Response of the Japanese flying squid(Todarodes pacificus) in the Japan Sea tofuture climate warming scenarios
Fei Ji, Xinyu Guo, Yucheng Wang &Katsumi Takayama

Climatic Change
An Interdisciplinary, International
Journal Devoted to the Description,
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ChangeISSN 0165-0009
Climatic Change
DOI 10.1007/s10584-020-02689-3

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Author's personal copy
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https://doi.org/10.1007/s10584-020-02689-3

Response of the Japanese flying squid (Todarodes
pacificus) in the Japan Sea to future climate
warming scenarios
Fei Ji 1,2 & Xinyu Guo 2& Yucheng Wang

2,3

& Katsumi Takayama

4

Received: 5 November 2019 / Accepted: 26 February 2020/# Springer Nature B.V. 2020

Abstract

The Japanese flying squid (Todarodes pacificus) is an important fishery resource in East
Asia, including Japan, China, and Korea. Recent studies have suggested that it exhibits
significant interannual variation with a close relationship with environmental variables,
among which water temperature has an apparent influence on its spawning, hatching, andgrowth. Therefore, we need to understand how the Japanese flying squid in the Japan Sea(JS) will be affected by global warming. Based on particle tracking methods, we
simulated migration and individual growth of Japanese flying squid from spawning tojuveniles (182 days) in six cases: one for high-level catch years (1960–1966), one forlow-level catch years (2008–2014), and four for the future, including two for 50 years(2056–2062) and two for 100 years from now (2104–2110) under the warming scenario
of RCP2.6 and RCP8.5, respectively. The simulations showed that the number of squidincreased with warming. The main reason for the increase was the warming of the JS,
which increased the survival rate of autumn- and winter-spawning cohorts. In addition,
the model also demonstrated the potential effects of warming on the physiological processof the early life stage of the Japanese flying squid.
Keywords Japanese flying squid . Fishery stock . Global warming . Numerical simulation . JapanSea

1 Introduction
Climate changes have significant effects on marine ecosystems and fishery resources(Blanchard et al. 2012; Barange et al. 2014; Cheung 2018; Moore et al. 2018). As a sub-Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10584-02002689-3) contains supplementary material, which is available to authorized users.

* Xinyu Guo
guoxinyu@sci.ehime-u.ac.jp
Extended author information available on the last page of the article

Author's personal copy
Climatic Changepolar region, the Japan Sea (JS) exhibits more pronounced warming than other regions (Japan
Meteorological Agency 2018). The mean value of the sea surface temperature (SST) in the JS
increased at a rate of 1.08 °C/century from 1891 to 2012, which is twice that of thecorresponding value in the other waters (Wakamatsu et al. 2017). With such significant
environmental changes, the fishery resources in Japan has been reported to have decreasedby ~ 35% from 1930 to 2010, making it one of the largest declines in the world (Free et al.
2019).
The Japanese flying squid (Todarodes pacificus) is an important fishery species in East
Asia. Its catch records are available from the early 1900s in Japan and beginning in the 1930s
in Korea. According to these records, the annual catch exhibited several different phases in thepast 100 years: at a low-abundance phase (less than 200 thousand tons) before the 1940s,
increasing to a high-abundance phase (approximately 400 thousand tons) from the early 1950s
to the end of the 1960s, and decreased gradually from the highest catch of 668 thousand tons in
1968 to the lowest catch of 90 thousand tons in 1986 (Murata 1989). After 1986, the catch ofJapanese flying squid increased again and returned to a high level of ~ 400 thousand tons in the
1990s and the early 2000s (Kidokoro et al. 2010). Recently, the catch from 2010 to 2018 has
shown a new decreasing trend (Fisheries Agency of Japan and Japan Fisheries Research and
Education Agency 2018) and this has widely raised social concerns regarding its future
production.
A climate regime shift was suggested to be the main cause for the variation in the amount ofstock (Sakurai et al. 2000; Kidokoro 2003; Kidokoro et al. 2010; Rosa et al. 2011). Sakurai
et al. (2000) suggested that the change in fishery resources of the Japanese flying squid wascontrolled by the spawning area. Their study showed that the density of paralarvae in thenorthern part of the East China Sea (ECS) and the southwestern part of the JS in autumn had asignificant positive relationship (R2 = 0.91, p < 0.01) with annual catches of the autumnspawning cohort in the JS. The increasing catch of Japanese flying squid since the late1980s is consistent with expansion of spawning grounds for the autumn- and winterspawning cohorts. The direct reason for the expansion of spawning grounds is the increasing
regional water temperature along with a climate regime shift (Sakurai et al. 2000). In addition
to water temperature, the prey conditions are also important to the growth of Japanese flying
squid. Kang et al. (2002) reported a good correlation between annual catch of squid andzooplankton biomass in the previous year.
Using the survival and growth conditions for Japanese flying squid provided by cultivation
experiments (Watanabe et al. 1996; Sakurai et al. 1998), it is possible to develop a numericalmodel to consider the effects of water temperature and prey conditions on Japanese flying
squid. Kishi et al. (2009) proposed a bioenergetics model for Japanese flying squid andcoupled it with a low trophic ecosystem model to assess the change in the production ofJapanese flying squid in relation to global warming. Their model focused on the response ofphysiological processes of squid to global warming; therefore, it fixed the spawning grounds
and migration route of the squid, both of which likely change with global warming because of
their high dependence on water temperature and the flow field. In addition, their model does
not include ontogenetic vertical migration (OVM) and temperature-dependent survival rate.
Kim et al. (2015) used a particle tracking model to simulate the migration route and spatial
distribution of larvae of Japanese flying squid. Their model included OVM and temperaturedependent survival rates but did not consider the physiological processes of squid larvae.
For the JS, the increase rate in annual mean SSTs is ~ 1.71 °C per century in the central partand ~ 1.29 °C per century in the southwestern part in the past 100 years (Climate Change

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Monitoring Report 2017 by the Japan Meteorological Agency, https://www.data.jma.go.
jp/gmd/kaiyou/english/long_term_sst_japan/sea_surface_temperature_around_japan.html).
Moss et al. (2010) proposed four Representative Concentration Pathways (RCPs) for possible
socio-economic development states in the future. RCP2.6 assumes a low level of CO2
emissions that increases the global annual mean surface temperature by 0.3–1.7 °C by theend of the twenty-first century compared with that at the end of the twentieth century. RCP8.5
assumes a high level of CO2 emissions that increase the global annual mean surface temperature by 2.6–4.8 °C. RCP4.5 and RCP6 are between the above two extreme scenarios (Van
Vuuren et al. 2011). Because the current emission rate was reported to be close to that of
RCP8.5 (Peters et al. 2012), we have to meet a situation with a stronger warming intensity than
before. According to the results of climate model intercomparison project (CMIP) 5 under the
RCP8.5, the annual mean SST in the JS will increase by at least 3.5 °C by the end of thetwenty-first century compared with that at the end of the twentieth century (Mizuta et al. 2014).
Against background of reduction in Japanese flying squid catch in recent years anddramatic changes in the seas around Japan over the next 100 years, we used an individualbased model (IBM) to examine the change in spawning grounds and resource of the squid in
the twenty-first century. In our simulation, we coupled the IBM, which contains the physiological processes of Japanese flying squid, with a behavioral-hydrodynamic model that
includes the transport and survival processes of the larvae of Japanese flying squid.
The model structure and the data used are described in Section 2; Section 3 presents thecalculated results; Section 4 discusses the causes for the changes in squid resources in the
future.

2 Model and data
2.1 IBMIn this study, we used a particle to represent a squid larva and released particles in the place
whose water depth and sea surface temperature meet the condition for spawning of squid(Supplementary Material 1). The movement of particles is controlled by ocean current, random
walk, and OVM (Supplementary Material 1). In addition to migration, we also applied an
ontogenetic model to each particle to incorporate the temperature-dependent survival process
and the physiological processes affecting the wet weight of squid (Supplementary Material 1).
The model simulated the early life stage of squid for 182 days. Kidokoro et al. (2014) indicatedthat there was a positive correlation between the number of young squid and the number of
harvestable squid.
The calculation for a year started on January 1, followed by another 36 times when particles
were released for 10-day intervals. Particles released from January to March were defined as
the winter-spawning cohort, from April to June as the spring-spawning cohort, from July to
September as the summer-spawning cohort, and from October to December as the autumnspawning cohort.

2.2 Field data for IBM
To simulate the migration of the Japanese flying squid larvae, we needed water temperatureand ocean current velocity for each case. We also needed the density of zooplankton and water

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temperature as input data for the ontogenetic model for squid. For such purpose, we used the
RIAM ocean model (RIAMOM) (Lee et al. 2003, Hirose et al. 2013, https://dreams-c.riam.
kyushu-u.ac.jp/vwp/) to obtain time-varying, 3-dimensional fields of ocean current velocity,water temperature, and zooplankton density whose daily means were saved in one-day interval
for the use of IBM model. The 3-dimensional field data has a resolution of 1/12° in the zonal
direction, 1/15° in the meridian direction, and 38 layers in the vertical direction and covers the
ECS, the JS, and part of the Kuroshio area south of Japan (Fig. 1).
The RIAMOM was initially designed to simulate the physical variables in the JS (Lee et al.
2003). Recently, it has included a low-trophic ecosystem module for the study on dissolved
oxygen in the JS (Kim et al. 2019). Using RIAMOM and its ecosystem module, we designed
six cases for the calculation corresponding to different climate conditions. Case 1960s was forthe period of 1960–1966, whereas case 2010s was for the period of 2008–2013. Case 1960s
was an example of a high-abundance phase while case 2010s was a low-abundance phase.

Toyama Bay
Wakasa Bay
ToyamaToori
ShimaneTsugaru Strait

Depth<100

Depth>500

Fig. 1 The topography of the area under study. The colored area is within a water depth of 100–500 m, which is apotential area for spawning grounds. If the SST in this area is in the range of 19.5–23 °C, it will become aspawning ground for the squidAuthor's personal copy
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These two cases were designed for comparisons with 4 warming cases. In addition, case 2010s
was also used to compare with the resource data of squid to verify our IBM model. Case
2050s-2.6 and case 2100s-2.6 were based on the RCP 2.6 warming prediction for the period of
2056–2061 and 2104–2109, respectively; case 2050s-8.5 and case 2100s-8.5 were for the
RCP8.5 warming prediction for the period of 2056–2061 and 2104–2109, respectively.
The air-sea boundary conditions in cases 1960s and 2010s were calculated from JRA55(Kobayashi et al. 2015) that provides winds, air temperature, specific humidity, precipitation,
long-wave radiation, and short-wave radiation from 1956 to 2015 with an interval of 3 h. In thewarming cases, except for the air temperature and specific humidity, the other air-sea boundary
conditions were kept the same as those in case 2010s. In two cases of RCP2.6, the airtemperature and specific humidity were increased by 1 °C and 1.5%, respectively; in twocases of RCP8.5, they were increased by 2 °C and 3% in case 2050s-8.5, and by 4 °C and 6%
in case 2050s-8.5, respectively. The lateral boundary conditions and initial conditions for theabove 6 cases were from an ocean model with a coarse grid but a larger domain (northwestern
Pacific Ocean), which has been run continuously from 1958 to 2111 with a similar configuration for air-sea boundary conditions (Kim et al. 2019).

3 Results
In this study, we defined the particle surviving to day 182 (i.e., end of each calculation) as
young squid and their total number in the JS was the young stock or number of young squid.
The model has been validated to be able to reproduce the interannual variations in the numberof young squid in the JS (Supplementary Material 2).

3.1 Spawning of Japanese flying squid
The first effect of ocean warming on the squid in our model is the release of particles at day 0.
It represented the amount of spawning and spawning grounds, which have been used asindexes for examining the change in the common squid resource with SST (e.g., Rosa et al.
2011). The amount of spawning showed a slightly decreasing trend with warming (firstcolumn in Table 1). For example, it decreased by 2.8% in case 2100 s-2.6 and by 5.3% incase 2100 s-8.5 as compared with that of case 2010s.

Table 1 Summary of spawning, migration, survival, and mortality. The budget for Japanese flying squid in the
JS by percentage
Cases

Spawning Migration into Number ofyoung squid
JS (particle(particle(particle
abundance) abundance)
abundance)

Lowtemperature
mortality(%)

Hightemperature
mortality
(%)

Flow
out

Survival toyoung squidstage (%)

1960s
2010s
2050s-2.6
2100s-2.62050s-8.5
2100s-8.5

3.31⨯105
3.21⨯105
3.12⨯105
3.12⨯105
3.12⨯105
3.04⨯105

46.9
49.1
36.9
34.1
32.2
18.8

14.5
16.6
17.5
19.5
17.0
22.0

2.6
1.7
3.6
3.4
3.2
3.3

36.0
32.6
42.0
43.0
47.6
55.9

1.43⨯105
1.33⨯105
1.36⨯105
1.40⨯105
1.36⨯105
1.39⨯105

5.13⨯104
4.34⨯1045.70⨯1046.01⨯104
6.49⨯104
7.79⨯104

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Because the autumn-spawning cohort is the main contributor to the annual stock size, weshow the change of its spawning area with warming (Fig. 2). In case 2010s, spawning occurredalong the shelf area in the ECS and the JS, ranging from north of Taiwan to the south coast of
the JS offshore of Toyama (Fig.2b). In case 1960s, the spawning density in the ECS was higher
than that in case 2010s, whereas that in the JS was lower than that in case 2010s (Fig.2a). For
the warming cases, a decrease in spawning density occurred throughout the spawning grounds.
The reduction of spawning density was larger in case 2100 s than in case 2050s for both RCP
8.5 and RCP 2.6, whereas it was larger in both cases for RCP 8.5 than in those for RCP 2.6.
Spatially, the reduction is greater in the southern area than in the northern area. The largest
reduction occurred in the spawning grounds north of Taiwan. We also show the changing of
spawning areas in other seasons with warming in S-Figs.2-4 in Supplementary Material 3
whose caption gives necessary description on these results.

Spawning grounds of autumn-spawning cohort

(a)

(c)

(e)

1960s

2050s-2.62050s-8.5

N=1.57hh105

N=1.33h105

N=1.29h105

(b)

(d)

(f)

2010s

2100s-2.6

2100s-8.5

N=1.42h105

N=1.31h105

N=1.16h105

Spawning density(Parcles per Grid)Diﬀerence from 2010s(Parcles per Grid)

Fig. 2 Autumn-spawning larvae particles in the six cases. Color in (b) denotes the spawning density (number ofparticles per grid point) in Case 2010s averaged from 2008 to 2014. a, c, d, e, and f are cases 1960s (average of
1960–1966), 2050s-2.6 (average of 2056–2062), 2100 s-2.6 (average of 2104–2110), 2050s-8.5 (average of2056–2062) and 2100 s-8.5 (average of 2104–2110). Color in (a), (c), (d), (e), and (f) denotes the difference
between the respective case and that of Case 2010s. N is the sum of the spawned particles over the entire
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The temporal variation in the amount of spawning also changed with warming (Fig. 3). The
amount of spawning had two peaks. The first peak denoted larvae spawned from late May toJune and became young squid in late November to December. The second peak denoted larvae
spawned in mid-November and became young squid in May of the following year. We called
the first the spring peak and the second the autumn peak in this study.
The spring peak tended to occur earlier with warming. In cases 1960s and 2010s, it was inlate June; in cases 2050s-2.6, 2050s-8.5, and 2100s-2.6, it occurred 10 days earlier and in case
2100s-8.5, it occurred 20 days earlier than that of case 2010s. On the other hand, the autumn
peak tended to be delayed with warming. In case 1960s, the autumn peak occurred in late
October, remaining at a high value from mid-October to early November. Compared with thatof case 1960s, it was delayed by 10 days in cases 2010s, 2050s-2.6, and 2050s-8.5 and by
20 days in cases 2100s-2.6 and 2100s-8.5. In case 2100 s-2.6, the amount of spawning reacheda high level 10 days before the peak. In the case 2100 s-8.5, the amount of spawning
maintained a high value for 10 days after the autumn peak.
The amount of spawning in each season is presented in S-Table 1 in Supplementary
Material 3 as a quantitative summary of Fig. 3. Firstly, there was apparent seasonal variation.
For example, in case 2010s, the amount of spawning in spring and autumn represented the
majority of the annual amount of spawning, accounting for 34% and 44% of the annual
spawning. The winter-spawning cohort accounted for 18%, whereas the amount of spawningin summer accounted for 4%.
The amount of spawning for the winter-spawning cohort increased significantly in warmingcases. Compared with that of case 2010s, the winter-spawning cohort increased the amount of
spawning by 11% in case 2050s-2.6, by 28% in case 2100s-2.6, by 22% in case 2050s-8.5, andby 49% in case 2100s-8.5. In contrast, the amount of spawning in other seasons showed aFig. 3 Average spawning abundance (numbers of particles released) by case, lagged to the spawning date

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reduction with warming. For example, compared with that of case 2010s, the autumnspawning cohort reduced the amount of spawning by 6% in case 2050s-2.6, by 7% in case2100 s-2.6, by 9% in case 2050s-8.5, and by 18% in case 2100s-8.5.

3.2 Young squid stock in the JS
The number of young squid in the JS varied with spawning seasons (Table 2). The autumnspawning cohort had the largest stock size, accounting for ~ 70% of the annual number ofyoung squid in the JS in all six simulations. The spring-spawning cohort was in the second
position, accounting for 20% of the annual number of young squid in case 2010s, which
decreased with warming to 15% in case 2100 s-8.5. The winter-spawning cohort was in thethird position, accounting for ~ 7% of the annual number of young squid in case 2010s, which
increased with warming to 14% in case 2100s-8.5. The summer-spawning cohort made thesmallest contribution, accounting for only ~ 2% of the annual number of young squid in case2010s, which declined to zero in case 2100s-8.5.
The distribution area of young squid showed a northward extension with warming (Fig. 4
and S-Figs. 5-7 in Supplementary Material 3). Being the largest contributor to the annual stock
size, the autumn-spawning cohort was distributed over the entire area south of the polar front
in the JS, west of the Korean coast, and east of the Japanese coast (Fig. 4). The highest densityof young squid was located in the offshore area of Japan, moving northeastward along theJapanese coast with warming. In cases 1960s (Fig.4a) and 2010s (Fig.4b), the highest density
was located in the offshore area of Shimane and Tottori. In cases 2050s-2.6 (Fig.4c) and2050s-8.5 (Fig.4e), the highest density was located in the offshore area near Wakasa Bay andthe southern part of Yamato Rise (latitude, 37.5–40.3° N; longitude, 133.3–135.0° E). In cases
2100s-2.6 (Fig.4d) and 2100s-8.5 (Fig.4f), the highest density was located in the offshore area
near Toyama Bay.
Not only the highest density area but also the northern boundary of the young squid moved
northward with warming. For example, besides those near the Tsugaru Strait, the young squid
were distributed south of 40° N in cases 1960s and 2010s. In cases 2050-2.6 and 2050-8.5, theyoung squid were distributed near the offshore area of the Korean Peninsula around 40° N. In
cases 2100s-2.6 and 2100s-8.5, the young squid were distributed north of 40° N. Except forthe summer-spawning cohort, who could not survive under future warming conditions, theTable 2 Young stock of each seasonal spawning cohort and its annual percentage in all cases
Young stock (annual
Percentage)

Total abundance
WinterSpringSummerAutumn

Cases
1960s

2010s

2050s-2.6

2100s-2.62050s-8.5

2100s-8.5

5.13⨯104
3.20⨯103(6.2%)
1.23⨯104(23.9%)
1.01⨯103(2.0%)
3.48⨯104(67.8%)

4.34⨯104
3.35⨯103(7.7%)
9.52⨯103(21.9%)
1.03⨯103(2.4%)
2.95⨯104(68.0%)

5.70⨯1045.10⨯103(9.0%)
1.13⨯104(19.8%)
6.10⨯102(1.1%)
3.99⨯104(70.1%)

6.01⨯104
6.99⨯103(11.6%)
1.10⨯104(18.4%)
3.73⨯102(0.6%)
4.17⨯104(69.4%)

6.49⨯104
6.20⨯103(9.6%)
1.34⨯104(20.7%)
2.41⨯102(0.4%)
4.50⨯104(69.4%)

7.79⨯1041.12⨯104(14.3%)
1.19⨯104(15.3%)
2.63⨯101(0.0%)
5.48⨯104(70.4%)

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Distribuon of young squid of autumn-spawning cohort

(a)

(c)

(e)

1960s

RCP2.62050s

RCP8.52050s

N=3.48h104

N=3.99h104

N=4.50h104

(b)

(d)

(f)

2010s

RCP2.62100s

RCP8.52100s

N=2.95h104

N=4.17h104

N=5.48h104

Num.
(Grid)

Fig. 4 Distribution of young squid of the autumn-spawning cohort. a, b, c, d, e, and f correspond to cases 1960s,
2010s, 2050s-2.6, 2100s-2.6, 2050s-8.5, and 2100s-8.5. Color denotes the averaged density of young squid over
the years in each case, which is the number of live particles per grid point after 182 days of simulation. N is thesum of the number of particle

cohorts spawned in other seasons (S-Figs. 5-7 in Supplementary Material 3) showing a similar
northward movement as the autumn-spawning cohort (Fig. 4).
To obtain a continuous image regarding how the young squid depended on the spawningtime, we present their total number (= young squid amount) in the JS versus their spawning
time that is 182 days ahead of the time for calculating young squid amount (Fig. 5). Like thespawning peaks in Fig.3, there were also spring and autumn peaks in the number of young
squid amount over a year. For the spring peak, the maximum value of the young squid amount
had no apparent variation among the six simulation cases. For the autumn peak, however, themaximum value of young squid amount increased significantly with warming: ~ 6000 in case2010s, ~ 7000 in case 1960s, ~ 8000 in cases 2050s-2.6 and 2100s-2.6, ~ 9000 in case 2050s8.5, and ~ 12,000 in case 2100s-8.5. The winter-spawning cohort, especially the squidAuthor's personal copy
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spawned in January, apparently increased with warming. In contrast, the summer-spawningcohort could not survive in warming cases.
Table 2 summarizes the results shown in Fig. 5 according to the spawning season. The
winter-spawning cohort had an apparent increase with warming. Compared with case 2010s(low-abundance phase), its stock size was 52% higher in case 2050s-2.6, 85% higher in case
2050s-8.5, 108% higher in case 2100s-2.6, and 233% higher in case 2100s-8.5. Comparedwith case 1960s (high-abundance phase), these values became 59%, 94%, 118%, and 248%,
respectively.
The spring-spawning cohort had no clear increasing trend with warming as did the winterspawning cohort. Compared with case 2010s, future cases showed an increase in the numberof young squid: 19% in case 2050–2.6, 41% in case 2050–8.5, 16% in case 2100–2.6, and
25% in case 2100–8.5. However, compared with case 1960s, the three future cases showed adecrease in the number of young squid: − 8% in case 2050s-2.6, − 10% in case 2100 s-2.6, and− 3% in case 2100 s-8.5. An increase occurred only in case 2050-8.5 (9%). The summerspawning cohort showed a simple response to warming, by going extinct in the future(Table 2).
The autumn-spawning cohort showed an increase with warming. Compared with case2010s, its stock size increased by 35% and 41% in 2050s and 2100 s, respectively, under
RCP 2.6 and by 53% and 86% in 2050s and 2100 s, respectively, under RCP 8.5. Comparedwith case 1960s, the increase was 15% in case 2050s-2.6, 20% in case 2100-2.6, 29% in case2050-8.5, and 57% in case 2100-8.5.
Table 2 also shows a comparison for the change in stock size from the high-abundancephase (1960s), low-abundance phase (2010s), and those from the 2010s to 2050s and from theFig. 5 Average young squid stock (= number of particles surviving for 182 days) in the Japan Sea by case,lagged to spawning date

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2050s to 2100s. The winter- and autumn-spawning cohorts exhibited a consistently increasing
trend with warming. Their increasing range from the 2010s to 2050s and from the 2050s to
2100s under RCP 2.6 and RCP 8.5 were generally larger than the difference between the highabundance phase (1960s) and low-abundance phase (2010s), indicating a larger warming
effect than that previously experienced.

4 Discussions
4.1 The mechanism for the increase in the number of young squid
Our simulations showed that although the number of spawned larvae decreased slightly withwarming in the future, the number of young squid in the JS increased significantly (Table 1).
Here, we present the causes for the increase in the number of young squid in our model.
The number of young squid in the JS depended on the particles spawning in and entering
the JS as well as their survival rate up to the end of each simulation. Figure 6a displays thevariation in the total number of particles, including both alive and dead larvae, inside the JS atthe end of each simulation, which represents the particles spawning in and entering the JS. If
we compare the total numbers of particles in the JS (Fig. 6a) with those of spawned particles(Fig. 3), their ratio is ~ 2/3 for the spring- and autumn-spawning cohorts. Consequently, theautumn peak in Fig. 6a is only slightly higher than that of the spring peak, which cannot
explain the apparent difference in the spring and autumn peaks of young squid amount in Fig.
5. Further, either the spring-spawning cohort or the autumn-spawning cohort did not show aclear change in the total number of particles in the JS with warming (Fig. 6a). Therefore, thechange in the particles spawning in and entering the JS is not the cause of the increase in thenumber of young squid with warming (Fig. 3 and Table 2).
The significant change in the number of young squid among the six cases was caused by
the increased survival rate of larvae in the JS. Figure 6b shows the amount of mortality forsquid larvae inside the JS in all cases, among which the autumn-spawning cohort presented an

Fig. 6 Number of larvae particles inside the Japan Sea at the end of each simulation. a Both alive and deadparticles. b Number of dead particles. Abscissa is spawning time

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apparent change with warming: its peak value decreased and its peak time was delayed. The
spring-spawning cohort advanced its peak time but exhibited little change in total mortality
number (Fig. 6b). In the projected future warming cases, the autumn-spawning cohort hadsignificantly lower mortality than did the spring-spawning cohort, which is the cause why theautumn-spawning cohort had a higher peak value for the number of young squid than did thespring-spawning cohort in warming cases (Fig. 5).
There are three factors that prevented a particle from becoming a young squid inside the JS.
As introduced in Supplementary Material 1, the particle was judged to be dead if the watertemperature surrounding the particle was higher than the high limit or lower than the low limit.
We named them as mortality due to high temperature (MHT) and mortality due to lowtemperature (MLT). In addition to these two factors, the particle can be also transported outof the JS (flow out).
For the particles transported into or spawned inside the JS in the 1960s and 2010s, thewinter-spawning cohort had the lowest loss rate (~ 5–18%), followed by the autumn-spawningcohort (~ 60%), spring-spawning cohort (~ 70–80%), and summer-spawning cohort (> 90%).
MHT was the major factor for the loss of the spring- and summer-spawning cohorts, while
MLT was the major factor for the loss of the autumn-spawning cohort (S-Fig. 8 in Supplementary Material 3).
With warming, the proportion of MHT increased but that of MLT decreased (Table 2). Thischange was greater in both cases for the 2100s than those for the 2050s and greater in cases for
RCP8.5 than those for RCP2.6. As a major reason for the loss of the autumn-spawning cohort,
MLT rapidly decreased with warming, which made the survival rate of the autumn-spawningcohort increase rapidly. This increase alone was able to explain the majority of the increase in
the annual number of young squid. For example, the MLT for the autumn-spawning cohort
decreased from 62% in case 2010s to 31% in case 2100s-8.5. This process increased thenumber of young squid in the autumn-spawning cohort by 3.12 × 104 for case 2100s-8.5,
which explains 90.4% of the increase relative to that of case 2010s (S-Fig. 8 in Supplementary
Material 3).
Pörtner and Knust (2007) mentioned that cold water species at high latitudes usually benefit
from warming. Our simulations suggest that Japanese flying squid in the JS follows this rule.
At least for the next 100 years, the water temperature conditions under the warming scenarios
favor the increase in the number of young squid in the JS.

4.2 Change in temperature experienced by squid larvae under warming conditions
To understand the benefit of warming, it is necessary to examine the temperature experienced
by the squid larvae along their migration route. Variation in the temperature over the next
100 years is also a prerequisite for understanding the response of the squid’s physiologicalprocesses to warming, which will be addressed in Section 4.3.
Using the recorded water temperature at the position of each particle during its calculated
duration of 182 days, we obtained the mean temperature experienced by all the particles thatwere alive at the end of each simulation. We then averaged them according to season in the six
cases based on days passed since release and presented the mean temperature experienced for
an interval of 20 days (Fig. 7). Using case 2010s as an example, the summer-spawning cohort
showed a continuous reduction in its mean experienced temperature, whereas the otherseasonal cohorts experienced a reduction at the beginning of the calculation but then an
increase in their mean experienced temperature. Specifically, the winter-spawning cohort had

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an average release time in February. Its mean experienced temperature began with a value of ~19 °C, dropped to a low value of ~ 16 °C after 80–100 days (May), and then increased to avalue of ~ 18 °C by the end of the calculations (August). The experienced temperature for thespring-spawning cohort began at ~ 20 °C, dropped to ~ 16 °C after 80–100 days (August), andsubsequently increased to ~ 17 °C. That of the summer-spawning cohort began at ~ 21 °C anddropped linearly to ~ 12 °C after 160–180 days (February). The mean release date of theautumn-spawning cohort was in November. Its mean experienced temperature began at ~20 °C, dropped to ~ 12 °C after 120–140 days (March), and increased to ~ 13 °C after 160–
180 days (June).
Three factors were able to explain the variation in mean experienced temperature of larvae
particles. Larvae particles moved northward in all cohorts, and their experienced watertemperature generally decreased with increasing latitude. Seasonal variation in water temperature also affected the temperature experienced by larvae particles. OVM caused larvae
particles to remain at different depths as a response to changes in their life stages. With thechange of depths, the experienced temperature also varied. In the early stage of the simulation,
migration is fast because of the strong current. At the late stage of the simulation, the migration
speed of particles was relatively slow (Fig. 8 and S-Fig. 9 in Supplementary Material 3).

Fig. 7 Time series of average water temperature experienced by individuals. a, b, c, and d correspond to thespawning season of winter, spring, summer, and autumn. The colors from dark blue to light yellow represent
cases from 1960s to 2100s-8.5. Color bar represents the mean of water temperature experienced within 20 days,
whereas the error bar above it represents the standard deviation of the water temperature

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Therefore, the experienced temperature of larvae particles was mainly influenced by thelatitudinal factor in the early stage of simulation, and by the seasonal factor in the later stage.
OVM led to two jumps in the depth of larvae particles on the 20th and 60th days of thesimulation, and the increased depth at the two jumps led to a reduction in the experienced
temperature, which was more apparent in spring and summer than in autumn and winterbecause of the presence of a strong stratification.
In our simulations of warming cases, the increase in the temperature experienced by larvae
particles was less than the increase of mean water temperature in the JS due to warming. Using
the autumn-spawning cohort as an example again, the experienced temperature of larvae
particles was not affected by warming during 0–80 days (Fig. 7d). This was because thenorthward movement of the migration route with warming was generally synchronized withthe warming-induced northward movement of isotherms (Fig. 8). During 80–180 days, theexperienced temperature of larvae particles increased with warming, and the range increased

Fig. 8 The migration route of the autumn-spawning cohort in the six cases. The daily horizontal positions for allparticles that were released in autumn (October–December) and eventually were alive in the Japan Sea wereaveraged to obtain the mean migration route over the 182 days of simulation. The 182 averaged locations are
represented by the black line inside the ellipses, which represent the standard deviation of the distribution ofparticles. The radius of the major axis of the ellipse represents the standard deviation in the direction of maximum
dispersion. The direction of the major axis of an ellipse is the same as that of the maximum variance. The radiusof minor axis of the ellipse represents the standard deviation in the direction of minimum dispersion. The ellipses
were plotted every 30 days along the trajectory. Colors of ellipses are used to distinguish different dates. The
average position of the particles on a day, i.e., center of the ellipse, is represented by points in the same color. The
black lines with number indicate the annual mean temperature for the layer (0–100 m) where the squid larvae
particles existed. The reason we plotted isotherm of 10 °C, 15 °C and 23 °C is because the maximal spawningand surviving temperate is 23 °C and the minimum surviving temperature is between 10 and 15 °C in different
life stage. In addition, 10–15 °C is the OGTR for the early life stage of Japanese flying squidAuthor's personal copy
Climatic Changewith the number of tracked days. However, compared with case 2010s, the maximum increaseof experienced temperature was less than 2 °C in case 2100s-8.5, which was lower than that of
the 4 °C reported by Kishi et al. (2009).
The main reason for the difference in temperature experienced by squid larvae between ourstudy and that of Kishi et al. (2009) was because the IBM simulation in this study presented anorthward shift of the migration route of the squid larvae with warming (Fig. 8 and S-Fig.9 in
Supplementary Material 3), which was however not considered by Kishi et al. (2009) who
used a fixed migration route for the squid larvae.
There are two processes responsible for the northward shift of the migration route. First, theflow and water temperature fields in the JS changed with warming (Kim et al. 2019; Mao et al.
2019), and the enhanced Tsushima Current in the warming cases (S-Fig. 10 in Supplementary
Material 3) brought the particles further to the north than in cases 2010s and 1960s. Second,
the northward shift of the spawning grounds made it easy for particles to follow the northwardcurrents into the JS. For example, more particles from the winter-spawning cohort entered the
JS rather than went to the Kuroshio area in the warming cases.

4.3 Effects of warming on physiological processes and individual weight
Kishi et al. (2009) examined the influences of warming on the physiological processes of theautumn-spawning cohort. Because warming will increase the energy loss of squid throughrespiration, Kishi et al. (2009) suggested that squid wet weight would decrease with warming.
However, our simulations predicted that the weight of young squid would not decrease in thenext 100 years, although we used the model by Kishi et al. (2009) as an individual growthmodule in our simulation system. The different temperatures experienced by squid particles
caused a different effect on the physiological processes and individual weight of the squid.
Table 3 summarizes the mean individual weight and mean life-long metabolic amount ofphysiological processes of the alive larvae particles in the six cases. The weight of squid is low
in case 1960s but high in case 2010s. The weight in four warming cases was slightly lower
than that of case 2010s but was higher than that of case 1960s. In fact, the weight of young
squid of the autumn-spawning cohort increased slightly with warming (S-Fig. 11a in Supplementary Material 3). For example, in the four warming cases, the individual weight was higher
in the two 2100 s cases than in the 2050s cases, in both cases for RCP 2.6, and those for RCP
8.5. Because the number of young squid in the autumn-spawning cohort accounted for ~ 70%of that of the entire year, the change in individual weight in the autumn cohort was the leading
factor in the annual mean individual weight.
Table 3 Mean and standard deviation of life-long physiological processes and individual wet weight, all of them
was averaged across all larvae particles in each case. The percentage represents the proportion of the physiological process to the total consumption in mean value
Cases

Wet weight(g/squid)

Consumption(g/squid)

Respiration(g/squid)

SDA(g/squid)

Egestion(g/squid)

Excretion(g/squid)

1960s
2010s
2050s-2.6
2100s-2.62050s-8.5
2100s-8.5

50 ± 18 (57%)
61 ± 22 (55%)
58 ± 22 (54%)
59 ± 23 (52%)
55 ± 23 (53%)
59 ± 22 (49%)

88 ± 33 (100%)111 ± 43 (100%)109 ± 45 (100%)114 ± 51 (100%)105 ± 49 (100%)
121 ± 53 (100%)

12 ± 6 (14%)
17 ± 9 (16%)
19 ± 11 (17%)
22 ± 13 (19%)
19 ± 13 (18%)
26 ± 17 (22%)

14 ± 5 (16%)
18 ± 7 (16%)
18 ± 7 (16%)
18 ± 8 (16%)
17 ± 8 (16%)
19 ± 8 (16%)

4 ± (5%)5 ± 2 (5%)
5 ± 2 (5%)
5 ± 2 (5%)
5 ± 2 (5%)
6 ± 2 (5%)

7 ± 3 (8%)
9 ± 3 (8%)
9 ± 4 (8%)
9 ± 4 (8%)
8 ± 4 (8%)
10 ± 4 (8%)

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We also examined the mean life-long metabolic amount of physiological processes of thelive larvae particles (Table 3), which helped to elucidate the cause for the change in individual
weight. The proportion of respiration to consumption (ingestion) increased with warming(14% in case 1960s, 16% in case 2010s, 19% in case 2100s-2.6, and 22% in case 2100s-8.5).
Consequently, the proportion of biomass (individual weight) decreased in warming cases
because SDA, egestion, and excretion remained a constant proportion of consumption. This
result was consistent with the estimation by Kishi et al. (2009) who suggested that the energy
loss because of respiration was higher under warming conditions.
However, the individual weight of squid did not necessarily decrease with warming even if
the respiration rate increased, because consumption itself also changed with warming. The
mean consumption roughly increased with warming (Table 3). For example, it increased by
3% in case 2100s-2.6 and 8% in Case 2100s-8.5 compared with that of case 2010s, andincreased by 30% in case 2100s-2.6 and 37% in case 2100s-8.5 as compared with that of case1960s. The increasing trend was clear in the autumn-spawning cohort (S-Fig.11b in Supplementary Material 3).
A detailed analysis on the experienced water temperature and prey density proposed an
optimal growth temperature range (OGTR) of 10 °C and 15 °C for Japanese flying squid at the
young stage (Supplementary Material 4). For the autumn-spawning cohort, even in the most
severe warming conditions (case 2100s-8.5), the water temperature along the larvae migrationroute in the late life stage did not exceed 15 °C, i.e., it was within the OGTR (Fig.7d).
Therefore, ocean warming benefited individual growth in the autumn-spawning cohort, whose
individual weight also showed a slight increase with warming (S-Fig. 11a in Supplementary
Material 3).

5 Conclusions
We simulated the migration and growth of Japanese flying squid from spawning to a young life
stage (182 days) for cases 1960s, 2010s, and 2050s and 2100 s under warning scenarios
RCP2.6 and RCP8.5. Although the amount of spawning particles showed a slight decrease
with warming, the number of young squid in the JS will be higher in the next 100 years than incurrent situations. In the warming cases, the spawning grounds shifted northward, thespawning time of the autumn-spawning cohort was delayed, and the migration routes of squid
shifted northward. This partly offset the negative influences of warming on squid. Along the
migration routes, the maximum temperature increment was less than 2 °C under the warming
conditions. The increase in water temperature increased the survival rate of the autumnspawning cohort and led to a larger stock size of squid in the JS. The warming favored theindividual growth of Japanese flying squid when the temperature experienced by squid was
lower than the OGTR (10–15 °C), but limited the individual growth when the temperature wasabove the OGTR. In the next 100 years, the growth of the autumn-spawning cohort has apossibility to benefit from warming and the individual weight also increased slightly in ourmodel.
It must be noted that our simulation has an essential assumption: we did not consider thechange of parent squid numbers under global warming conditions, which may affect the egg
number, i.e., the number of particles in our study. In addition, our analysis on model results islimited to the particles in the JS. Although it will be a major challenge for the future, themodeling of squid migration inside and outside the JS over its complete life cycle byAuthor's personal copy
Climatic Change

considering the swimming behavior of adult squid is necessary for a better understating of theeffects of global warming on the Japanese flying squid.
Acknowledgements We want to thank Prof. Michio J. Kishi who provided us the individual growth module ofsquid, Prof. Yasunori Sakurai for his advice on our simulations, and Prof. Naoki Hirose at Kyushu University
who support the work of Dr. Katsumi Takayama.
Funding information This study was financially supported by the Environment Research and Technology
Development Fund (S-13) of the Ministry of the Environment, Japan; by the Ministry of Education, Culture,
Sports, Science and Technology (MEXT), Japan, to a project on Joint Usage/Research Center - Leading
Academia in Marine and Environment Pollution Research (LaMer); and by the National Natural Science
Foundation of China (41576010).

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Affiliations
Fei Ji 1,2 & Xinyu Guo 2 & Yucheng Wang 2,3 & Katsumi Takayama 4
1

Key Laboratory of Marine Environment and Ecology, Ocean University of China, Ministry of Education,
238 Songling Road, Qingdao 266100, China

2Center for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-Cho, Matsuyama 790-8577,
Japan

3

Present address: Pilot National Laboratory for Marine Science and Technology, 1 Wenhai Road,
Qingdao 266237, China

4

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