Journal of Geophysical Research: Oceans
RESEARCH ARTICLE
10.1029/2018JC014017
Key Points:• Dispersal of coral spawn and larvae isinvestigated using double-nested
circulation modeling coupled with a
Lagrangian particle tracking model• Corals mostly remain near the release
areas, while a nontrivial portion istransported ~1,000 km over thearchipelago due to the Kuroshio• Corals are partly separated from theKuroshio, entrained in the Kuroshio
Counter Current, and transported
eastward to Okinawa Island

Supporting Information:• Supporting Information S1• Data Set S1• Data Set S2• Data Set S3• Data Set S4• Data Set S5• Data Set S6• Data Set S7• Data Set S8• Data Set S9• Data Set S10• Data Set S11• Data Set S12• Data Set S13• Data Set S14
Correspondence to:
Y. Uchiyama,uchiyama@harbor.kobe-u.ac.jp

Citation:
Uchiyama, Y., Odani, S., Kashima, M.,
Kamidaira, Y., & Mitarai, S. (2018).
Inﬂuences of the Kuroshio on interisland
remote connectivity of corals across the
Nansei Archipelago in the East China
Sea. Journal of Geophysical Research:
Oceans, 123, 9245–9265. https://doi.org/
10.1029/2018JC014017
Received 27 MAR 2018
Accepted 27 NOV 2018
Accepted article online 29 NOV 2018
Published online 25 DEC 2018

Inﬂuences of the Kuroshio on Interisland Remote
Connectivity of Corals Across the Nansei
Archipelago in the East China Sea
Yusuke Uchiyama1,2
Satoshi Mitarai5, Sachika Odani1, Motohiko Kashima3, Yuki Kamidaira4,1

, and

1

Department of Civil Engineering, Kobe University, Kobe, Japan, 2Coastal and Estuarine Environment Department, Port andAirport Research Institute, Yokosuka, Japan, 3Department of Humanities, Kobe Gakuin University, Kobe, Japan, 4Nuclear
Science and Engineering Center, Japan Atomic Energy Agency, Tokai, Japan, 5Marine Biology Unit, Okinawa Institute of
Science and Technology, Onna, Japan

Abstract For the preservation and protection of coral habitats along the Nansei Archipelago in the East
China Sea, a submesoscale eddy-resolving synoptic ocean model was developed based on the Regional
Oceanic Modeling System coupled with a 3-D Lagrangian particle tracking model. Millions of neutrally
buoyant particles representing coral spawn and larvae were released from 19 major islands and one lagoon
every spring from 2012 to 2015. The model results were compared to satellite data, in situ observation, andsurface drifters to conﬁrm reasonable agreement. The connectivity matrix across the archipelago wasquantiﬁed using Lagrangian probability density functions of the modeled particle displacement. Most
particles remained near the release areas, while some traveled long distances by the northeastward drifting
Kuroshio, leading to notable interisland coral transport across the archipelago that promotes interislandconnectivity. A possible mechanism was examined by analyzing the transition from coastal to pelagictransport of the particles released from the Yaeyama Islands, the southernmost area of the archipelago. The
Kuroshio trapped the particles released from the northern coast of the islands with considerable temporalvariability in the entrainment rate. By contrast, particles released from the southern coast are markedly
affected by the eastward current around the release sites, which signiﬁcantly reduces their entrainment in the
Kuroshio and, thus, long-distance transport. Some entrained particles were expelled abruptly from theKuroshio, trapped by the southwestward drifting Kuroshio Counter Current developed between the Kuroshio
and the archipelago, and subsequently transported eastward to the islands.

Plain Language Summary For the preservation and protection of coral habitats along the Nansei
Archipelago in the East China Sea, we conducted 3-D tracking of particles mimicking coral spawn and larvae
released from shallow seas around the islands across the Nansei Archipelago, based on a numerical 3-D
ocean circulation model, Regional Oceanic Modeling System. Although the released corals were mostlytransported short distances and settled down near the release sites, a moderate amount was entrained in thenortheastward drifting Kuroshio Current developed in ~150 km west of the islands. The entrained particles
could be conveyed northeastward for ~1,000 km, leading to interisland population connections. However,
the entrainment rate largely depended on locations of the releases and variability in local oceanic currents
upon the releases. Furthermore, some entrained particles were expelled abruptly from the Kuroshio, trappedby the southwestward drifting Kuroshio Counter Current developed between the Kuroshio and thearchipelago, and subsequently transported eastward to the islands. These ﬁndings can be directly utilized inpolicy-making for the effective design management of marine protected areas.
1. Introduction©2018. American Geophysical Union.
All Rights Reserved.

UCHIYAMA ET AL.

Coral reefs are among the most biologically diverse and productive marine ecosystems on the globe (cf.,Connell, 1978), playing important roles in many aspects. For instance, coral reefs host favorable habitat toan extensive variety of ﬂora and fauna and are home to ~25% of marine life in the world’s oceans, while providing favorable conditions for the evolution of new species (Paulay, 1997). These reefs serve as remarkably
effective wave absorbers that protect shorelines from severe storms and erosion. Moreover, coral reefs also
support the regional economy and tourism as a recreation resource (e.g., Moberg & Folke, 1999). However,
because of overharvesting, pollution, disease, and climate changes, corals worldwide have recently been9245

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Figure 1. (a) ROMS-L1 and L2 model domain and bathymetry (m) in color. Blue line indicates the PN-line transect of the Japan Meteorological Agency (JMA) used for
Figure 3. A magenta box represents the domain for the wavenumber kinetic energy spectral analysis in Figure 13. (b) An enlarged ﬁgure of (a) approximately
coinciding with the ROMS-L2 domain that encompasses the entire chain of the Nansei archipelago. Red stars depict the locations of tide gauge stations used for the
harmonic analysis in Figure 4. The arrows represent approximate paths of the three major currents around the study area. Red: Kuroshio, gray: Ryukyu under current,
dashed black: Kuroshio Counter Current, respectively. (c) The inset corresponds to the Yaeyama Islands area bounded by the broken rectangular in (b). The numbers
identify the 20 selected major areas (19 islands and one lagoon) for the source and sink sites used in the costal connectivity matrix quantiﬁcation. The full list of thenames of these areas is presented in Table 1 and Figure 5b.

threatened by a considerable decline in biomass (Bellwood et al., 2004). The reasons behind this downward
trend include the high sensitivity of corals to their surroundings. Factors, such as water temperature, salinity,and light for photosynthesis, are widely known as principal factors that effect on coral growth (HoeghGuldberg, 1999). Corals generally inhabit ecosystems with a salinity range between 32 and 40 psu and a
rapid decrease in salinity brings death to corals (Hoegh-Guldberg & Smith, 1989; Veron, 1986). Light also
plays a major role in providing the energy for driving photosynthetic activity of zooxanthellae (Chalker
et al., 1988), a symbiotic alga that is also particularly sensitive to high turbidity in near-surface waters.
Finally, corals require water temperatures in a relatively narrow range, typically between 18 and 30 °C,
resulting in a general restriction of their habitable latitude band between 30°N and 30°S (e.g., HoeghGuldberg, 1999).
The Nansei Archipelago, commonly known as the Ryukyu Islands, is a chain of islands that stretch northeast
between Taiwan and Kyushu (Figure 1 and Table 1). The islands are in the subtropical region of Japan, hosting
ecologically abundant coral reefs, even though they are situated at the northernmost extreme of the habitable region. The Kuroshio plays a key role in heat transport from the lower latitudes, and maintains warm watertemperatures adequate for the growth of corals. The Kuroshio is one of the most powerful western boundary
currents generated from the North Paciﬁc Subtropical Gyre, originating near the Philippines. The current
enters the East China Sea (ECS) through the passage located between the east coast of Taiwan andYonaguni Islands (#17; Figure 1c), the southernmost island of the archipelago (Figure 1b), and drifts northeastward along the continental shelf slope in the central ECS at a mean net volume transport of 18.7 to
23.0 Sv (e.g., Andres et al., 2008; Ichikawa & Beardsley, 1993; Ichikawa & Chaen, 2000; Johns et al., 2001).
The Kuroshio’s intensive poleward transport of warm water extends the northern limit of coral habitats tomuch higher latitudes along the Kuroshio path, for example, to Tanegashima Islands (#1; Figure 1b) at
30.574°N, the northernmost major island in the archipelago, compared to other parts of the world’s oceans(Ikeda et al., 2006).
Three mechanisms are anticipated to promote transverse, southeastward transport of warm water fromthe Kuroshio to the coastal areas around the Nansei Archipelago situated ~150 km from the Kuroshio

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Table 1The Names, Geographical Positions, and the Number of Patches of the 19 Selected Major Islands and a Lagoon Are Shown in Figures 1b and 1c
Site Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

Name

LatitudeTanegashima Island
Yakushima Island
Kikaijima Island
Amamioshima Island
Tokunoshima Island
Okinoerabujima Island
Yoronshima Island
Okinawa Island*1
Kerama Islands
Kume Island
Miyako Island
Tarama Island
Ishigaki Island*2
Sekisei Lagoon
Iriomote Island
Hateruma Island
Yonaguni Island*3
Senkaku Islands
Taiwan*4
Daito Islands

0

30°37 28″
30°200 46″
28°190 22″
28°220 38″
27°470 21″
27°220 43″
27°020 40″
26°130 12″
26°100 36″
26°200 29″
24°470 26″
24°390 33″
24°200 18″
24°240 55″
24°200 34″
24°030 24″
24°270 18″
25°440 44″
23°520 42″
25°500 43″

Longitude
0

130°59 46″
130°310 14″
129°580 25″
129°290 38″
128°580 29″
128°360 43″
128°250 59″
127°420 08″
127°210 11″
126°470 15″
125°180 12″
124°420 00″
123°580 40″
124°120 14″
123°480 48″
123°460 52″
122°590 16″
123°280 27″
120°570 24″
131°140 33″

Number of Patches
14
11
6
20
9
8
4
40
7
6
11
6
14
5
8
3
4
4
38
6

Note. The patches surrounding these islands and lagoon are used as the source and sink sites of Lagrangian particles for the coastal connectivity analysis. Asterisks
identify local archipelagos, where the positions are indicated by those of (1) Tokashiki Islands, (2) Kohama Islands, (3) Utsurijima Islands, and (4) Minamidaito
Islands, respectively.

axis. First, there is a quasi-persistent intrusion of the Kuroshio due to mesoscale and submesoscale eddyinduced lateral mixing (Kamidaira et al., 2017). The second mechanism is the direct advective inﬂuence
from the Ryukyu Under Current (RUC), a branch current of the Kuroshio running along the eastern sideof the Nansei Archipelago. The RUC is a subsurface current, which is split from the Kuroshio east of
Taiwan and merges with the Kuroshio again north of Amamioshima Island (e.g., Ichikawa et al., 2004;
Yuan et al., 1998), carrying warm water from the lower latitudes to the east coast of the archipelago.
Although the RUC is largely responsible for poleward heat transport, its contribution to the maintenanceof marine ecosystem in the Nansei Archipelago is not substantial because the RUC’s main body is situatedat ~600 m below the surface (Ichikawa et al., 2004) where larvae and spawn of marine fauna are rare. The
third mechanism is due to the transverse advective effects associated with the Kuroshio Counter Current,
which often develops between the archipelago and the Kuroshio (e.g., Hisaki & Imadu, 2009; Ichikawa &Beardsley, 2002; Qiu & Imasato, 1990, and references therein). The KCC manifests as a quasi-persistent
southwestward current with pronounced appearance of mesoscale eddies, extracting water from theKuroshio, and transporting it toward the west coast of the archipelago. This is an important mechanismthat dynamically connects the Kuroshio with the islands, which are typically separated by approximately
100–200 km (cf., the distance between the Kuroshio and Okinawa (Main) Island is ~150 km). This suggeststhat the Kuroshio and the KCC concurrently promote the transport and dispersal of coral spawn and larvae
across the islands.
Nevertheless, the corals in the Nansei Archipelago have suffered severely from occasional coral bleaching
since the most intensive bleaching event in 1998, which affected coral ecosystems worldwide. Priority should
be given to those coral reefs that have a healthy and reliable supply of spawn. Therefore, specifying the originof the coral spawn supply is an essential consideration to properly identify those coral ecosystems thatshould be given the highest priority (e.g., Munday et al., 2009). In doing so, connectivity of coral spawn
and larvae will inevitably play an important role in the development of policies that effectively identify andmanage marine protected areas.
Genetic markers and numerical models have generally been used to measure coastal connectivity associated with nearshore and pelagic dispersal of coral spawn and larvae. For instance, Wood et al. (2014)

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estimated a connectivity of virtual coral larvae by using the global ocean circulation product based on a
1/12° HYCOM reanalysis product (Chassignet et al., 2007) coupled with a Lagrangian particle tracking
model for the domain between 47°N and 47°S, reporting that the northeastward larval transport prevails
along the Kuroshio around the Nansei Archipelago. Nakajima et al. (2010) conﬁrmed this observation using
a population genetic approach based on the genetic connectivity of Acropora digitifera, which is one of thecommon coral species in the Nansei Archipelago, extending across a large geographic area inclusive of theseven major regions (Kerama, Miyako, Ishigaki, Tanegashima, Amamioshima, and Okinawa Islands andSekisei Lagoon) along the 1,000-km-long chain of the islands. Nakajima et al. (2010) also suggested that
A. digitifera populations in the Nansei Archipelago may be able to recover rapidly if the coral ecosystem
is properly maintained and managed, with other reefal systems serving as the spawn and larval source.
Extensive ﬁeld campaigns have been carried out in several individual islands and speciﬁc local coral reefs in
the Nansei Archipelago to characterize local spawn transport mechanisms (e.g., Nakamura et al., 2017;
Yamano et al., 1998). To date, some efforts have already been made by combining in situ measurement
and local numerical circulation modeling to analyze inﬂuences of ambient currents on coral spawn dispersal(e.g., Shimokawa et al., 2014; Tamura et al., 2007). There is a growing consensus of opinion among expertsthat complicated coastal currents on complex reefal bathymetries could signiﬁcantly alter the oceanic coraltransport and dispersal, in particular for their initial behavior just after release (viz., spawning). Accordingly,
the demand for high-resolution numerical modeling has been on the rise so that complex coastal topography can be adequately deﬁned while encompassing a sufﬁciently extensive domain to illustrate interislandand much longer transport distances induced by the Kuroshio.
Based on the above arguments, the primary objective of this study was to quantify coastal connectivity ofcoral spawn and larvae across the islands using a state-of-the-art, high-resolution, coupled numerical hydrodynamic and particle tracking model. Particular attention was paid to interisland, remote connectivity, primarily induced by the Kuroshio, which persistently drifts northeastward on the continental shelf break of
the ECS. The circulation model was expected to adequately account for multiscale physics comprising
synoptic-scale currents (viz., the Kuroshio, KCC, RUC), mesoscale and submesoscale eddies, and nearshore
ﬂows inﬂuenced by complex coastal topography and bathymetry, all of which are anticipated to be responsible for fate of coral larvae. A high-resolution model is required for accurate evaluation of detachment andsettlement of larvae in shallow areas, referred to as “nearshore retention,” as has been argued in the previous
modeling studies (e.g., Wolanski & Kingsford, 2014). An Eulerian passive tracer transport model in our own
previous work (Kamidaira et al., 2018) has also demonstrated that high-resolution models are inevitable toobtain a plausible reproducibility of concentration ﬁeld conﬁned near the coasts, and that a 1-km lateral grid
spacing is a marginally sufﬁcient resolution. Moreover, a massive amount of Lagrangian particles representing planktonic coral larvae by considering their spawning timing and depth and survival rate before settlement was released from nearshore sites across the archipelago to properly quantify long-distance larvaltransport that scarcely occurs. The modeled ambient 3-D currents were exploited to trace the released larvae(particles) and to evaluate population connectivity based on Lagrangian probability density function estimated from particle tracking experiments. To this end, a double nested high-resolution synoptic ocean
model with Regional Oceanic Modeling System (ROMS; Shchepetkin & McWilliams, 2005, 2008) was developed with grid spacing down to 1 km. This ROMS model was coupled with an off-line 3-D Lagrangian particle
tracking model over a sizable area embracing all the major islands in the archipelago. The computational period of the particle tracking was chosen for spring to represent coral spawning for a four-year period between
2012 and 2015.
The remainder of this paper is organized as follows. The modeling framework used for the hindcast experiment and the method to quantify connectivity based on Lagrangian probability density functions (PDFs) of
the modeled particle displacement are described in section 2. Section 3 demonstrates the model’s reproducibility through extensive comparisons with in situ observations and satellite altimetry data from an Eulerian
viewpoint. The pelagic interisland connectivity across major islands and a lagoon in the Nansei Archipelago
are presented in section 4. In section 5, inﬂuences of the Kuroshio on long-distance transport and spatiallyextensive dispersal of emulated corals responsible for interisland connectivity are examined. In section 6,
we examined particles that are entrained in the Kuroshio, travel long distance, and then are abruptly separated from the Kuroshio to approach Okinawa Island due to the KCC and associated eddy mixing. Finally,

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conclusions as well as discussion on comparison with other sites including the Great Barrier Reef, Australia,
and the Florida Keys, USA, are presented in section 7.

2. The Models
2.1. Japan Coastal Ocean Predictability Experiment Version 2–ROMS Downscaling Circulation Model
Figure 1 shows the numerical domain of the oceanic downscaling model in a double-nested conﬁguration
embedded in the Japan Coastal Ocean Predictability Experiment version 2 (JCOPE2; Miyazawa et al., 2009)
domain. JCOPE2 is a numerical reanalysis product for the northwestern Paciﬁc Ocean assimilated with a vast
amount of satellite and in situ data, including Argo ﬂoats using three-dimensional variational assimilation. The
JCOPE2 product is provided as daily-averaged sea surface height (SSH), temperature, salinity, and meridionaland zonal horizontal current velocities, which are spatiotemporally projected onto the perimeter of the
ROMS-L1 model for the open boundary conditions. The one-way off-line nesting approach described in
Mason et al. (2010), Uchiyama et al. (2014), Uchiyama, McWilliams, et al. (2017), Uchiyama, Suzue, et al.
(2017), Uchiyama, Kanki, et al. (2017), Uchiyama et al. (2018), Kamidaira et al. (2017, 2018), and Tada et al.
(2018) is applied to successively decreasing grid spacing from ~10 km (JCOPE2) to 3 km (ROMS-L1), andfurther down to 1 km (ROMS-L2). The parent ROMS-L1 model is designed to encompass a wide area toaccount for the Kuroshio ﬂowing from the Taiwan Strait, consisting of 768 × 768 horizontal grid cells and32 vertically stretched terrain- and surface-following s layers (Shchepetkin & McWilliams, 2005). The innermost
ROMS-L2 model has 1,280 × 1,120 cells laterally and 32 s layers vertically, covering the whole chain of the
Nansei Archipelago from the northeastern part of Taiwan to the southern tip of Kyusyu (Figures 1a and 1b).
The model topography was obtained from the Shuttle Radar Topography Mission at 30-arc-sec resolution(SRTM30_Plus; Rodríguez et al., 2005; Becker et al., 2009), which covers the global ocean at 30 geographic
arc seconds, supplemented by the Japan Oceanographic Data Center-Expert Grid data for Geography at
500-m resolution (http://www.jodc.go.jp/jodcweb/JDOSS/infoJEGG.html) for the coastal region around
Japan. Surface wind stresses were obtained from the QuickSCAT-ECMWF blended wind data (e.g.,
Bentamy et al., 2007) for 2005–2007 and the Grid Point Values Global Spectral Model operational atmospheric product from the Japan Meteorological Agency (JMA) with horizontal resolution of 0.2° × 0.25°
for 2008–2015. Net heat, freshwater, and radiation ﬂuxes at the ocean surface were obtained from the
NOAA Comprehensive Ocean–Atmosphere Data Set (Woodruff et al., 1987) monthly climatology. The 20day averaged JCOPE2 data were applied to sea surface temperature and salinity restoration with a nudging time scale of 90 days to correct long-term biases caused by the imposed climatologicalComprehensive Ocean–Atmosphere Data Set surface ﬂuxes. The monthly climatology of the major riverdischarges in Dai et al. (2009) was applied for Yangtze River as an additional surface freshwater ﬂux distributed around the river mouth. To improve the reproducibility of the Kuroshio, we introduced a fourdimensional nudging for temperature and salinity with a weak nudging inverse time scale of 1/20 days
toward the 10-day averaged JCOPE2 3-D temperature and salinity ﬁelds (Uchiyama, McWilliams, et al.,2017). This combination of the data sets and conﬁguration has been exploited for high-resolution modeling of the Kuroshio around Japan extensively with great successes (e.g., Tada et al., 2018; Uchiyama,
McWilliams, et al., 2017; Uchiyama, Suzue, et al., 2017). For further details of the present ROMS-L1 model
conﬁguration, readers should refer to Kamidaira et al. (2017).
The child ROMS-L2 model was initialized and forced along the perimeter by the spatiotemporally interpolated daily-averaged ROMS-L1 output. The hourly output of the JMA GPV-Meso Scale Model operational
atmospheric reanalysis with horizontal resolution of 0.05° × 0.0625° was utilized for the ROMS-L2 model
instead of the Grid Point Values Global Spectral Model used for the L1 model. The same sea surface temperature and sea surface salinity restoration for the surface ﬂux correction was included as was done for the L1model. Ten principal tidal constituents from the TPXO.7.0 global tidal reanalysis (Egbert et al., 1994; Egbert& Erofeeva, 2002) were additionally imposed on the SSH along the perimeter to include barotropic tides
and resultant intrinsic baroclinic tides. The temperature and salinity nudging was not applied to the L2model, so as not to interfere with spontaneous development and decay of intrinsic eddies. A computational
period of approximately six years was chosen for the ROMS-L2 model from December 2010 to November
2015. The other numerical conditions were the same as those used in the ROMS-L1 model conﬁguration as
summarized in Table 2.

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Table 2Computational Conﬁgurations for the ROMS-L1 and ROMS-L2 Models
ROMS-L1
Computational period
Grid cells
Horizontal grid resolution
Baroclinic time step
Surface wind stress
Surface ﬂux
SST and SSS to restore
Yangtze River discharge
Open boundary/initial conditions
TS nudging
Tides
Topography

ROMS-L2

1/1/2005–2/11/2015
27/12/2010–2/11/2015
768 × 768 (×32 layers)
1280 × 1120 (×32 layers)
3.0 km
1.0 km
240 s
40 s
QuikSCAT-ECMWF (daily, till 31/12/2007)
JMA GPV-MSM (hourly)
JMA GPV-GSM (daily, 1/1/2008, and later)
NOAA COADS (monthly climatology)
JCOPE2 (20-day averaged)
Monthly climatology (Dai et al., 2009)
JCOPE2 (daily)
ROMS-L1 (daily)
JCOPE2 (10-day averaged)
TPXO 7.0
SIO SRTM30_Plus

2.2. Ofﬂine Lagrangian Particle Tracking Model
Coastal connectivity around the Nansei Archipelago was estimated from Lagrangian probability functions(PDFs) with an enormous amount of Lagrangian particles originating from arbitrary nearshore sites aroundmajor islands and a lagoon where spawn and larvae are released. We exploited the off-line 3-D Lagrangianparticle tracking model developed in Carr et al. (2008) and used previously, for example, in Mitarai et al.
(2009) and Romero et al. (2013) with the ROMS model outputs. This off-line model integrates the
Lagrangian equation of motion of the nth realization of ﬂuid particle,∂
X n ðτ; aÞ ¼ Un ðτ; aÞ;∂τ(1)where Un(τ, a) and Xn, respectively, indicate the velocity and position of the nth Lagrangian particle; a is theinitial position of the particle of interest; and τ is the elapsed time, usually referred to as advection time. Notethat variables in bold denote vectors. The particle velocity Un is expressed by
U n ðτ; aÞ ¼ u½X n ðτ; aÞ; tn þ τ ;(2)where u(x, t) is the Eulerian velocity at a given location x and time t and tn indicates the release time of the nth
Lagrangian particle. The Eulerian velocity at a given particle location is estimated through liner interpolationin time and space of the discrete velocity ﬁelds. A fourth-order accurate Adams-Bashforth-Moulton
predictor–corrector scheme is employed over time for solving equations (1) and (2) with a series u of stored
2-hr-averaged 3-D ROMS velocity. Hence, the particle tracking model is capable of simulating the trajectories
of many Lagrangian particles released from arbitrary nearshore sites deﬁned around the Nansei Archipelago,
once the ROMS-L2 circulation ﬁeld is obtained.
Our coupled model takes into account basic biological properties of coral species in the area, dominated byAcropora (e.g., A. tenuis and A. hyacinthus). After spawning, coral eggs evolve into zooplanktons (planula larva)
in several days, which have weak swimming ability that we omitted merely for the simplicity in the modeling.
Hayward et al. (2011) reported that Acropora’s egg is buoyant to reach near the surface, yet they spawn in
depths of 8–20 m. We assumed that eggs are released near the surface, at 3 m below the surface to formally
avoid inﬂuences from a thin surface Ekman boundary layer. In general, planula larvae of corals survive a few
days to a few months before settlement. Marine biologists have introduced a concept of “50% survival rate”
to interpret whether larvae successfully settle down their favorable nearshore areas to grow into adults.
Nishikawa et al. (2003) and Nozawa and Okubo (2011) have reported that 50% survival rate of Acropora in
the study area is 21–25 days. Therefore, we employed 21 days as the maximum advection time to trace
Lagrangian particles. In the present particle tracking model, released particles were tracked with a time stepof 200 s for at least 21 days, or until they crossed the boundaries of the L2 model domain, or were beached ona land grid. The estimated total number of Lagrangian particles, release dates of particles, and positions ofsink and source areas are detailed in sections 4.1 and 5.1, respectively. We did not introduce any ad hoc

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behavior models, such as random walk subgrid-scale diffusivity or diel vertical migration, to avoid further
complexity and uncertainty. These factors should be examined in future works.
2.3. Lagrangian PDF and Coastal Connectivity
To quantify the dispersal of Lagrangian particles released from source patches, we calculate Lagrangian PDFs
following the methodology described in Mitarai et al. (2009) from the results of the particle tracking model. A
Lagrangian PDF represents the probability density function of particle displacement for a given advection
time τ. Its discrete representation is0

f x ðξ; τ; aÞ ¼

1 N
∑ δðX n ðτ; aÞ  ξ Þ;
N n¼1

(3)

where N is the total number of Lagrangian particles, δ is the Dirac delta function, and ξ is the sample spacevariable for X. Although tracking is conducted three-dimensionally, the results are illustrated in a horizontal
0
two-dimensional format; thus, f x ðξ; τ; aÞ is evaluated only in the horizontal directions, primarily because theparticles generally remain in the upper ocean. Discrete Lagrangian PDFs are evaluated from the center location of the release sites deﬁned later, averaged over each site. Hence,0f x ðξ; τ; aÞ≈

01
2 ∫jr j≤ R f x ðξ; τ; a þ r Þdr;πR

(4)where R is the radius of each release site a deﬁned later. The ﬁnal form of discrete Lagrangian PDF is obtained
after applying an isotropic spatial Gaussian ﬁlter G(x  ξ) with standard deviation of 3 km:∞

0f x ðξ; τ; aÞ≈∫∞ Gðx  ξ Þf x ðξ; τ; aÞdx:(5)

Coastal connectivity, denoted by Cji(τ) here, is deﬁned as the probability of Lagrangian ﬂuid particles transported from a source site j to a sink site i over a time interval τ.

C ji ðτ Þ ¼ f x ξ ¼ x i ; τ; a ¼ x j πR2 ;(6)where xjand xi are the source and sink patch locations, and the normalization by πR2 converts the PDFs toprobabilities. For further details on mathematical representations and their physical interpretations, readers
should refer to Mitarai et al. (2009).
The Lagrangian analyses were performed for the particles positioned at all the depths for quantitative measures of the total transport rather than near-surface transport. We observed that long-distance transport
occurs near the surface, where currents are most energetic. The coupled model result indicated that particles
mostly stay in the upper ocean, despite their substantial three-dimensional behavior. Our preliminary inspection of comparison between the near-surface Lagrangian transport and depth-integrated transport exhibitedthat they are qualitatively equivalent. Therefore, we decided that the depth-integrated results are demonstrated in the present study.

3. Model Validations
In this section, the newly developed ROMS-L2 model is validated with satellite and in situ observations andthe assimilative JCOPE2 reanalysis. It is worth noting that Kamidaira et al. (2017) has already demonstrated
good agreement between similar double-nested ROMS-L1 and L2 results and the corresponding observations over a 2.5-year period from 2010 to 2013. To validate reproducibility in the mean structure and variability of synoptic-scale and mesoscale motions near the ocean surface, the present L2 model output wascompared with the satellite altimetry data produced and distributed by the Archiving, Validation, andInterpretation of Satellite Oceanographic (AVISO) data (e.g., Le Traon et al., 1998). In particular, we utilized
the AVISO Ssalto/Duacs gridded multimission altimeter products, that is, the gridded data sets of daily SSH
anomaly and associated geostrophic velocity composite at 1/4° resolution. Additionally, surface velocity
and SSH of the assimilative JCOPE2 reanalysis are used for a further conﬁrmation.
Figure 2 shows time-averaged surface currents and temporal variances of SSH for the entire one year of 2012
from the three outcomes, viz., the AVISO, JCOPE2, and ROMS-L2 embedded in the ROMS-L1 output. The SSH

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Figure 2. Plan view plots of (a) the time-averaged surface velocity magnitude (left column) and (b) temporal variance of
SSH (right column) for the entire year 2012. From top to bottom panels, the results from the AVISO satellite altimetrydata, JCOPE2, and ROMS-L2 (embedded in the L1 results) are depicted.

variances represent synoptic and mesoscale variability at the surface due to the transient Kuroshio path andresolved eddies. The modeled mean Kuroshio path represented by larger geostrophic surface current
velocity (warm colors) of the ROMS models is consistent with that of the AVISO and JCOPE2, particularly in
the ECS (Figure 2a). The ROMS-derived SSH valiance exhibits several important features with magnitudes
equivalent to those obtained from the AVISO data (Figure 2b). For example, smaller variances are observedon the persistent Kuroshio path on the western side of the archipelago in the ECS as compared with thoseon the eastern side. More intensive eddy activity in the east of the archipelago is apparently caused byinﬂuences of westward propagating mesoscale eddies as Rossby waves headed toward the eastern side of
the chain of the islands (e.g., Liu et al., 2012; Qin et al., 2015). In contrast, the other high SSH variances
south of Kyushu and in the Kuroshio Extension Region (cf., Kamidaira et al., 2018; Uchiyama, Suzue, et al.,UCHIYAMA ET AL.

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Figure 3. (a) Seasonally averaged temperature and (b) salinity for spring from (left) the ROMS-L2 model and (right) the JMA
observations in the vertical section along the PN line (Figure 1a).

2017) correspond to the areas where the Kuroshio path largely meanders, reﬂecting intrinsic synoptic
variability of the Kuroshio.
The modeled stratiﬁcation was then compared with repeated in situ hydrographic observations in the vertical section along the PN line transect performed by JMA, shown by the blue line in Figure 1a. The measurements along the PN line comprise 16 conductivity, temperature, and depth casts that have been obtained
seasonally since 1972 to date from JMA’s research vessels. Figure 3 is an example that shows comparisonsof the seasonally averaged temperature (Figure 3a) and salinity (Figure 3b) for spring from April to June forthe entire model years, when coral spawning mostly occurs in the study area (section 4.1). It is clearly exhibited that the modeled seasonal climatology of the stratiﬁcation shows a good agreement with the observation, not only in spring but also in all the four seasons (not shown).
Subsequently, Figure 4 shows the result of a harmonic analysis of surface elevations from ROMS-L2 and in situ
tidal observations at six tide gauge stations of JMA in Daito Islands, Tanegashima Islands, AmamioshimaIslands, Okinawa Islands, Ishigaki Islands, and Yonaguni Islands, shown by the red markers in Figure 1b.
The tidal harmonic analysis was conducted with the data from 1 January to 31 December in 2013. We show
the result for four selected principal tidal constituents (M2, S2, K1, and O1) as scatterplots of amplitudes andphase epochs of the four components. The tidal amplitude and phases are reasonably well reproduced by theL2 model with the correlation coefﬁcients of 0.997 and 0.990, respectively, whereas the M2 lunar semidiurnal
amplitudes were slightly underestimated in approximately 10–20% of those of the observation, presumably
due to underresolved topographic inﬂuences near the gauges. The evidence described above suggests thatthe present ROMS-L2 model is sufﬁciently capable of reproducing the synoptic and mesoscale features
including those due to the Kuroshio, as well as high-frequency tidal variability around the
Nansei Archipelago.

4. Coastal Connectivity Across the Archipelago
4.1. Particle Tracking Experiment 1
For quantiﬁcation of costal connectivity across the Nansei Archipelago, we performed a 3-D Lagrangian particle tracking experiment (experiment 1) using the ROMS-L2 3-D ﬂow ﬁeld at 2-hr intervals. The conﬁgurationof experiment 1 is summarized in Table 3. A total of 19 major islands and a lagoon were chosen as source and

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Figure 4. Scatter diagram of four principal tidal constituents (M2, S2, K1, and O1) derived from a harmonic analysis of thehourly surface elevations from ROMS-L2 and observation at six tide gauge locations of JMA in the Nansei Archipelago
depicted in Figure 1b. (a) Amplitudes and (b) phase epochs. CR in each plot indicates the correlation coefﬁcient.

sink sites of corals (Figure 1b). Relatively small circular areas, which we call source/sink patches, with adiameter of 10 km are deﬁned next to the shorelines to surround each of the 20 sites with minimal gaps
between them, comprising a total of 224 patches in the L2 domain. About 1.5 million Lagrangian particles
were released at 3 m below the surface (section 2.2). To be consistent with the life cycle of corals, theseparticles were tracked for up to 21 days after the release, until they were beached on land grids, or until
they exited the L2 domain through the open boundaries.
Spring was chosen for the experiments because it is the season when coral spawning and subsequent larval transport occur in the Nansei Archipelago. The dominated reefal corals in the study area, Acropora, arewell known to spawn synchronously at one particular night in May or early June, which is often referred toas “mass spawning,” although the exact timing has not been fully understood with a range of a few weeks(e.g., Zayasu & Shinzato, 2016). Therefore, we designed the particle tracking model with the release timing
at particular time in the evening for two weeks centered at the night proposed by Zayasu and Shinzato(2016). The 3-D particle tracking was thus conducted for every spring over the four model years from2012 to 2015 to increase the statistical robustness and interannual variability. About 110,000 Lagrangianparticles were equally distributed in the 224 patches and released every day for a two-week period in eachof the four years. Hence, releases of particles take place 14 times each year, centered on the peakspawning period that occurs at 8 P.M. local time on the day subsequent to the full moon (a spring tide)in May (Table 4). This release scenario mimics actual coral spawning with increased statistical signiﬁcanceby repeated releases.
4.2. Results
Figure 5 shows the connectivity matrix for the advection time of 21 days from the particle tracking experiment 1. The magnitude of the connectivity represents probability of particles transported from a source site
to a sink site. Warmer (colder) colors correspond to higher (lower) connectivity of coral spawn and larvae
between origins labeled in the abscissa and destinations in the ordinate. Evidently, high connectivity manifests on and around theTable 3Conﬁgurations for the Particle Tracking Experiments
straight line with a slope of 1:1 (viz., 45°) depicted by reddish colors,
exhibiting that the particles mostly remain near the released areas
Experiment 1
Experiment 2
even after the advection time of 21 days. Nevertheless, moderately
Number of patches
224
145high connectivity also appears in the lower right part of the matrix,
Diameter of patches
10 km
2 km
relative to the straight line, than in the upper left part. This asymmetry
Release depth
3m
3m
demonstrates that the particles travel long distance to reach otherMaximum advection time
21 days
21 daysReleased particles/day (A)110,000
280,000
islands, which we call “interisland remote connectivity” across theReleased time/year (B)
14 times
14 times
islands, with prevailing northeastward transport. Evidently, the northTotal number of released particles (A × B)
1,540,000
3,920,000
eastward drifting Kuroshio appears to play a key role in promoting

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Table 4
Durations of Daily Particle Releases Mimicking Actual Coral Spawning
Year
2012
2013
2014
2015

such asymmetric transport. For instance, the connectivity for the longdistance transport from the southernmost area, including Ishigaki
Peak Spawning
Release Durations
Island, Sekisei Lagoon, and Iriomote Island (which are collectively(Full Moon in May +1 day)(14 days)referred to as the Yaeyama Islands; sites #13–15 in Figure 1b), to
Okinawa Island (#8; which is the largest island in the Nansei
7 May
30 April–13 May
26 May19 May–1 June
Archipelago, serving as the prefectural capital and the most popular
16 May9–22 May
tourist destination) are at O (103). This result indicates that one sin5 May
28 April–11 May
gle particle (i.e., coral spawn or larva) out of 1,000 released aroundthe Yaeyama Islands may be transported northeastward more than
400 km to reach Okinawa Island within the advection time of 21 days. This numerical evaluation suggests that
there is a signiﬁcant interisland connectivity that is qualitatively consistent with the coral connectivity basedon population genetics reported in Nakajima et al. (2010). Such interisland remote connectivity, most likely
induced by the Kuroshio, would have considerable importance for determining marine protected areas suitable to maintain coral ecosystems. Therefore, we focused in the subsequent sections on the particles traveling long distances (hereinafter referred to as long-distance particles or LDPs) responsible for remoteconnectivity of coral spawn and larvae across the Nansei Archipelago.

5. Interisland Remote Connectivity
5.1. Particle Tracking Experiment 2
Another off-line Lagrangian particle tracking experiment (experiment 2; Table 3) was conducted to investigate LDPs responsible for the interisland remote connectivity. In experiment 2, we focused on LDPs originated from the Yaeyama Islands (the southernmost area in the Nansei Archipelago that includes the
major islands of Ishigaki and Iriomote, with Sekisei Lagoon located between them; Figures 1c and 6a) as
the source areas of coral spawn. In experiment 2, about 280,000 Lagrangian particles were released daily
from 145 patches with a reduced diameter of 2 km deﬁned around the Yaeyama Islands (Figure 6a).
Hence, a total of ~3.9 million particles are analyzed. Other release procedures, the model conﬁgurations,and the rationale behind the experimental design are the same as those used in experiment 1 (sections
2.2 and 4.1). Before analyzing the interisland transport, the local connectivity matrix around the
Yaeyama Islands averaged for the four-year model period is brieﬂy discussed (Figure 6b). Similarly tothe interisland connectivity matrix from experiment 1 (Figure 5), selfrecruitment is predominant as illustrated by the highest connectivity
around the 1:1 line. Sekisei Lagoon is a “hot spot” that intensively
collects particles released from other sites, as represented by higher connectivity in the upper half of the matrix, particularly groups 14–18. There
are several pinpoint connections such as 6 ➔1, 1➔5, 2 ➔16, and 10 ➔18,
suggesting that local nearshore transport invokes anisotropic connections. Moreover, the southwestern coast of Ishigaki Island (groups
8–11) has less pronounced connectivity and does not receive many
particles from remote sites within the Yaeyama Islands. Such intraislandconnectivity is inevitable for management of the local marine protectedareas, whereas we do not further discuss as it is beyond the scope of thepresent study.

Figure 5. Connectivity matrix of coral spawn and larvae across the Nansei
Archipelago for the advection time of 21 days from experiment 1. The
numbers and names of the 19 islands and a lagoon shown in the abscissa
and ordinate correspond to those presented in Figure 1b and Table 1.

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For validation of the particle tracking model results, two sets of drifter data
were utilized. The ﬁrst one is composed of six surface drifters GPSequipped released near Sekisei Lagoon during the spring tide in May
and June, 2006 and 2007. The other set of drifter data is the publicly available one distributed as the Global Drifter Program (GDP) operated by
NOAA (http://www.aoml.noaa.gov/phod/dac/index.php). This GDP data
set consists of time series of the geographical positions of a large numberof satellite-tracked surface drifting buoys. These buoys were mostly
released in the deep ocean to minimize coastal inﬂuences. Therefore, it
is suitable to use the GDP data for examining pelagic behaviors
of particles.

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Figure 6. (a) Locations of the source patch sites for the particle tracking experiment 2 distributed around the Yaeyama
Islands including Iriomote Islands, Ishigaki Islands, and Sekisei lagoon. Red circles indicate 145 patches with a diameterof 2 km, the dashed black rectangle is the domain for the spatial averaged velocity estimate in Figure 10, the dashed black
oval approximates the area of Sekisei lagoon, and color shows bathymetry (m). The orange rectangles with numbers (1–18)
represent the groups of source and destination patches used in (b). (b) The local connectivity matrix around the Yaeyama
Islands from experiment 2 averaged over the four-year period from 2012 to 2015. The site group numbers in the abscissa
and ordinate are as indicated in (a).

5.2. Advection Time-Averaged Lagrangian PDFs
Figure 7 presents trajectories of the six in situ surface drifters (section 5.1) on the modeled time-averaged
Lagrangian PDFs, which are computed by taking averages of Lagrangian PDFs at 6-hr intervals over theadvection time of 21 days for the four model years, from 2012 to 2015. Stated another way, the averaged
Lagrangian PDFs are averages of 21 (days) × 4 (times a day) × 4 (model years) frames of the Lagrangian
PDFs over the advection time of 21 days, representing relative magnitude of the probability of passage ofthe particles in a climatological sense. Therefore, interannual variability of the Lagrangian PDFs is signiﬁcantly
reduced allowing for qualitative comparisons to in situ drifter trajectories that did not synchronize with themodel. It should be noted that the model years differ from the observation, mainly because a reliable highresolution wind data set for the L2 model was not available (viz., the JMA GPV-Meso Scale Model; section 2.1)
for 2006 and 2007. In Figure 7, warmer (colder) colors illustrate the regions with higher (lower) averaged PDFs,
where the particles have passed more (less) frequently. The in situ surface drifter trajectories appear mostly inthe regions where higher Lagrangian PDFs manifest, showing a good agreement with the model results climatologically. The observations reveal that only one single drifter was transported long distance in the northeastward direction partly along the Kuroshio path (Figure 2a), whereas the other ﬁve drifters drifted aroundFigure 7. (a) Six in situ surface drifter’s trajectories (black curves) on advection time-averaged Lagrangian PDF (color) over
the advection time of 21 days for all the releases in 2012–2015. (b) Enlarged ﬁgure of (a) focusing on the Yaeyama Islands
shown by the red box in (a). Red dots are the released positions of the drifters originated from Sekisei lagoon between
Ishigaki and Iriomote Islands, while blue dots are the position of the drifters at the labeled advection times since drifters
were released until they beached, or up to the maximum advection time of 21 days (unit: day).

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Figure 8. Averaged Lagrangian PDF for each of four years from 2012 to 2015. Thin black contours represent ﬁve-week averaged surface velocity magnitude of 0.5 m/s, showing approximate shapes of the Kuroshio path during the modeled coral
spawn periods.

°

Figure 9. LDP (particles travel long distance to pass 26.5 N) probability at each release patch around the Yaeyama Islands.
Contours are isobaths <500 m with intervals of 100 m.

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Figure 10. Time series of LDP probability averaged over all the 145 patches (blue) and eastward (green) and northward
components of the modeled surface current velocity (red) for each of the particle release periods in 2012–2015. The
velocities are spatially averaged over the dashed black rectangle in Figure 6.

the islands and beached within 21 days. These results are qualitatively consistent with the averaged PDF distribution, in which the highest PDFs appear near the release sites and the elongated, meridionally asymmetric far-ﬁeld PDF pattern, as well as the connectivity matrix shown in Figure 5. Although coral spawn
and larvae are largely constrained around Sekisei Lagoon (see also Figure 6b), a nontrivial population could
travel long distance by entrained in the Kuroshio.
Subsequently, interannual variability of the averaged Lagrangian PDFs is investigated for each of the fourmodel years (Figure 8). In all the four years, many of the particles travel northeastward as higher averaged
PDFs manifested along the Kuroshio path depicted by thin black contours. However, there are obvious interannual differences in the spatial distributions of the averaged PDFs. For example, some particles reach 32°N
and further north in 2012 and 2013, although almost no particle passes 30°N in 2014 and 2015. LDPs
entrained in the Kuroshio travel northeastward, then are expelled to the east toward Okinawa Island (centered at 26°280 46″N and 127°550 40″E) most actively in 2014. Striking interannual variability in the PDFs is also
found in the southeastern area of Sekisei Lagoon. In 2012 and 2014, many particles are dispelled southward,
while in 2013 and 2015, high PDFs appear in the east of the lagoon. These results suggest the Kuroshio plays acentral role in promoting the northeastward LDPs from the Yaeyama Islands, with pronounced interannual
variability in the advection-time averaged Lagrangian PDFs, which could be associated with the transient
Kuroshio path and other regional oceanic variability.
5.3. LDP Probability in the Yaeyama Islands
To investigate how the LDPs occur with signiﬁcant interannual variability, the release sites around Sekisei
Lagoon were revisited and the probability of LDP occurrence in each release patch for each year was

Figure 11. Selected trajectories of satellite-tracked surface drifters from the data set of the global drifter program. (a) The
296 trajectories of the extracted drifters entrained in the Kuroshio (blue curves), which have entered the red box (boundedby 121°E, 124°E, 24°N, and 26°N) once and then crossed the red line (at 126°E between 25°N and 28.5°N). (b) Trajectories of
24 drifters expelled from the Kuroshio (blue curves). Magenta lines: transects for extracting the drifters separated from theKuroshio, corresponding approximately to the Kuroshio axis where the maximum velocity occurs. Black texts: the transect
numbers (1–6) used in Figure 12. Gray contours: the ﬁve-week averaged surface velocity magnitude >0.5 m/s.

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Figure 12. Number of the drifters crossing the six magenta transects(Figure 11b) and then drifting southward by the KCC. The horizontal axis
represents the transect numbers shown in Figure 11b. Colors correspond to
the seasons when the drifters crossed the magenta transects. The deﬁnitionof the boreal seasons is as follows. Fall: October–December, winter: January–
March, spring: April–June, and summer: July–September.

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evaluated. In this case, the LDPs are deﬁned as the particles passing 26.5°N
due to intense advective effects of the Kuroshio. Figure 9 shows the probability of LDP around the Yaeyama Islands, in which the colors of patches
correspond to the LDP probability. For instance, the LDP probability of 0.3
suggests that 30 particles out of 100 released from that patch travel long
distance and pass 26.5°N to become LDPs. LDP probability is generallyhigh in the northern coastal areas of the Yaeyama Islands, where released
particles are readily entrained in the Kuroshio to travel long distance. In
contrast, LDP probability near the southern coasts is much lower with larger interannual variability with quite low probability in 2013 and 2014. This
result clearly indicates that the oceanic conditions south of Sekisei Lagoon
could inﬂuence the number of LDPs.

We then examined possible causes for the interannual variability in LDP
probability. Figure 10 shows time series of LDP probability and surface current velocities averaged over the black rectangle shown in Figure 6 for the
14-day release period in each year. The blue curves show the PDF probability averaged over all 145 patches around the Yaeyama Islands, and thegreen and red curves are the spatially averaged eastward and northward surface current velocities. The probability prominently changes as the release time changes, leading to the mean LDP variations in each year.
Overall, the eastward velocity near the release sites and the LDP probability seem to have a correlation.
The LDP probability is signiﬁcantly suppressed with large eastward currents and moderate northward currents. This tendency is most evident during the latter half of 2013 and ﬁrst half of 2015 in Figures 10b and10d (yellow shades). Therefore, coral spawn released from the southern area of the lagoon is quite sensitive
to eastward currents upon the release for becoming LDPs, even with mean northward currents.

6. Inﬂuences of the Kuroshio Counter Current (KCC)
6.1. Deviations From the Kuroshio: The Global Drifter Data Set
Relative positions between release sites and the Kuroshio, as well as local oceanic currents, are responsible forthe transition to LDPs (section 5). The next question to be answered may be how are the LDPs that were once
entrained in the Kuroshio extracted from the current to approach the islands? Before examining the model
result, it is possible to make use of the satellite-tracked surface drifter data set from the GDP (section 5.1)
to analyze the cross-Kuroshio (transverse), southeastward transport of
the GDP drifters entrained once in the Kuroshio. Given the large numberof the drifter trajectories observed from 1972 to 2017, the drifters thatentered the red box once and then crossed the red transect wereextracted (Figure 11a). This operation extracted a total of 269 drifters thatwere trapped and transported long distances by the Kuroshio in the study
area. Blue curves in Figure 11a show their trajectories. A further selection
was performed for the 269 drifters to extract the LDPs that crossed at least
one of the six transects shown by the magenta lines in Figure 11b. The
magenta transects depict connections between approximate locations of
the Kuroshio axis, which were deﬁned as the zonal positions where themaximum surface velocity occurs every 0.5° meridional bin between
26°N and 29°N in close proximity to the Kuroshio path. Sixty-three drifters
out of 269 were found to have crossed the magenta lines from west toeast, and 24 drifters drifted southward after crossing the magenta lines.
This result quantiﬁes that ~23% (i.e., 63/269) of the LDPs entrained in the
Kuroshio are expelled and travel eastward, and ~9% (24/269) are subsequently transported southward.

Figure 13. Surface kinetic energy wavenumber spectra for winter (February)and spring (May) in the magenta box in Figure 1a.

UCHIYAMA ET AL.

Figure 12 represents seasonal variability of the number of the drifters
crossing the six magenta transects and then traveling southward.
Twenty-two drifters out of 24 crossed southern transects 1–4 close to9259

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Okinawa Island where the higher averaged Lagrangian PDFs extended
westward from the Kuroshio path (Figure 8 and section 5.2). No driftercrossed transect 5, while the largest number of drifters crossed transect
4. Therefore, the separation of the LDPs from the Kuroshio and subsequent
southward transport associated with the KCC opposed to the Kuroshio are
crucial for promoting landward particle transport and thus interisland connectivity. Therefore, seasonal variability in Figure 12 depicts the period
when more drifters are entrained in the KCC. The separation of the LDPs
from the Kuroshio has frequently occurred in warmer seasons, most evidently in spring along transect 4.
A preliminary model experiment conducted in Uchiyama et al. (2016) suggested that the KCC developed between the Kuroshio and the Nansei
Archipelago is accompanied by seasonally varying mesoscale eddies withdiameters of O (100 km), which emerged most notably in spring. Figure 13
presents wavenumber kinetic energy spectra of surface currents from the
Figure 14. Averaged Lagrangian PDFs of the trapped LDPs over the advecROMS-L2 model analyzed over the magenta box shown in Figure 1a fortion time of 21 days from experiment 2 (color). Trajectories of two in situ
warm (boreal spring, May) and cold (boreal winter, February) seasons.
drifters in the global drifter program data set that crossed transects 3 and 4(Figure 11b) and then entered the magenta circle (blue curves) are depicted. The spectral slope in spring is steeper than in winter with signiﬁcantly less
Red dots are the ﬁnal position of the two GDP drifters. Gray contours repre- energized kinetic energy for higher wavenumber components (i.e., subsent the ﬁve-week averaged surface velocity of 0.5 m/s.
mesoscale) with wavelengths <O (10 km), while the lower wavenumber
components (mesoscale) are decreased in winter. This result indicates that
the KCC is likely accompanied by mesoscale eddies that develop in warmer seasons and propagate southwestward as Rossby waves. In turn, down-front, northerly seasonal wind and surface cooling in winter induce
symmetric and baroclinic instability, leading to the emergence of submesoscale eddies (e.g., Kamidaira et al.,2018; Sasaki et al., 2014), which act to break up mesoscale eddies into smaller eddies. These results suggest

Figure 15. Averaged Lagrangian PDFs of the trapped LDPs (i.e., long-distance particles trapped by the KCC) for each year.
Magenta circles: the domain for extracting the trapped LDPs as deﬁned in Figure 14. The blue line in (b) is the transect for
the KCC volume ﬂux analysis in Table 5.

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Table 5Comparison Between the Time-Averaged Volume Transport of the KCC in
6 3
Sverdrup (Sv; 10 m /s) and the Rate (%) of the Trapped LDPs Over the Total
Number of Particles Released From the Lagoon, for Each of the Four
Model Years
Year

2012

2013

2014

2015

Mean volume transport of the KCC (Sv)
Trapped LDPs/total particles × 100 (%)

3.94
0.70

6.24
0.03

7.02
1.31

17.21
2.89

Note. See the main text for detail.

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that development of the KCC is more stable and persistent in spring, thanother times of the year, when submesoscale activities in the upper ocean
are lower. Evidently, this season corresponds to when coral spawning
occurs. Less disturbed mesoscale eddies act to enhance lateral transverse
mixing that promotes the eastward zonal transport of coral spawn and larvae expelled from the Kuroshio toward the west coast of the
Nansei Archipelago.
6.2. LDPs Entrained by the KCC

Finally, we further examined the model result (experiment 2; Table 3) byextracting the LDPs entrained in the KCC near Okinawa Island that were
responsible for interisland remote connectivity between the Yaeyama Islands and Okinawa Island. In doing
this, we deﬁne the magenta circle as shown in Figure 14 and extracted the drifters released from the
Yaeyama Islands that have entered the circle. Colors indicate the averaged Lagrangian PDFs of the LDPs
trapped in the KCC (referred to as “trapped” LDPs) for the advection time of 21 days from experiment 2(Table 3), while blue curves are the trajectories of two in situ GDP drifters crossing the circle and drifted
toward the west coast of Okinawa Island. The separation of the two in situ drifters from the Kuroshio has
occurred on 13 July 2011 and 26 June 2013, viz., in warmer seasons. The distribution of the averaged
Lagrangian PDFs of the trapped LDPs (colors) and the trajectories of the two in situ drifters (blue curves)
are closely correlated each other, reassuring that the model does a moderately good job in a climatologicalsense. The trajectories of the two drifters abruptly separated from the Kuroshio were entrained in the KCC
and drifted southeastward, similarly to the modeled PDFs.
Figure 15 shows averaged Lagrangian PDFs of the trapped LDPs for each of the four model years. Table 5summarizes the rate (in %) of the trapped LDPs relative to the total number of the released particles, andthe time-averaged southwestward volume transport (in Sv) of the KCC across the transect shown by the blueline in Figure 15b. The period for the time-averaging operation is 35 days including 14 days of the particles
releasing period plus the advection time of 21 days, and thus for the entire spawning period in spring.
Overall, the rate of the trapped LDPs is linearly correlated with the KCC volume transport, except for 2013.
In spring of 2013, the LDPs were much less as shown in Figure 15b because of the considerably reduced
LDPs associated with the eastward currents near the release site (section 5.3). The KCC transport is largest
in 2015 with the increase of the trapped LDPs of ~3%. This rate is nontrivial because 3% means that threecoral individuals out of 100 born in the lagoon travel more than 400 km to reach Okinawa Island anisotropically only within 21 days. The KCC intensity largely contributes to enhancement of the zonal particle transport
from the Kuroshio to Okinawa Island, and thus, to increase of the interisland remote connectivity.

7. Discussion and Conclusions
Toward optimal preservation and restoration of the coral habitats around the Nansei Archipelago, the ECS,
we quantiﬁed the coastal connectivity of corals across the islands using a double-nested high-resolution
synoptic ocean model based on ROMS, coupled with a 3-D off-line Lagrangian particle tracking model. Our
central scientiﬁc question posed was how interisland, remote connectivity is provoked. We successfully
demonstrated that long-distance larval transport and resultant interisland connectivity were primarily
induced by the Kuroshio that persistently drifts northeastward on the continental shelf break of the ECS near
the study area.
The connectivity matrix of 19 major islands and one lagoon over the archipelago indicated that most of theparticles (corals) remain near the released area. However, a modest amount of the particles drifts primarily
northeastward, in the direction of the Kuroshio, and ﬁnally reaches other islands in the advection time of
21 days. The interisland connectivity is apparent even for pairs of islands ~1,000 km apart from each other,with tolerable southwestward connections as well. These primary ﬁndings are consistent with previous
genetic population studies. For instance, Nakajima et al. (2010) examined the population genetics of A. digitifera using highly variable markers at 19 sites in seven regions along the Nansei Archipelago. They conﬁrmed
the existence of high genetic connectivity with signiﬁcant genetic differentiation among populations in geographically close sites and regions, without losing genetic diversity, leading to complex and various connective patterns. Such disagreement between genetic connectivity and geographic distance in the A. digitifera

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populations were also reported in Nishikawa and Sakai (2005) within the southern Nansei Archipelago.
Shinzato et al. (2015) concluded from the decoded genome of A. digitifera that the population structure in
the southern Nansei Archipelago is inﬂuenced by infrequent long-distance dispersal over the SRA. They
detected strong historical migration from the Yaeyama Islands (YIs) to Okinawa Island. Zayasu et al. (2016)
reported from 13 microsatellite markers of A. tenuis sampled at 15 location across the Nansei Archipelago thatgenetic population structure is complex as gene ﬂow not only occurs from south to north but also from northto south in various locations. Hence, the YIs and the Amami Islands (the islands #3–7 in Table 1) are potential
northern and southern sources of corals, while Miyako Island (#11) and west central Okinawa Island arepotential sink populations.
The present numerical study provides the mechanism responsible for maintaining the interisland connectivity with experiment 2, where corals were released from the YIs. The larval dispersal was asymmetrically elongated in the direction of the Kuroshio and is consistent with the trajectories of the in situ surface drifters. One
deployed in situ drifter out of six was entrained in the Kuroshio and traveled a considerably long distancenortheast for ~1,000 km in 21 days, as solid evidence of a key role played by the Kuroshio. Although the corals
released from the northern coasts of the YIs were readily entrained in and transported long distances by the
Kuroshio, those from the southern coasts resulted in notably suppressed entrainment due to the prevailingeastward currents invoked upon the releases. With a guide of the satellite-tracked global surface drifter dataset, the model conﬁrmed separation of particles from the Kuroshio and subsequent eastward transport to thewestern shore of Okinawa Island, occurring particularly in warmer seasons. A key inﬂuencer of eastward zonaltransport is the Kuroshio Counter Current (KCC), which is formed between the Kuroshio and the Nansei
Archipelago and drifts southwestward. The larger volume the KCC has to transport, the more intensive
separation from the Kuroshio occurs.
Previous numerical connectivity studies for other areas with abundant corals, such as the Great Barrier Reef
(GBR), Australia, generally have concluded that local larval transport is relatively important and primarily
unrelated to ambient currents. For the GBR and other coastal sites around Australia, recent numerical connectivity studies (e.g., Hock et al., 2014; Kool & Nichol, 2015; Wood et al., 2014) mainly have focused on sensitivity
to individual behavior models (e.g., diel vertical migration), while dynamical roles in population connections
have been poorly explored. This is partly because, for settlement of larvae responsible for coral connectivity
along GBR, neashore retention, or so-called “stickiness,” is believed to be most crucial in trapping larvae ascompared to dynamical effects from offshore currents (e.g., Wolanski et al., 1989; Wolanski and Kingston,2014). However, a satellite observation suggested a possible implication of topographically generated
mesoscale eddy associated with the southward drifting East Australia Current for ecological linkage in thesouthern GBR (Weeks et al., 2010). By contrast, Garavelli et al. (2016) analyzed larval dispersal along theChilean coasts based on a climatological ROMS model output at 4-km lateral resolution. They demonstrated
that advective dynamics predominantly controls the major separation feature of the coastal population connection, which always occurred at around 29–30°S, regardless of choices of behavior models. Another ecologically important coral habitat, the Florida Keys in the Straits of Florida, USA, was reported to be inﬂuenced
both by two branches of the Gulf Stream, that is, the Loop Current in the Gulf of Mexico and the eastward
drifting Florida Current in the Florida Straits, based on a multinested high-resolution HYCOM model(Kourafalou et al., 2018; Sponaugle et al., 2012). Dominant transport occurred eastward along the Florida
Current, whereas a weak upstream (westward) transport was also observed (Paris et al., 2013). These studies
have anticipated that the balance between these two major currents alters the Southwestern Florida Shelf
circulation and associated mesoscale eddies, leading to substantial impacts on the regional
coral connectivity.
The present study in the Nansei Archipelago and the previous studies in other areas have suggested that theintensive advective transport due to persistent and/or transient oceanic currents and associated eddies could
be crucial factors that control the remote population connectivity. However, detachment from and settlement in the nearshore areas are another yet to be fully understood processes in literature. We evidently presented insights into such processes affected not only by the Kuroshio but also by local dynamics accounting
for the entrainment rate of pelagic particles in the Kuroshio upon the release, secondary processes such as
those by the KCC responsible for transverse expelling transport from the Kuroshio to islands, and seasonallyvarying submesoscale dynamics. As have pointed out by Werner et al. (2007), Siegel et al. (2008), and Whiteet al. (2010), we emphasize here that numerical connectivity studies require high-resolution models,

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preferably down to submesoscale eddy-resolving resolutions with grid spacing of ~1 km (e.g., Kamidaira et al.,2018), to properly account for advective effects to minimize uncertainty of overly simpliﬁed diffusive transport assumption in genetic approaches.
Furthermore, it is worth noting that effects from surface gravity waves are omitted in the present model, eventhough waves are anticipated to substantially inﬂuence regional and local circulations and the associatedcoral transport through Stokes advection, the Stokes-Coriolis force, and wave setup and littoral currents
induced by breaking waves (e.g., Uchiyama et al., 2010). These factors respectively contribute to altering
Lagrangian particle transport through additional Stokes drift velocity, modifying the geostrophic andEkman balances particularly in the upper oceans, and changing nearshore hydrodynamics and resultant particle transport near reefal edges through sporadic offshore eruption by transient rip currents and/or energetic
alongshelf transport by wave-driven longshore currents (e.g., Uchiyama et al., 2009; Uchiyama, Kanki, et al.,2017; Weir et al., 2011). As a consequence, it is hypothesized that the coral transport and resultant connectivity may be altered by (1) additional wave-induced residual coral transport developed in the direction of wave
and swell propagation and (2) more pronounced cross-shore exchange and alongshore transport in the nearshore shallow areas. The former may introduce large-scale biases in the coral transport pattern, while the latter may enhance the coral recruitment in the spawning and potential settlement sites around reefal edges,
which are germane to maintaining abundant coral community. A quantitative assessment of these factors
is beyond the scope of the present study, but they must be taken into account in future works, in particularwhen models will have much higher resolutions at O (10 m) to sufﬁciently resolve the surf zone.

Acknowledgments
This research was ﬁnancially supportedby the Japan Society for the Promotionof Science (JSPS) Grant-in-aid for
Scientiﬁc Research (grants 15H04049,
15KK0207, and 18H03798 at Kobe
University and 17K00653 at Kobe
Gakuin University). The supplementaryﬁles contain simulation data that enable
the ﬁgures to be reproduced. The
model output and computing codesused in this study are available from theauthors upon request (uchiyama@harbor.kobe-u.ac.jp). The JCOPE2 reanalysis
data set is freely available for scientiﬁc
purposes upon request to APL/JAMSTEC(jcope@jamstec.go.jp). The
STRM30_Plus data are available at ftp://
topex.ucsd.edu/pub/srtm30_plus/. The
J-EGG500 data set is distributed by the
JODC at http://jdoss1.jodc.go.jp/cgibin/1997/depth500_ﬁle. The mapped
sea level anomaly in delayed-time product was generated by Ssalto/Duacs
and distributed by AVISO (ftp.aviso.altimetry.fr). The GPV-GSM/MSM atmospheric product was generated by the
JMA, and it is available from the repository at http://database.rish.kyoto-u.ac.
jp/arch/jmadata/data/gpv/original/. The
COADS data are maintained and distributed by NOAA at https://www.esrl.
noaa.gov/psd/data/gridded/data.
coads.1deg. The Global Drifter Program
data set is processed and distributed at
http://www.aoml.noaa.gov/phod/dac/
index.php by NOAA/AOML.

UCHIYAMA ET AL.

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