatched cohorts.
The age spectrum reflected changes in the age of theharvestable stock during the year. Most harvestable squids
were younger individuals between 180 and 220 days inJune and July. From August to November, most of thestock comprised individuals over 240 days old. The differences in age composition among months offered thepotential of explaining the variations in fishery effort during the year using age preferences in fishing.

Age preferences in fishing
The harvestable stock size time series estimated by the
IBM model and the observed CPUE showed consistentvariation throughout the year (Fig. 2A). The peak harvestable stock size was reached from July to September
and declined from October until the end of the fishingyear. The lowest stock size period was from December toApril. In contrast, the peak of fishery efforts detected by
SIRFL was recorded from August to September (Fig. 2B).

254

There was a lag of approximately 1.5 months between
harvestable stock and fishery effort.
The fishing age preference model (Equation 6) was
fitted using the time series of harvestable stock size foreach year (Fig. 2A) and the amount of fishery effort indicated by the SIRFL (Fig. 2B). Figure 3A shows the resultsof the age preference model, where the ASPA changes
with the average age of stock of T. pacificus in the Japan
Sea. The parameters obtained for each year are shown in
Table 1.
Parameter B1 represents the proportional coefficient of
the ASPA converted to the fishery effort. There was little
difference in B1 for each year, except for the 2018 fishingyear. This indicates that the fishery effort invested in fishing grounds based on fishing preferences in the Japan Sea
has been relatively stable in most years, and we could
undertake a conversion between stock size and fisheryeffort based on ASPA. In the 2018 fishing year, B1 wasmore than twice as high as in the other years because of
the higher fishery effort in 2018 (Fig. 3C). However, thereason for this is not clear. ϑopt indicates the most popular age at which the most squid was harvested. The optimal age for all years was 310  20 d.

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VBD Data and Migration Pattern of Oceanic SquidVBD Data and Migration Pattern of Oceanic Squid

Figure 3. (A) Variation in ASPA with age. (B) Scatter plot of the estimated fishery effort by fishing preference model and SIRFL (VBD) data. (C)Comparison between the time series of fishery effort indicated by VBD and the fishery effort calculated from the fishing preference model. As an
input variable to the model, the harvestable stock time series is plotted with the green line with referring to the right ordinate.

Table 1. Parameter regression results using the fishing preference
model (Equation 6).
Fishing year\parameters
2012
2013
2014
2015
2016
2017
2018
2019
Fitted for all years

ϑopt

B2

B1
3

4.71 × 10
4.94 × 103
3.02 × 103
4.68 × 1032.82 × 103
3.77 × 103
7.29 × 103
3.73 × 103
3.95 × 103

5

8.97 × 10
8.52 × 1058.37 × 105
8.69 × 105
8.55 × 105
8.27 × 105
9.02 × 1058.82 × 1058.68 × 105323.376331.974302.925
335.336301.856281.994321.427303.544310.299

B1 represents the proportional coefficient of the ASPA converted to
the fishery effort. Parameter B2 determines the decay rate of the
ASPA on both sides of the preferred age group. ϑopt indicates the age
at which the most-preferred squid was harvested.

Parameter B2 determines the decay rate of the ASPA
on both sides of the preferred age group. The averagevalue was approximately 8.5 × 105, and the relative variation was \7% in all years. With conversion, the ASPA
of individuals with an age of 180 days was approximately

0.19 times that of individuals with the age of ϑopt .
Figure 3B and C show the relationship between the fishery effort recorded by the VBD data and the fishery effort
calculated by the fishing preference model based on theestimated values in Table 1. The results have shown thatthe model can effectively reproduce the variation patternof fishery effort recorded in the VBD data.

Habitat transfer with age
Figure 4A shows the variation in T adapt ðϑÞ with the age ofT. pacificus. C 0 in T adapt ðϑÞ also contains a constantowing to environmental changes. The changes in β0 could
not be separated, which may have induced interannual
variability in the baseline of T adapt ðϑÞ. However, the seasonal variation of T adapt ðϑÞ is credible and reflects thetemperature variation caused by autonomous decisionmaking in the migration process.
The APHT model predicts that T. pacificus migrates tocolder regions from 180 days, reaches the coldest region
at approximately 250 days and then migrates to warmer
regions (Fig. 4A).

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255

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F. Ji & X. Guo

F. Ji & X. Guo

Figure 4. (A) Ambient temperature change because of the habitat of individuals (T adapt ðϑÞ) with age. (B) Scatter plot between the APHT model
result and the water temperature recorded on the fishing set. (C) Comparison between the time series of water temperature on the fishing set
indicated by VBD data (blue curve) and water temperature estimated by the APHT model. The orange curve used the same parameters (bottom
line of Table 2) for all years, while the yellow curve used the parameters varied from year to year (Table 2). The purple curve represents theaverage water temperature in the Japan Sea, which is used in the model to represent the environmental effect on the fishing set and has areference shown at the right coordinate axis.

Based on the APHT model and fishing preferences, weused Equation 10 to generate time series of the averagetemperatures for the Japan Sea fishing grounds (Fig. 4C).
The result based on yearly change parameters from Rows
2 through 9 of Table 2 partially replicates the temperaturepattern of the fishing ground, which exhibits a bimodal
structure throughout the year with peaks in May/June
and November, at approximately 14.5°C and a low point
in July/August at approximately 12°C.
Using the same parameters for all years from 2012 to2019 corresponding to the parameter at the bottom line of
Table 2, the APHT model could still reproduce this pattern.
This suggested that the model was stable in different years,
although the stock size and catch changed substantially in

256

those years. The fitness between the model results andsatellite-based observations is shown in Figure 4B and theR-square coefficient of linear regression is 0.388, whichindicated that the model could partially explain the variation in the average temperature of the fishing ground.
The temperature variation in the fishing ground wassignificantly different from that of the average temperature in the Japan Sea (blue curves in Fig. 4C). The average temperature of the upper 100 m layer in the Japan
Sea reached its lowest at approximately 7°C in March–
April and the highest at approximately 15°C in August–October annually, representing a difference of approximately 8°C. This is greater than the temperature change
in the fishing ground.

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VBD Data and Migration Pattern of Oceanic SquidVBD Data and Migration Pattern of Oceanic Squid

Table 2. Results of parameter estimation in the Todarodes pacificus
habitat temperature model (Equation 9).
Fishing year\parameters
2012
2013
2014
2015
2016
2017
2018
2019
Fitted for
all years

C2

C1

C0

β

C 1=2C

0.333 × 1030.290 × 1030.442 × 1030.179 × 1030.539 × 1030.564 × 1030.470 × 1030.534 × 1030.349 × 103

0.164
0.147
0.218
0.087
0.263
0.289
0.241
0.269
0.17528.60028.28134.20320.71039.98144.20538.80042.40530.842

0.355
0.234
0.414
0.203
0.300
0.359
0.337
0.307
0.276

246.25253.45
246.61243.02243.97256.21
256.38251.87250.722

C0, C1 and C2 denote the 0th-, 1st- and 2nd-order coefficients of age
with respect to growth temperature, respectively. β stand for the influence coefficient of the environment on the individual. The last column
denotes the age corresponding to the lowest temperature point.

Fleet tracking using auxiliary informationon ambient temperature
Figure 5A depicts the temporal pattern of fishing vessels
in VBD in 2016. The density of fishing vessels exhibits astrong tendency to accumulate in a specific temperatureband under the temperature coordinate. Despite anapparent temporal discontinuity, the trend of bands’ continuous evolution over time became evident. Figure 5B
illustrates the density ridges that emerged after applying
temporal filtering to eliminate interval disruptions in the
bands.
The ridges in Figure 5B could be divided into threegroups. Group 1 is active in waters between 5 and 10°C,
with ridges appearing at approximately 5°C in May andJune. The density of vessels increased rapidly as the temperature rose to approximately 7°C from August to

Figure 5. (A) Time series of the VBD fishery effort distributed by the water temperature of the fishing set. (B) Same as (C) but after a 30-day
running mean, the highlighted ridges in (A) represent continuous temperature changes where fishery effort has been concentrated. The clusteringof the fishing vessels based on the age-temperature relation and special distance is denoted by a dotted arrow line. Each cluster represents a
potential fleet that chases and catches fishery targets from a batch. The initiation of a chase is denoted by solid dots, while the conclusion of thechase is indicated by arrows.

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257

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F. Ji & X. Guo

F. Ji & X. Guo

Figure 6. The trajectory of ‘fleets’ processed from VBD data. Taking 2016 as an example, the trajectories are divided into four panels according
to the season of occurrence. The solid black line represents the daily trajectory of a cluster fishing boat, which reflects the migration of schools of
Todarodes pacificus. The dot and triangle marks represent the position of the centre of the cluster in the corresponding month. The triangle
marks are the starting positions of the trajectories where the fishing boat began to catch T. pacificus from that school. The dot marks are theposition of the trajectory on the first day of that month. The colour of the markers denotes the specific month. The background contour lines
indicate the average water temperature of the upper 100 m layer.

September. By November, the ridges reached approximately 10°C before gradually dissipating around December. Group 2 formed between the 10 and 15°C isothermin July and August, gradually moved to lower temperatures and merged with Group 1 in October–November.
Group 3 consisted of several ridges that maintained afixed temperature difference, appearing from May to next
January with a temperature variation between 12 and
24°C. This was synchronized with the annual change in258

water temperature around the Tsushima Strait. The vessel
density was highest from August to November.
The movement trajectory of the fleets is depicted in
Figure 6, exhibiting a distinct seasonal distribution. In
spring, the fleets are usually found in three areas, that is,along the Korean Peninsula coast at around 40 degrees
north latitude with temperatures of approximately 5°C,
on the western side of Honshu Island in Japan (10–15°C)
and in the Tsushima Strait ( [ 15°C). Theseª 2023 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.

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VBD Data and Migration Pattern of Oceanic Squidcorresponded to Groups 1, 2 and 3, respectively. Group 3
remained near the Tsushima Strait for four seasons. During summer, fleets from Group 1 moved eastward along
the 7°C isotherm to reach the sea south of Primorsky,
Russia. Group 2 was found near the 10°C isotherm andaround the shelf area located at 130–134°E and 38°N westof Hokkaido, Japan. In autumn, Group 1 shifted southward to merge with Group 2 near the 10°C isotherm,
forming fishing grounds across the northern Japan Sea. In
winter, both groups disappeared.
There was a consistent pattern in the trajectories of all
fleets, characterized by a gradual move towards the cooler
water with a slight angle to the isotherm before making a
sharp turn towards the warmer water that remained forapproximately 2–4 weeks before the chasing activity concluded. This trend was observed across all three groups
during all four seasons, with a particular emphasis on
Groups 1–2. Most fleets examined in this study continued
the chasing phase for 2–3 months.

Discussion
We considered the relationship between fishery lighting
and the stock of squid using a new method. The jigger
interest in squid varied by age, with individuals around
310 days old attracting the most effort, while older andyounger stocks became less attractive. This is consistent
with Murata (1990) that fishermen prefer to catch immature squid of larger size. It is uncertain whether the fishermen acted intentionally, but their long-establishedfishing practices appear to have the preference for squid.
If it is acknowledged that the fleet’s pursuit is agespecific, once a certain batch of squid has been harvested,
the fleet lacks the incentive to accompany the fish
through their life history. It appears more strategic tomove directly to the next fishing ground for potential
fishery stocks. Similarly, the fleet did not track underageschools. Therefore, it is unlikely that the annual movement of the fishing ground where fishing effort is concentrated could reflect the migration trajectories of a cohortof squid throughout their entire life history. However,many studies have depicted the migration by tracking the
movement of the fishing grounds (Murata, 1990; Kiyofuji& Saitoh, 2004; Choi et al., 2008; Wei et al., 2018; Tianet al., 2022).
In our understanding, squid of different batches werecaught at a specific age. The fleet moved and followed themigration of target squid during the chasing and catch
phase, which usually lasted for 2–3 months (Fig. 5B).
However, the catch timing and location of batches varied
according to season, resulting in an annual cycle in thelocation of fishing grounds (Choi et al., 2008; Murata, 1990) Given that T. pacificus hatches throughout the

VBD Data and Migration Pattern of Oceanic Squid

year, likely, the location of fishing moves continuallyalong specific routes.
Our analysis (Fig. 6) has shown that the fleet’s chasing
positions and timing was consistent with the fishinggrounds as summarized by Murata (1990). However, we
did not estimate the migration path according to the orderof these fishing grounds following the methods of Murata (1990), Kiyofuji and Saitoh (2004) (Figures S1 and S2).
T. pacificus in the Japan Sea can be divided into two groups
according to its migration path, initial migration along thecoast of Japan (Tsushima group) and the Korean peninsula(subarctic group) (Kim et al., 2015; Kishi et al., 2009). The
path examined by Choi et al. (2008) likely comprised themigration of the subarctic group. Our results showed thatthe fleets fishing in two distinct groups corresponded to themigration paths identified by Kishi et al. (2009). The fishing grounds for Group 1 were distributed along the path of
the subarctic group, and Group 2 was distributed along thepath of the Tsushima group.
In our results, fleets within a group exhibited their
independent moving trajectories, all of which conformed
to the pattern of moving towards colder waters before
transitioning to warmer areas. This suggested that squid
undergo similar migratory movements during their fishing stage and are likely caught at similar ages. Theseobservations aligned with the assessment of changes ingrowth environments by Ikeda et al. (2003) based onstatolith analysis.
Statolith analysis does not provide the absolute value ofgrowth temperature. Therefore, we estimated the specificrange of the temperature-based APHT model (Fig. 4A).
The temperature gradient experienced by T. pacificus during migration, ranged from 8.5 to 13.5°C, after removing
the environmental background temperature. This is suitable for T. pacificus migrating across the polar front areain the Japan Sea, which has a temperature difference ofapproximately 5°C across the front (Park et al., 2004).
This temperature pattern has been maintained throughout
2012–2019 and was consistent with the pattern presented
by Ikeda et al. (2003).
The calculation of the age spectrum was based on thestock of 140 days-old individuals of IBM (Ji et al., 2020)
and the fixed mortality coefficient, which is consistent
with the statistical calibre of FAJ. To validate the age
spectrum, we used a report of age calculated from statoliths (Sakaguchi et al., 2009) and the average mantle
length of captures (FAJ, 2020, Appendix S2) to estimate
the annual variation pattern of their mean age. The comparison between the age spectrum-based capture age and
FAJ records indicated a close fit; the age distribution
based on statolith is close to our age spectrum in termsof mean age and peak date, providing further support for
our findings (Appendix S1).

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259

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F. Ji & X. Guo

To date, the fisheries effort data in VBD has lacked verification from the northern part of the Japan Sea, which
is analogous to that in the southern part. There were
unusually high values recorded between September andOctober 2018. The high density of vessels observed in theoffshore waters of Primorsky, Russia, north of latitude
42°N in September 2018 accounted for 26% of the total
annual detections of 2018. Likely, this is not a false positive in the satellite data, but rather an unknown eventfulprocess not closely related to the main conclusions of ourstudy.

Conclusions
In this study, a new idea was proposed for processing
satellite-recorded fishery data, which has introduced
information on the habits and age composition of fishingtargets. This information is likely to regulate the habitat
selection of the target fish and the investment of effort
from fishing vessels. Our method helped us to establish alink between the abundance of fishing vessels andOmmastrephidae resources. The results suggested that therelationship between fishing effort and harvestable stock
size could be estimated from the age spectrum, which was
also found to be related to the temperature conditions of
the fishing grounds. The age of the target fish could thenbe inferred based on these temperature conditions.
We used inferred age information from the T. pacificus
targeted based on environmental conditions to cluster
fleets in VBD data. By tracking those fleet movements, we
obtained a likely migration trajectory for the T. pacificus
targeted. This method allowed for the examination of
migration patterns at the batch level, rather than a cohort
level in previous research. This has the potential to facilitate further research on the relationship between environmental conditions and migration patterns. It avoids
anchoring migration trajectories solely based on latitude
and longitude coordinates without considering feedback
to environmental changes. An IBM model with active
migration processes could be developed based on thisstudy to quantitatively analyse how changes in environmental conditions affect the entire life cycle of Ommastrephidae. For instance, the spawner–recruit relationshipvariation of T. pacificus during regime shifts (Nishijima
et al., 2021) may be attributed to alterations in reproductive migration patterns influenced by climate change.

Author Contributions
Conceptualization, Fei Ji and Xinyu Guo; methodology,
Fei Ji and Xinyu Guo; software, Fei Ji.; validation, Fei Ji;
formal analysis, Fei Ji; investigation, Fei Ji; resources, Fei
Ji and Xinyu Guo; data curation, Fei Ji; writing—original260

F. Ji & X. Guo

draft preparation, Fei Ji and Xinyu Guo; writing—review
and editing, Fei Ji and Xinyu Guo; visualisation, Fei Ji;
supervision, Xinyu Guo; project administration, Xinyu
Guo; funding acquisition, Fei Ji. All authors have readand agreed to the published version of the manuscript.

Acknowledgements
We would like to thank the Fisheries Research Agency ofJapan for sponsoring a series of works that share the fishing record and stock data of Japanese flying squid. This
research is supported by the National Natural Science
Foundation of China (42206114).

Data Availability Statement
Daily VBD data can be accessed at https://eogdata.mines.
edu/eog/EOG_sensitive_contents. Resource evaluation ofJapanese flying squid by the Fisheries Research Agency ofJapan can be accessed at https://abchan.fra.go.jp/index.
html. The rest of the data used in this paper including
the result of the IBM model and the age spectrum will be
uploaded to the publisher’s server for open access.
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VBD Data and Migration Pattern of Oceanic SquidSupporting Information
Additional supporting information may be found online
in the Supporting Information section at the end of thearticle.
Figure S1. Schematic representation of the migrationroutes of multiple batches and the fishing grounds.
Figure S2. Schematic representation of the migrationroutes by (A), Kiyofuji and Saitoh (2004) and (B), Kishi
et al. (2009).
Figure S3. Compare between average age of catch (dottedline) and (A) averaged age of harvestable stock estimatedby age spectrum (solid line), (B) averaged age of catches
estimated by age spectrum (solid line).
Figure S4. Age spectrum of T. pacificus in the Japan Sea
from 2012 to 2019. Based on model result.

VBD Data and Migration Pattern of Oceanic Squid

Figure S5. Same of Figure 6 in main text, but for the
result of 2012.
Figure S6. Same of Figure 6 in main text, but for the
result of 2013.
Figure S7. Same of Figure 6 in main text, but for the
result of 2014.
Figure S8. Same of Figure 6 in main text, but for the
result of 2015.
Figure S9. Same of Figure 6 in main text, but for the
result of 2017.
Figure S10. Same of Figure 6 in main text, but for the
result of 2018.
Figure S11. Same of Figure 6 in main text, but for the
result of 2019.

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