sustainability
ArticleInfluences of Global Warming on the Larval Survival
and Transport of Snow Crab (Chionoecetes opilio) in
the Sea of Japan
Xinyan Mao 1 , Xinyu Guo 2, * , Yucheng Wang 2,† and Katsumi Takayama 3
1
2
3

*
†

Key Laboratory of Physical Oceanography, Ocean University of China, 238 Songling Road, Qingdao 266100,
China; maoxinyan@ouc.edu.cn
Center for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-cho, Matsuyama 790-8577, Japan;
wangyc_qnlm@163.com
Research Institute for Applied Mechanics, Kyushu University, 6-1 Kasuga-koen, Kasuga 816-8580, Japan;
katsumit@riam.kyushu-u.ac.jp
Correspondence: guoxinyu@sci.ehime-u.ac.jp; Tel.: +81-089-927-9824
Current address: Pilot National Laboratory for Marine Science and Technology, 1 Wenhai Road,
Qingdao 266237, China.

Received: 12 March 2019; Accepted: 9 April 2019; Published: 12 April 2019


Abstract: The snow crab (Chionoecetes opilio) sustains an important bottom trawling fishery in the
Sea of Japan. Its response to global warming is attracting the attention of the public. Using atransport and survival model for crab larvae in the Sea of Japan, we examined the spatial-temporalvariations of crab spawning and larval settlement in the past (mid-20th century), present (early
21st century), and future (mid- and late 21st century) under the low and high radiative forcing
scenarios. It was found that the variations in spawning differed between the regions south of andnorth of 41.5◦ N, on both seasonal and long-term scales. Larval settlement in the Sea of Japan was
projected to increase in the future, which is mainly attributed to a reduction in mortality due to the
low water temperature. Moreover, the aggregating location of the settled megalopae will likely shiftnorthward, with increasing settlement off Hokkaido Island. With additional sensitivity experiments,
we confirmed that the change in water temperature has a stronger impact on larval settlement thanthat in the current field. The change in water temperature controlled both the amount and distributionof crab larval settlement, while a change in current field only affected the distribution to some extent.
Keywords: snow crab larvae; survival and transport; global warming; numerical simulation; the Sea
of Japan

1. Introduction
The snow crab Chionoecetes opilio (C. opilio) is distributed in deep cold waters in the northwest
Atlantic Ocean and north Pacific Ocean, including the Sea of Japan. It is an important commercial
fishery species, particularly in Japan, Alaska, and Atlantic Canada [1–5]. Snow crabs hatch between
early February and late April, with a peak in March. After three larval stages (first, second zoea, andmegalopa), larvae settle on the seabed and metamorphose into the first stage benthic crab in June [6].
Female crabs molt eleven times in 7–8 years before they are mature and reproductive. Male crabs
complete nine molts and mature one year earlier than females, but they do not mate until they undergo
the terminal molt between the eleventh and sixteenth molt [7,8]. As the most important species in theoffshore bottom trawling fishery in Japan, the catches of snow crab declined dramatically during themid- to late 20th century, mainly due to overfishing [9]. However, changes in marine environmental
conditions, due to climate change, have also contributed to the decline [10,11].
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Historical observational data have shown a trend in global warming since the second half of the
19th century. Global annual mean surface air temperature has increased at a rate of 0.72 ◦ C/century andsea surface temperature (SST) increased at a rate of 0.53 ◦ C/century [12]. In the projection of the futureclimate, a set of four Representative Concentration Pathways (RCPs) was developed, representing
different socio-economic and emission scenarios [13], including one mitigation scenario leading to avery low forcing level (RCP2.6), two medium stabilization scenarios (RCP4.5/RCP6), and one very
high baseline emission scenario (RCP8.5). Global annual mean surface temperature at the end of the
21st century, compared to that at the end of the 20th century, was projected to rise by 0.3–1.7 ◦ C under
the RCP2.6 scenario and 2.6–4.8 ◦ C under the RCP8.5 scenario. At present, trends in greenhouse gas
emissions are consistent with those assumed in the high emission scenario RCP8.5 [14].
The ecological impacts of global warming have been studied from polar terrestrial to tropicalmarine environments. It is evident that climate change can affect demography, geographic distribution,body size, fishery catch, phenology of populations, and biodiversity of ecosystems [15–22]. Global
meta-analyses indicated significant range shifts averaging 6.1 km per decade towards the poles(or meters per decade upward) and a significant mean advancement of spring events by 2.3 days per
decade [23]. Studies on the responses of ocean ecosystems to future climate change include projections
on primary production [24], species distribution, and abundance at local [25,26] to global [27,28] scales
under different emission scenarios.
Climate change in Japan due to global warming is inducing serious concerns among the Japanesepublic and political sectors [29,30]. It was reported that the annual mean surface air temperature overJapan has increased at a rate of 1.19 ◦ C/100 years, faster than the global average [31]. Annual mean air
temperatures in Japan were projected to rise by 1.1 ± 0.4 ◦ C and 4.4 ± 0.6 ◦ C under the RCP2.6 and
RCP8.5 scenarios at the end of the 21st century, relative to the end of the 20th century [32]. Although
the warming impacts on the fishery species around Japan were mentioned in the report from the
Ministry of Agriculture, Forestry and Fisheries [33], little information exists on the snow crab. Past
warm phases provide a preview of the future, in which C. opilio in the Sea of Japan will shift northward
and the resource decline will continue [34,35].
In this study, a survival-transport model was applied to clarify the impact of climate change
on snow crab larvae in the Sea of Japan. We focus on future responses, but also summarize thechanges simulated in the second half of the 20th century as a reference. We were not concernedwith organismal-level physiological changes, individual-level behavioral changes, or ecosystem-levelfood-web-interaction changes, although all four were apparent and interlinked [36].
The paper is organized as follows: Section 2 introduces the model configuration. Section 3
describes changes in spawning and larval settlement in response to global warming. There is a brief
discussion on the impacts of hydrodynamic factors in Section 4. Conclusions are presented at the end.
2. Model Structure
2.1. Model Descriptions
A survival and transport model was developed for snow crab larvae in the Sea of Japan (Figure 1a).
The model included the process of releasing virtual particles from spawning grounds, the tracking andsinking processes during larval advection, accompanied by judging their survival status, and the final
process of larval settlement on the seabed, with an additional diurnal vertical migration for the second
zoea [6]. The model can reproduce the distribution of crab larval drift and settlement on the seabedfrom 1999 to 2013 [37].

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FigurethetheSeaSeaof Japan;(b) (b)Possiblespawningsites sitesof snow
crab,
Figure 1.
1. ModelModeldomain.
domain.(a)(a)Bathymetry
Bathymetryinin
of Japan;Possiblespawningof snow
determined
by
water
depth
in
the
range
of
200
m
to
500
m.
crab, determined by water depth in the range of 200 m to 500 m.

The
The 3-D3-D flow
flow field
field was
was indispensable
indispensable inin thethe particle
particle tracking
tracking process,process, while
while water
water temperaturetemperaturewasnecessaryforthespawning
processandsurvival
judgement.
In
thisstudy,
we usedused thethe flowflow
was necessary for the spawning process and survival judgement. In this study, we
field
and waterwatertemperaturetemperaturedata
datafrom
fromdaily
dailyreanalysis
reanalysis
datasets
Data
Assimilation
Researchoffield anddatasetsof of
Data
Assimilation
Researchof the
the
East
Asian
MarineSystem(DREAMS)[38].
TheDREAMS
systemhas
two
sub-models
withaEast Asian Marine System (DREAMS) [38]. The DREAMS system has two sub-models with a low
lowresolution,one
covering
almosttheentirePacific
Ocean
and
a
high
resolution
one
coveringtheresolution, one covering almost the entire Pacific Ocean and a high resolution one covering thenorthwestern
Pacific Ocean.
The low-resolution
low-resolution sub-model
sub-model was
was runrun forfor 150150 years
years fromfrom 1960
1960 toto 2100,
2100,
northwestern Pacific
Ocean. The
whose
results
providethelateralboundary
conditions
andinitialconditionsforthehigh-resolution
whose results provide the lateral boundary conditions and initial conditions for the high-resolution
one.
of of
thethehigh-resolution
sub-model
was 5’
in longitude
and 4’ inlatitude.
one. TheThe horizontal
horizontalresolution
resolution
high-resolution
sub-model
was5’ in longitude
and
4’ in
There
were
40
layersintheverticaldirection
(1
m,
3.5
m,
6.5
m,
10
m,
15
m,
22
m,
30
m,
39
m,
50 m,
latitude. There were 40 layers in the vertical direction (1 m, 3.5 m, 6.5 m, 10 m, 15 m, 22 m, 30 m,
39
64
m,m,
120
m,m,
. . .120, 5674m).5674 m).
m, m,
50 81
m, m,
64 100
m, 81
100
m, …,
Without
impactof femalecrab crab
abundanceon theon
distributionof spawns,
a uniforma
Without regard
regardfor
forthethe
impactof femaleabundance
the distributionof spawns,
distributionofreleasedparticleswasadoptedastheinitialconditioninthesimulation.
Specifically,
uniform distribution of released particles was adopted as the initial condition in the simulation.
25
particles wereregularly
distributedin the surface
layerof thegridofwith
a waterrangingSpecifically,
25 particles
wereregularly distributedin thesurfacelayer
the grid
withdepth
a waterdepth◦◦
between
200
m
and
500
m.
If
the
SST
of
the
grid
fell
within
the
range
of
5
C–16
C,
the
particles
ranging between 200 m and 500 m. If the SST of the grid fell within the range of 5 °C–16 °C, thewerereleased
first zoea.
Thezoea.
spawningsites in the
Sea
were
dividedinto eightregions
particles
were asreleased
as first
The spawning
sites
inof
theJapanSea ofJapan
were divided
into
eight
(Figure1b).
Regions
A,
B,
and
C
were
importantbottomtrawling
fishery
grounds
for
snow
crab,
regions (Figure 1b). Regions A, B, and C were important bottom trawling fishery grounds for snow
respectively
locatedwestofToyamaPrefecture,
westofNiigata
Prefecture,
and
west
of
Hokkaido
crab, respectively located west of Toyama Prefecture, west of Niigata Prefecture, and west ofPrefecture
RegionD waslocatedeast locatedof the Korean
E wasRegion
at Yamato
Bank.
Hokkaido [39].
Prefecture[39].
RegionD waseast ofpeninsula.
the KoreanRegion
peninsula.
E was
at
◦
Regions
F
and
G
were
north
of
46
N,
along
the
east
and
west
coast,
respectively.
The
last
spawning
Yamato Bank. Regions F and G were north of 46°N, along the east and west coast, respectively. The
site,
located between
40◦ N and46◦ N40°N
alongandthe 46°N
west coast
thewestSea ofJapan,
wasSeanamed
regionwas
H.
last spawningsite, locatedbetweenalonginthe
coast
in theof Japan,Previous
studieshavereported
that
snow
crab
spawning
generallyoccursinspring[6].
However,named region H.
we do
not know
how theseasonrespondto global
warming.
It waspreviously
Previous
studieshavehatchingreported
that would
snow crab
spawning
generallyoccursin spring[6].
reportedthatclimatechangeaccounted
for
37%
of
a
trend
towards
earlier
egg-laying
inbreeding
However, we do not know how the hatching season would respond to global warming.
It wasbirds[40]. Consequently,
assumedin ourstudy thatprocesscould
occur
at any timepreviously
reported that we
climatechangeaccounted
for the37%spawningof a trend
towards
earlier
egg-laying
in
of
year,
if
SST
was
proper
for
first
zoea
spawning.
Tobespecific,37
release-drift
eventswere
simulated
breeding birds [40]. Consequently, we assumed in our study that the spawning process could
occur
for
a given
Theiffirstbegan foron 1first
January,
with subsequent
events initiated
10 daysevents
apart.
at anytime year.
of year,
SST eventwas proper
zoea spawning.
To be specific,
37 release-drift
Eachevent lasted
120 days.
zoea,
and megalopa
stages wereset at30, 30,were simulated
forfora givenyear. TheThe first
first zoea,
event second
began on1 January,
with subsequent
eventsinitiated
and
60
days,
respectively[6,41,42].
Allhydrodynamic
data
were
updated
daily
and
were
linearly
10 days apart. Each event lasted for 120 days. The first zoea, second zoea, and megalopa stages wereinterpolated
frequencies
using
a time step
3600 s.
set at 30, 30,into
andhourly
60 days,respectively[6,41,42].
All ofhydrodynamic
data were updated daily and◦ C for the zoea stageDuringlarvaladvection,
thesurvival
temperature
limitswere
setat
5 ◦ C–16
were linearly interpolated into hourly frequencies using a time step of 3600s.
◦◦(first During
60 days larvalin eachadvection,
cycle) andthe
5 C–14C for
the megalopa
stage(second
If onesurvival
temperature
limitswere
set at60
5 days)°C–16[41].
°C forthe larva
zoea
stayedoutofthepropertemperature
range
for
24
consecutive
hours,
it
would
“die”
and
was
removedstage (first 60 days in each cycle) and 5 °C–14 °C for the megalopa stage (second 60 days) [41]. If one
fromsimulation.
Two
conditions
were setrange
for theto settle onhours,
the seabed.
The“die”
first required
larva thestayedout of thepropertemperatureforlarvae
24 consecutive
it would
and wasthatthelarvaehad
beentransported
forlonger
than
90
days,
meaning
that
they
have
developed
intoremoved from the simulation. Two conditions were set for the larvae to settle on the seabed. The firstrequired that the larvae had been transported for longer than 90 days, meaning that they havedeveloped into the late megalopa stage and had prepared to settle on the seabed. The second

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the late megalopa stage and had prepared to settle on the seabed. The second condition required thatthe larvae be hyperbenthic and located at an area with a water depth between 200 m and 500 m.
2.2. Model Setup
2.2.1. Control Group
There were six numerical experiments of larval models in the control group, covering from past
to future. They were all driven by the hydrodynamics of DREAMS datasets [38]. DREAMS was
simulated in three climate periods, the past, present, and the future. The future period consisted ofdata at the middle and end of the 21st century under the RCP2.6 and RCP8.5 scenarios. The air-sea
boundary conditions for the 1960s and 2010s were from the JRA55 dataset [43,44]. The change in air-sea
boundary conditions for simulation in four future situations was a 1.0 ◦ C increase of air temperature in
RCP2.6 2050s, a 2.0 ◦ C increase of air temperature in RCP8.5 2050s, a 1.0 ◦ C increase of air temperaturein RCP2.6 2100s, and a 4.0 ◦ C increase of air temperature in RCP8.5 2100s. Therefore, there was a
total of six subsets in the DREAMS datasets, each of which contained ten years of daily data (Table 1).
The larval simulation in the control group was forced to the last five-year hydrodynamic data fromeach subset because of high uncertainty in the spin-up phase of DREAMS during the first several years.
Table 1. Name of larval simulation in the control group and relevant DREAMS information.
Years for
Running
Larval Model

Name of Larval
Experiments in theControl Group andDREAMS subset

PeriodEra

Years inDREAMS

Past

Mid-20th century

1957–1966

1961–1965

1960s

Present

Early 21st century

2005–2014

2009–20132010s

Mid-21st century

2053–2062

2057–2061

FutureRCP2.6 2050s
RCP8.5 2050s

RCP2.6

RCP8.5
End 21st century

2101–2110

2105–2109

RCP2.6 2100s
RCP8.5 2100s

2.2.2. Sensitive Groups
The sensitive simulation is structured to remove either the change in water temperature orthat in flow field among different eras. Therefore, there are two sensitive groups, “Tmodern” and“UVmodern”, each of which includes one past and four future experiments (Table 2). In the “Tmodern”
group, all the experiments are forced by the same 2010s (referred to “modern”) temperature (referred
to as ‘T’) but a different flow field corresponding to one past and four future situations. On the other
hand, all the experiments in group “UVmodern” are forced by different temperatures, but the same2010s (referred to as “modern”) flow field (referred to as “UV”). As for the experiment name, “T” isshort for water temperature; “U” and “V” are two horizontal components of velocity vector; and “mdn”
is short for “modern” (2010s subset). Take “Tmdn RCP8.5 2050s” as example, it means the experiment
was forced by water temperature from 2010s and flow field from the RCP8.5 2050s subset.

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Table 2. Experiments in two sensitive groups and relevant subset of hydrodynamic data.
Sensitive group

Tmodern

UVmodern

Experiment Name

Temperature Data

Flow Field Data

Tmdn 1960s

2010s

1960s

Tmdn RCP8.5 2050s

2010s

RCP8.5 2050s

Tmdn RCP8.5 2100s

2010s

RCP8.5 2100s

Tmdn RCP2.6 2050s

2010s

RCP2.6 2050s

Tmdn RCP2.6 2100s

2010s

RCP2.6 2100s

UVmdn 1960s

1960s

2010s

UVmdn RCP8.5 2050s

RCP8.5 2050s

2010s

UVmdn RCP8.5 2100s

RCP8.5 2100s

2010s

UVmdn RCP2.6 2050s

RCP2.6 2050s

2010s

UVmdn RCP2.6 2100s

RCP2.6 2100s

2010s

2.3. Climate Change Trend in DREAMS
The annual mean water temperature in the upper 200 m, where crab larvae reside, is presented in
Figure A1. Water temperature in the Sea of Japan was found to consistently increase over time. It wasestimated to warm by 0.6 ◦ C from the 1960s to 2010s, and to continue rising by 1.4 ◦ C and 2.8 ◦ C under
the RCP2.6 and RCP8.5 scenarios by the end of the 21st century. As for the flow field, a triple-branchpattern was observed in the flow field for the Tsushima Warm Current (TWC) (Figure A2a). The current
path along Honshu Island represented the first branch of the TWC (FBTWC), the path along thecontinental shelf edge was the second branch (SBTWC), and the path along the east coast of the
Korean peninsula represented the third branch (TBTWC) [45]. SBTWC and TBTWC met at (132◦ E,
37.5◦ N), flowed northeastward, and then joined with FBTWC at (139◦ E, 40◦ N). The TWC in the paststrengthened slightly for only two branches, FBTWC and SBTWC (Figure A2b). However, in the futuregroup, the TWC strengthened by 30%–50%, especially for the TBTWC, which reached as far as 40◦ N.
As a result, the meeting location of SBTWC and TBTWC shifted to north of 38◦ N and, afterwards, thejoint current was also reinforced and shifted further north than in the present. Moreover, the northward
flow off Hokkaido moved shoreward in the past and future when compared with the present.
3. Results
3.1. Spawning of Snow Crab
Spawning numbers showed distinct seasonal variation, with much higher numbers from December
to May than in other months and a peak in May (Figure 2b). In midsummer, spawning can only occur
in regions F, G, and H, at quite low levels (Figure 2f,h). Two types of seasonal variation could be
distinguished in the spawning sites. The variations in spawning sites south of 41.5◦ N (regions A, B, D,
and E) demonstrated that particles were released at the maximum level from January to May and thendramatically decreased until November (Figure 2, left panel). However, at the sites north of 41.5◦ N(regions C, F, G, and H), two spawning peaks were identified in May–June and October–November(Figure 2, right panel).
The seasonal variation in crab spawning was fully dependent on the spatio-temporal variationsof SST. We therefore classified the possible spawning sites identified in Figure 1b by SST as
always/sometimes/never falling in the range of 5 ◦ C–16 ◦ C (Figure 3). In the 2010s, the highest
spawning levels occurred in May, as indicated in Figure 3 by the limited blue area that month.
In August, the particles could only be released in regions G and H, where SST sometimes fell in theproper range. The two spawning peaks in regions north of 41.5◦ N could be distinguished easily due
to the larger area covered in red during May and November.

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Figure2.
time-seriesof spawninglevelsin sixof thegroup.
FigureAnnualtime-seriesof spawninglevelsinexperiments
six experimentsof control
the control
group.7 of 19

The seasonal variation in crab spawning was fully dependent on the spatio-temporal variationsof SST. We therefore classified the possible spawning sites identified in Figure 1b by SST as
always/sometimes/never falling in the range of 5 °C–16 °C (Figure 3). In the 2010s, the highest
spawning levels occurred in May, as indicated in Figure 3 by the limited blue area that month. In
August, the particles could only be released in regions G and H, where SST sometimes fell in theproper range. The two spawning peaks in regions north of 41.5°N could be distinguished easily due
to the larger area covered in red during May and November.

Figure3. Seasonalvariationof SST
the SST
range
classificationin 2010s.
(a) February;(b) (c)May;(c)Figure3. Seasonalvariationof therange
classificationin 2010s.
(a) February;(b) May;
August;August;and (d) November.
dots indicate
that
thein
SST
in thosegrids
alwayswithinthe
therange
range ofand(d) November.
Red dotsRed
indicatethat the
SST
those
grids
alwaysfellfellwithin◦ C; brown
5 °C–16°C;
brown
indicates
SST
may
havebeen
beenout
outof
of range
range inin somethethe5 ◦ of
C–16
indicates
thetheSST
may
have
someyears,
years,andandblue
bluemeans
means
never
the
rangeduring
during2009–2013.
2009–2013.
SSTSSTnever
fellfell
in in
the
range

When comparing seasonal variations among experiments, minimal decline was found in theregions south of 41.5°N in the future (Figure 2, left panel). In contrast, spawning in region C
increased remarkably in future spring, with larger increments under the RCP8.5 scenario (Figure2d). In regions F, G, and H (Figure 2f and h), the spawning peak in spring shifted forward, while thatin autumn shifted backward, showing the phenological effects of warming [19]. For example, theFigure 3. Seasonal variation of the SST range classification in 2010s. (a) February; (b) May; (c)August; and (d) November. Red dots indicate that the SST in those grids always fell within the range
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2198 indicates the SST may have been out of range in some years, and blue means the7 of 17
of 5 °C–16°C;
SST never fell in the range during 2009–2013.

When comparing seasonal variations among experiments, minimal decline was found in the
When comparing seasonal variations among experiments, minimal decline was found in theregions south of 41.5◦ N in the future (Figure 2, left panel). In contrast, spawning in region C increased
regions south of 41.5°N in the future (Figure 2, left panel). In contrast, spawning in region C
remarkably in future spring, with larger increments under the RCP8.5 scenario (Figure 2d). In regions
increased remarkably in future spring, with larger increments under the RCP8.5 scenario (Figure
F, G, and H (Figure 2f,h), the spawning peak in spring shifted forward, while that in autumn shifted2d). In regions F, G, and H (Figure 2f and h), the spawning peak in spring shifted forward, while thatbackward, showing the phenological effects of warming [19]. For example, the spring peak in region H
in autumn shifted backward, showing the phenological effects of warming [19]. For example, theoccurred on 10 June in the 1960s and was advanced 10 days in the 2010s and another 10 days/30 days
spring peak in region H occurred on 10 June in the 1960s and was advanced 10 days in the 2010s andin the 2100s, under the RCP2.6/RCP8.5 scenarios (Figure 2h).
another 10 days/30 days in the 2100s, under the RCP2.6/RCP8.5 scenarios (Figure 2h).
Figure 4 presents the quantitative estimations of spawning amounts in the six experiments. As the
Figure 4 presents the quantitative estimations of spawning amounts in the six experiments. As
ocean becomes warmer in the future (Figure A1), the area of SST > 16 ◦ C in the southern region would
the ocean becomes warmer in the future(Figure A1), the area of SST > 16 °C in the southern region
be larger, as would the area of SST > 5 ◦ C in the northern region. This resulted in different spawning
would be larger, as would the area of SST > 5 °C in the northern region. This resulted in different
trends between regions south and north of 41.5◦ N. Additionally, the variation in spawning amounts
spawning trends between regions south and north of 41.5°N. Additionally, the variation in
was more significant under the RCP8.5 scenario than RCP2.6. For example, the spawning amount inspawning amounts was more significant under the RCP8.5 scenario than RCP2.6. For example, theregion A was reduced by 23% in RCP8.5 2100s, but 6% in RCP2.6 2100s.
spawning amount in region A was reduced by 23% in RCP8.5 2100s, but 6% in RCP2.6 2100s.

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Figure 4. Annual spawning amount in different regions among six experiments.
Figure 4. Annual spawning amount in different regions among six experiments.

Unexpectedly, the spawning amount in the 1960s was higher than today in most regions.
Unexpectedly, the spawning amount in the 1960s was higher than today in most regions. The
The seasonal variation depicted in Figure 2 indicated a distinct increase in November in regions A andseasonal variation depicted in Figure 2 indicated a distinct increase in November in regions A and B
B and in August–September in regions F and G. A comparison of SST range classifications between theand in August–September in regions F and G. A comparison of SST range classifications between the2010s and 1960s (Figure 3c,d and Figure 5a,b) provides clarification for these unexpected results. More
2010s and 1960s (Figure 3c–d and Figure 5a–b) provides clarification for these unexpected results.
particles were released from region F in August and from region A in November in the 1960s than inMore particles were released from region F in August and from region A in November in the 1960s
the present period.
than in the present period.

Figure 5. Same as Figure 3, but for the 1960s. (a) August and (b) November.
Figure 5. Same as Figure 3, but for the 1960s. (a) August and (b) November.

3.2. Larval Settlement
After release, the crab larvae were advected by the cyclone-like circulation in the Sea of Japan,
mainly consisting of the Liman Current, the North Korea Cold Current, the Ulleung Eddy, and TWC

Sustainability 2019, 11, 2198

8 of 173.2. Larval Settlement
After release, the crab larvae were advected by the cyclone-like circulation in the Sea of Japan,
mainly consisting of the Liman Current, the North Korea Cold Current, the Ulleung Eddy, and TWC [46],and sank. In the later megalopa stage, some larvae settled on the seabed off Honshu and Hokkaido
Islands (regions A, B, and C; Figure 6). The fewest number of larvae settled in the 2010s. The settlingrate, i.e., the ratio of settled larvae number to initially released number, was also the lowest one in the2010s. Among the settlement, 99% occurred in regions A, B, and C. However, in RCP8.5 2100s, thispercent decreased to 88.5% because some megalopa settled on the seabed north of 46◦ N in region F,
while others settled along the coast in region H and a few settled in region E (Figure 6d). Additionally,
the gathering site of settled megalopa shifted northward as climate changed. The highest settlement
proportion occurred in region A in the 1960s, but shifted to region B in the 2010s. From then on,
region B exhibited a steady decline. As a result, the settlement proportion in region C increased by
Sustainability 2019, 11, x FOR PEERinREVIEW
9 of 1927%(RCP2.6)–45%(RCP8.5)
the 2100s.

Figuretotal
settledlarvae(shading)
in sixin
experiments
and zonal
The percent
Figure6.6.Distribution
Distributionofof
total
settledlarvae(shading)
six experiments
andstatistics.
zonal statistics.
The
after
the
settlednumber
in
the
top
left
is
the
settling
rate;
thepercent
after
the
regionisthesettlement
percent after the settled number in the top left is the settling rate; the percent after the regionis theproportion
that region.
(a) 2010s;(b)(a)
1960s;(c) (b)
RCP8.52050s;(d) RCP8.5
2100s;(e) RCP2.62050s;
settlement ofproportionof thatregion.
2010s;
1960s;(c) RCP8.52050s;(d) RCP8.5
2100s;(e)and(f)
RCP2.62100s.
RCP2.6 2050s; and (f) RCP2.6 2100s.

The effect of different RCP scenarios on crab larval settlement was much more evident in Figure
7. Settlement in regions A and B was lower under the RCP8.5 scenario than RCP2.6, in response tomuch warmer ocean temperatures under the high GHG emission scenario. However, settlement was
highest in RCP8.5 2100s, mainly due to settlement in region C. Region C was also responsible forsimilarly high levels of total settlement in the 2050s, under two different RCPs. However, settlement
in region F and other regions was only evident in RCP8.5 2100s.

Sustainability 2019, 11, 2198

9 of 17

The effect of different RCP scenarios on crab larval settlement was much more evident in Figure 7.
Settlement in regions A and B was lower under the RCP8.5 scenario than RCP2.6, in response to much
warmer ocean temperatures under the high GHG emission scenario. However, settlement was highest
in RCP8.5 2100s, mainly due to settlement in region C. Region C was also responsible for similarlyhigh levels of total settlement in the 2050s, under two different RCPs. However, settlement in region F
and other regions
only
evident
in RCP8.5 2100s.
Sustainabilitywas
2019, 11,
x FOR
PEER REVIEW
10 of 19

7. Annualsettlement
amountinin different
different regions
among
six experiments.
Figure 7.Figure
Annualsettlement
amountregionsamongsix experiments.

In addition to the departure from the Sea of Japan through the Tsugaru Strait and the Soya

In addition
to the departure from the Sea of Japan through the Tsugaru Strait and the Soya Strait,
Strait, four final states of the larvae were identified, i.e., death due to low or high temperatures,four final stateslarvaeorwere
identified,i.e., deathor experiments,
high temperatures,
drifting ofin thethe ocean,settlementon the seabed(Table due
3). Into
thelow
futureincreased drifting
settlement
resultedfromadeclineinthelow-temperature(<
5°C)
mortality
rate.
A
1%–3%
decreasein the ocean, or settlement on the seabed (Table 3). In the future experiments, increased
settlementinmortalitycanresult
in
a
1–2
time
increaseinsettlement
amount.
Spatialdistribution
analysis◦
resulted from a decline in the low-temperature (<5 C) mortality rate. A 1%–3% decreasein mortality
indicated that low-temperature mortality drastically decreased in the southern Sea of Japan (typical
can result area:
in a 134–136°E,
1–2 time 36–38°N)
increaseandin settlement
amount. Spatial distribution analysis indicated thatin northwest Hokkaido in the future experiments (Figure 8). For
low-temperature
drasticallyin the southern
ofsouthern
Japan (typicalarea:was134–136◦ E,
example,mortality
as shown in
Figure A3, decreased
water with temperature> 5 °C Sea
in theSea of Japan◦
more inabundant
in RCP2.6
2050s than
2010s. experiments
A similar discrepancyin 8).
water
temperature
36–38 N) andnorthwest
Hokkaido
in inthethefuture(FigureFor
example,was
as shown infor low-temperature(FigureA3c
Figure8e).more abundant in RCP2.6
Figure A3,observed
water withtemperature mortality>5 ◦ C indistribution
the southern
Sea
ofand
Japan
was
2050s than in the 2010s.
A similar
discrepancy
in water
wasobserved for low-temperatureTable
3. The numberof released (initial)
and temperatureending particles insix experiments.
mortality distribution (Figures A3c and 8e).
Settlement
Low-temp.
High-temp.
InitialDrifting
SettledRatio of
Experiments
Mortality
Table 3. The
number ofMortality
released (initial)and ending RateparticlesNumber
in six experiments.
NumberOthers to
Rate
Rate
2010s
High-temp.
1960s Initial
3.11 × 106 Low-temp.
85.5%
4.2%
6.6%
1.36 ×Settled
104
2.7 Settlement
Drifting
Experiments 2010s
Mortality
Ratio of Others
6 Mortality
4
2.95
×
10
86.6%
4.5%
5.7%
0.50
×
10
1.0
NumberRate
NumberRate
Rate
to 2010s
RCP2.6
2.97
× 106
83.2%
4.7%
7.7%
1.54 × 104
3.1
642050s3.11 × 10
1960s
85.5%
4.2%
6.6%
2.7
1.36 × 10
RCP2.664
64
2.97 × 10
81.4%
5.0%
8.3%
× 10 × 10
4.4
2010s
86.6%
4.5%
5.7% 2.190.50
1.0
2100s2.95 × 10
1.51 × 104
RCP2.6 RCP8.5
6 × 106
2.95
80.3%
5.0%
9.5%
83.2%
4.7%
7.7%
3.1
2.97 × 10
1.54 × 104 3.0
2050s
E4
2050s
RCP8.5
RCP2.6
2.78 × 106
75.0%
6.3%
11.8%
3.47 × 104
6.9
2100s2.97 × 106
81.4%
5.0%
8.3%
4.4
2.19 × 104

2100s

RCP8.52050s

2.95 × 106

80.3%

5.0%

9.5%

1.51 × 104

3.0

RCP8.52100s

2.78 × 106

75.0%

6.3%

11.8%

3.47 × 104

6.9

Sustainability 2019, 11, 2198
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10 of 1711
of 19

FigureDistribution ofof low-temperature
low-temperature mortality
mortality (shading)(shading) inin thethe 2010s
2010s (a)(a) andand theFigure 8.
8. Distribution
the difference
difference
betweenand thethe 2010s
Percent inin (a)(a) isis thethe deathdeath rate
rate fromfrom cold
cold inin 2010s;
2010s;
between other
other experiments
experiments and2010s (b–f).
(b–f). Percent
percent
in
(b–f)
shows
the
change
in
death
rate
from
cold
from
the
2010s
to
the
other
experiments.
percent in (b–f) shows the change in death rate from cold from the 2010s to the other experiments.

4. Discussion
4. Discussion
4.1. Hydrodynamic Factors Controlling Settlement
4.1. Hydrodynamic Factors Controlling Settlement
As mentioned in Section 2.2.2, the “Tmodern” and “UVmodern” groups were run to separate the
Asofmentioned
Section
2.2.2, theor“Tmodern”
groupsin
were
run to between
separate
effectschanges in inwater
temperaturechanges in andflow“UVmodern”
field. The difference
settlement
the effects ofchanges
in waswater
temperatureor changes
in flow
field. The anddifference
in settlement“Tmodern”
and“control”
more
distinct thanthat between“UVmodern”“control”(Figure 9),
between“Tmodern”
and“control”
wasmore
distinctthanthatbetween“UVmodern”
andsuggesting the dominant impact of water temperature in the Sea of Japan. This influence was “control”
observed(Figure9), suggesting
dominant
impact For
of watertemperaturein theSea of Japan.
This influenceinall periods
from thethe
past
to the future.
example,the annual
settlement
in region
B, underwasobserved
inallperiods
from
the
past
to
the
future.
For
example,theannualsettlementinregionscenario of RCP2.6 2100s, was reduced from 1740 in “control” to 322 in “Tmodern”, but increased
to
◦
B,
under
scenarioof
RCP2.62100s,
wasreduced
from
1740
in
“control”
to322
in“Tmodern”,
but2093 in the “UVmodern” group. This indicated that a 1.4 C increase in water temperature underincreased
to 2093 scenario
in the “UVmodern”
group.
Thislarval
indicatedthat aby1.4
increasein waterthehigh mitigationof RCP2.6 would
increasesettlement
five°Ctimes
and reveals
thetemperature under the high mitigation scenario of RCP2.6 would increase larval settlement by fivetimes and reveals the rectification of flow field on the distribution of larval settlement. As Figure A2

Sustainability 2019, 11, 2198
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11 of 17
12 of 19

rectification of flow field on the distribution of larval settlement. As Figure A2 shows, FBTWC in the
FBTWCthanin the2010s
wasRCP2.6
strongerthan withthat inthe RCP2.6weaker
2100s, withsoutherly
weaker and2010sshows,was stronger
thatin the2100s,
a southerly
joint acurrentof SBTWC
joint
current
of
SBTWC
and
TBTWC.
Therefore,
thesettlementinregionB
wasalittlehigherin
TBTWC. Therefore, the settlement in region B was a little higher in “UVmodern” than in “control”.
“UVmodern” than in “control”.

Figure9. Comparison
amountsamong
threegroups.
(a) Control
group;
Figure9. Comparisonofofannual
annual settlement
settlement amounts
among
three
groups.
(a) Control
group; (b)(b) Tmodern(c) UVmodern
UVmoderngroup.
group.
Tmoderngroup;group; andand (c)To elucidate
the effectof flow
fieldfield
on settlement,
wewe
identifiedof five
To elucidate
the effectof flow
on settlement,
identifiedthe
thesettlement
settlement proportion
proportion of
fiveinregionsin fourcases
futurefrom
casesthefrom
the “control”“UVmodern”
groups(Table(Table4).
4). When
When using
regionsfour future“control”
and and“UVmodern”
groupsusing thethe flowfieldof thesettlement
2010s, settlement
in region
was higher,
as mentioned
above,
while
settlementin theflow fieldof the2010s,
in region
B wasBhigher,
as mentioned
above,
while
settlement◦
intheregionnorthof
41.5°N(regions
C
and
F)
was
lower.
Thiswasrelatedtotheoffshoreregion north of 41.5 N (regions C and F) was lower. This was related to the offshore shiftshift
andandweakerweaker
flow
off
west
Hokkaido
in
the
2010s
(FigureA2).
Althoughthevariation
due
to
the
changein flow
flow off west Hokkaido in the 2010s (Figure A2). Although the variation due to the change inflow field was distinguishable, it was ~14% at most, much weaker than the impact of water
field was distinguishable, it was ~14% at most, much weaker than the impact of water temperature.
temperature. However, RCP8.5 2100s differed from the others, as settlement in the region north of
However, RCP8.5 2100s differed from the others, as settlement in the region north of 41.5◦ N was 3%
41.5°N was 3% higher in “UVmodern” than in “control”.
higher in “UVmodern” than in “control”.

Sustainability 2019, 11, 2198

12 of 17

Table 4. Settlement proportion in different regions between “control” and “UVmodern”. Results ofcontrol group are shown by grey background.

Control

Region A(%)
36.1

Region B(%)
49.1

Region C(%)
14.3

Region F(%)
0.5

Others(%)
0.0

RegionC+F (%)
14.8

UVmodern
Control

33.7
30.9

57.4
39.7

8.8
28.6

0.1
0.0

0.0
0.8

8.9
28.6

UVmodern
Control

27.2
24.3

51.4
43.2

20.9
32.2

0.5
0.2

0.0
0.1

21.4
32.4

UVmodern
Control

25.8
16.8

55.1
25.2

18.4
46.5

0.7
3.6

0.0
7.9

19.1
50.1

UVmodern

12.7

29.1

47.6

6.9

3.7

54.5

FutureCases
RCP2.62050s
RCP2.62100s
RCP8.52050s
RCP8.5
2100s

Group

4.2. Clarification of the Simulation
The results of six experiments in the control group suggested a positive impact of global warmingon crab larval settlement in the Sea of Japan. These results contrasted with the declining trends
predicted for Newfoundland and Labrador snow crab under a warming oceanographic regime [35].
However, the projected increase in larval settlement does not necessarily signify an increase in snow
crab resources in the future. Predation by jellyfish and juvenile pollock on snow crab planktonic
larvae was not included in the present model. This may increase under warming temperatures anddiminish larval settlement in the future. Though the warming conditions had a positive effect on
crab size and a negative effect on productivity and recruitment [47], a strong negative relationshipwas present between the bottom temperature and early-life survival of small crabs [48]. As indicatedin Figure A4, the bottom temperature in the Sea of Japan was projected to rise by 3 ◦ C at the end of
the 21st century, under the RCP8.5 scenario, which would present an extreme challenge for young
benthic crabs. Moreover, predation of small crabs by benthic fish, as well as cannibalism by elder crabs,
can lead to the mortality of metamorphosed benthic crabs. Since it takes 7–8 years for crabs to develop
to maturity, the effects of climate change on the crab abundance are unclear, although the harvesting ofsnow crab off Hokkaido Island is likely to increase in the future.
5. Conclusions
In this paper, the change in crab spawning and larval settlement as the climate changes is analyzed
with help of the survival and transport model. Seasonal variation in spawning was distinct in the Sea
of Japan and closely related to SST. Spawning in regions south of 41.5◦ N was maximal from January toMay, and then dramatically decreased until November. The annual spawning level at these sites ispredicted to decline slightly in the future. In the regions north of 41.5◦ N, two spawning peaks occur
every year. In response to climate change, the spring peak would be advanced and the autumn peakdelayed. The annual spawning amount would increase in the future, with larger increments under the
RCP8.5 scenario.
We found that ~90% of larval settlement occurred in regions A, B, and C, the most important
benthic crab grounds along the Japanese coast. Settling in the 2010s was the lowest among the six
experiments. The reduction of low-temperature mortality in crab larvae was found to be the primaryreason for settlement increase in the future. Additionally, the gathering site of settled megalopa
likely shifted northward due to global warming, with greater contribution from settling in region C.
We further investigated the impacts of change in either water temperature or flow fields on the larval
settlement. Water temperature was found to play a predominant role in larval settlement, not only inthe amount, but also in the distribution. However, the difference in flow field between the future andthe present could modulate the settlement off Hokkaido.

Sustainability 2019, 11, x FOR PEER REVIEW

14 of 19

larval settlement. Water temperature was found to play a predominant role in larval settlement, notonly in the amount, but also in the distribution. However, the difference in flow field between the
future and2019,
the present
Sustainability
11, 2198 could modulate the settlement off Hokkaido.
13 of 17
In this study, the change in snow crab larvae in response to global warming was reflected by thebalance among rates of mortality, drifting, and settlement on the seabed. Therefore, this study linked
In thiscrabatlarvaein response level,
to global
was reflectedchangesin study,
snow thecrabchange
larvae intosnow
changes
the population
the warming
third interlinked
level byof
thebalance
among
rates
of
mortality,
drifting,
andsettlementontheseabed.
Therefore,
this
study
biological organization [36]. The model has several shortcomings. One limitation is that the duration
linkedchangesin snow
larvae tochanges
at theinpopulation
the third ofinterlinked
levelofof the zoea
I, zoeaII, andcrab
megalopa
stageswas fixed
the model.level,
The durationthese stages
maybiologicalorganization
[36].
The
model
has
severalshortcomings.
Onelimitation
is
that
the
durationchange due to the warming ocean. Another weakness is that consumption and predation of crabofthe werezoea I,zoea
II, and megalopa
wasarefixed
in thetomodel.
The durationof thesestages may
larva
not
considered.
Planktonstages
bloomsthought
be accelerated
due tothe phenologicalchangedue
to thewarming
warming[23]ocean.
is that consumption
and predationof crabeffects ofglobaland,Another
thereby,weakness
cause changes
in the consumption
and growthoflarva
crab
were
not
considered.
Plankton
bloomsarethought
tobeaccelerated
due
to
the
phenologicaleffects
larvae. To improve the crab larval model, a lower trophic level ecosystem model should be coupled,ofglobalwarming
and,accurate
thereby,simulation.
cause changes
in theconsumption
and growthcrablarvae.
which
willlead to [23]a moreFinally,
it needs
to be reiterated
thatofthe
projected
To
improvethecrablarvalmodel,
alower
trophic
levelecosystem
model
shouldbecoupled,which
increase in larval settlement does not signify an increase of snow crab resources in the future,
for
will
lead
to
a
more
accurate
simulation.
Finally,
it
needs
to
be
reiterated
that
theprojected
increaseinmany factors can lead to the mortality of metamorphosed benthic crabs. However, we believe thatlarvalsettlement
does notsignify an increaseof snow
crabsignificant
resources thanin thetoday.
future, for many factors can
the snow
crab fishery
off Hokkaido
Island will
be morelead to the mortality of metamorphosed benthic crabs. However, we believe that the snow crab fisheryoff
Hokkaido
Island will
be more significant
AuthorContributions:Conceptualization,
Mao X.thanand today.
Guo X.; methodology, Wang Y.; validation, Wang Y. and
Mao X.; writing, Mao X. and Guo X.; calculation of DREAMS, Takayama K.
Author Contributions: Conceptualization, M.X. and G.X.; methodology, W.Y.; validation, W.Y. and M.X.; writing,Funding:
This calculation
research was
funded by T.K.
the Environment Research and Technology Development Fund of theM.X.
and G.X.;of DREAMS,Ministry of the Environment, Japan, grant number S-13; the National Natural Science Foundation of China,
Funding: This research was funded by the Environment Research and Technology Development Fund of the
grant number
Ministryof the41576010.
Environment, Japan, grant number S-13; the National Natural Science Foundation of China, grant
number41576010. We would like to thank Prof. Naoki Hirose at Kyushu University who support the work of
Acknowledgments:
We would
likethank
to thank
Naoki
HiroseSakurai
at Kyushu
supportfor
thehisworkof Katsumi
Acknowledgments:
Dr. Katsumi Takayama.
We alsoProf.
Yasunori
at University
Hokkaido who
University
adviceon ourTakayama.
We also thank Yasunori Sakurai at Hokkaido University for his advice on our model calculations.
model calculations.
Conflicts
Conflicts ofof Interest:
Interest: The
The authors
authors declaredeclare nono conflict
conflict ofof interest.
interest. The
The funders
funders had
had nono rolerole inin thethe design
design ofof thethestudy; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
study;
inthecollection,
analyses,orinterpretation
of
data;
in
the
writingofthemanuscript,
or
in
the
decisiontopublish the results.
publish the results.

Appendix A
Appendix A

Figure A1. Climatological annual mean water temperature in upper 200 m in six subsets. Green, red,and black lines represent the isotherms of 5 ◦ C, 14 ◦ C, and 16 ◦ C, respectively. (a) 2010s; (b) 1960s;(c) RCP8.5 2050s; (d) RCP8.5 2100s; (e) RCP2.6 2050s; and (f) RCP2.6 2100s.

Sustainability 2019, 11, x FOR PEER REVIEW

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Figure 1. Climatological annual mean water temperature in upper 200 m in six subsets. Green, red,and black lines represent the isotherms of 5 °C, 14 °C, and 16 °C, respectively. (a) 2010s; (b) 1960s; (c)Sustainability 2019, 11, 2198
14 of 17
RCP8.5 2050s; (d) RCP8.5 2100s; (e) RCP2.6 2050s; and (f) RCP2.6 2100s.

Figure A2. Climatological monthly mean flow field at 50 m in August in six subsets. The green line
Figure 2. Climatological monthly mean flow field at 50 m in August in six subsets. The green line
represents the isobath of 500 m. The color in (a) denotes the flow magnitude (cm/s) in the 2010s;
Sustainability
2019,the11,isobath
x FOR PEER
REVIEW
16 of 19
represents
of 500m. The color in (a) denotes the flow magnitude (cm/s) in the 2010s; thethe color in (b–f) denotes the difference in flow magnitude between the other subsets and the 2010s.
color in (b–f) denotes the difference in flow magnitude between the other subsets and the 2010s.

Figure A3. Climatological monthly mean SST in August in the 2010s (a), RCP2.6 2050s (b), and the
Figure 3. Climatological monthly mean SST in August in the 2010s (a), RCP2.6 2050s (b), and thedifference (c). The green line in (c) shows the isobath of 500 m.
difference (c). The green line in (c) shows the isobath of 500 m.

Figure 3. Climatological monthly mean SST in August in the 2010s (a), RCP2.6 2050s (b), and theSustainability
2019,(c).
11, 2198
difference
The green line in (c) shows the isobath of 500 m.

15 of 17

Figure A4. Climatological monthly mean temperature at the bottom layer (water depth <1000 m) in
June in six subsets. (a) 2010s; (b–f) the differences between the other subsets and the 2010s.

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