Ecology Letters, (2020) 23: 881–890

METHODS

Jun Matsubayashi,1,2*

doi: 10.1111/ele.13496

Tracking long-distance migration of marine fishes using
compound-specific stable isotope analysis of amino acids

YutakaOsada,1,3* Kazuaki Tadokoro,4
Yoshiyuki Abe,5,6 Atsushi
Yamaguchi,5 Kotaro Shirai,6Kentaro Honda,7 Chisato
Yoshikawa,2 Nanako O. Ogawa,2Naohiko Ohkouchi,2 Naoto F.
Ishikawa,2 Toshi Nagata,6 Hiroomi
Miyamoto,4 Shigeto Nishino2 andIchiro Tayasu1
The peer review history for thisarticle is available at https://
publons.com/publon/10.1111/

Abstract
The long-distance migrations by marine fishes are difficult to track by field observation. Here, we
propose a new method to track such migrations using stable nitrogen isotopic composition at thebase of the food web (d15NBase), which can be estimated by using compound-specific isotope analysis. d15NBase exclusively reflects the d15N of nitrate in the ocean at a regional scale and is notaffected by the trophic position of sampled organisms. In other words, d15NBase allows for directcomparison of isotope ratios between proxy organisms of the isoscape and the target migratory
animal. We initially constructed a d15NBase isoscape in the northern North Pacific by bulk andcompound-specific isotope analyses of copepods (n = 360 and 24, respectively), and then we
determined retrospective d15NBase values of spawning chum salmon (Oncorhynchus keta) fromtheir vertebral centra (10 sections from each of two salmon). We then estimated the migrationroutes of chum salmon during their skeletal growth by using a state-space model. Our isotope
tracking method successfully reproduced a known chum salmon migration route between the
Okhotsk and Bering seas, and our findings suggest the presence of a new migration route to theBering Sea Shelf during a later growth stage.

ele.13496

Keywords
Bering Sea Shelf, copepods, isoscape, migration, North Pacific, salmon, d15NBase.
Ecology Letters (2020) 23: 881–890

Long-distance migrations by highly mobile animals are oneof the most stunning and threatened biological phenomenain the world, and have long fascinated scientists (Huntsman
1948; Abrahms et al. 2019). These migrations also play
important ecological roles by increasing nutrient flow andconnectivity between disparate locations (Bauer & Hoye2014); however, they are highly vulnerable to disruption by
habitat alterations caused by ongoing climate change (Berger
2004; Robinson et al. 2009). Tracking the migration routesof highly migratory animals is a necessary step to predict the
impact of climate change as well as to boost understandingof the ultimate evolutionary drivers for such migratory behaviour.
Long-distance migrations are common across many taxa
(Brower 1995; Robinson et al. 2009; Block et al. 2011). Unfortunately, many of these taxa, including marine animals, aredifficult to track using field observation. Most of our knowledge of marine animal migration is based on fishing surveys,
tagging studies and telemetry data (e.g. Carey & Scharold
1990; Quinn & Myers 2004; Semmens et al. 2007). However,there are several shortcomings to these techniques, including

the enormous effort, cost, and difficulty of data collection, thelimited life and detection range of biologging devices, and limitations on the body size of target animals (Cooke et al.
2013). Recently, stable isotope analysis has attracted attention
as a new tool that could overcome these limitations in marine
animals (e.g. Trueman et al. 2012; Trueman & St. John Glew2019).
Isotope ratios in organism tissues often serve as intrinsic
markers of habitat because environmental isotope ratios of
one or more elements (usually) differ among geographic locations (e.g. Hobson 1999; Bowen et al. 2005; Bowen 2010).
Data on spatial gradients in isotope ratios of a specific element can be used to draw what is known as an ‘isoscape’(Bowen 2010). To be useful in the study of animal migration,
the specific isotope used to construct an isoscape must meetthe following criteria (Hobson et al. 2010): (1) substantial spatial variations are found in isotope ratios within the animal’s
habitat range, (2) isotope fractionation between the ambientenvironment and animal tissue can be corrected, and (3) thetime period of spatial integration corresponding to a particular animal tissue is known. Stable nitrogen isotope ratios(d15N) at the base of the food web (d15NBase) may meet thesecriteria in marine ecosystems.

1

Research Institute for Humanity and Nature, 457-4 Motoyama, Kamigamo,

5

Kita-ku, Kyoto 603-8047, Japanchi, Hakodate, Hokkaido 041-8611, JapanINTRODUCTION

2

Graduate School of Fisheries Sciences, Hokkaido University, 3-1-1 Minatoma-

Japan Agency for Marine-Earth Science and Technology, 2-15 Natsushima-

6

Cho, Yokosuka, Kanagawa 237-0061, Japan

Kashiwanoha, Kashiwa-shi, Chiba 277-8564, Japan

3

Graduate School of Life Sciences, Tohoku University, 6-3 Aramaki-Aoba,

7

Aoba-ku, Sendai, Miyagi 980-8578, Japan

Education Agency, 2-2 Nakanoshima, Toyohira-ku, Sapporo, Hokkaido, 062-

4

Tohoku National Fisheries Research Institute, Japan Fisheries Research and0922, Japan

Education Agency, 3-27-5, Shinhama-cho, Shiogama, Miyagi 985-0001, Japan*Correspondence: E-mail: matsu-jun@jamstec.go.jp and yosada@gmail.com

Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5,
Hokkaido National Fisheries Research Institute, Japan Fisheries Research and© 2020 John Wiley & Sons Ltd/CNRS

882 J. Matsubayashi et al.

d15NBase values are calculated through d15N analysis ofamino acids (d15NAAs; a technique that is often used to estimate trophic position [TP] in marine animals) by correcting
for TP-dependent d15N increases in phenylalanine (d15NPhe).
Unlike d15N values in bulk tissue (d15NBulk), which increase
with TP (DeNiro & Epstein 1981; Minagawa & Wada 1984),
d15NBase reflects the d15N of nitrate consumed by phytoplankton at a regional scale (Ohkouchi et al. 2017). Therefore, it is
not influenced by the TP of the sampled organism. Substantial
spatial variations in d15NBase are likely to exist within the
North Pacific because several studies have shown large spatial
variations in nitrate d15N in the region (e.g. Schell et al. 1998;
Pomerleau et al. 2014; Yoshikawa et al. 2018). Thus, d15NBase
meets criteria (1) and (2).
Recent studies have developed a method to perform retrospective d15N analysis in teleost fishes using their vertebrae(Matsubayashi et al. 2017, 2019). Fish vertebrae exhibit incremental growth, and isotopes incorporated into the bone collagen of each vertebral section remain in place for a long timein the absence of active metabolic tissue replacement. For thisreason, segmental isotope analysis of vertebral sections canreconstruct retrospective d15NBase values during periods ofskeletal growth, satisfying criterion (3). Owing to these advantages, d15NBase values can be a direct indicator of migrationroutes in fishes and other marine animals.
Here, we present a new method for identifying the migration route of highly migratory marine fishes by using
d15NBase isoscapes in conjunction with retrospective isotope
analysis of individuals. We chose chum salmon (Oncorhynchus keta) as the target fish in this study. The species’
migrations are known to take individuals over 10,000 km
offshore of their natal streams (Swain et al. 1962; Urawa
et al. 2000, 2005; Seeb et al. 2004). In previous studies, themigration routes of Asian populations of chum salmon were
investigated by conducting extensive fishing surveys in the
North Pacific (Urawa et al. 2000, 2005; Seeb et al. 2004).
These studies caught salmon at multiple sampling stationsin the North Pacific in several seasons, and investigatedtheir origin by genetic analysis. They suggested that chumsalmon initially run to the Okhotsk Sea after their seaward
migration until late autumn, then migrate to the Western
Subarctic Gyre in winter, and move to the Bering Sea by
the following summer. Then, they come and go between the
Bering Sea and Alaska Gyre in summer and winter, respectively, for several years. Finally, mature chum salmon
return to Japan from the Bering Sea at around ocean age 4(Fig. S1).
We constructed an isoscape of d15NBase in the North
Pacific using the d15NBulk and d15NAAs of copepods collected from the migration area of chum salmon. Then, we
performed retrospective d15NAAs analysis of vertebral sections extracted from bodies of spawning salmon, and measured d15NBase for successive growth stages of each fish
(Matsubayashi et al. 2017, 2019). Finally, we developed astate-space model to estimate migration routes of individual
salmon by using d15NBase isoscapes in the North Pacific andd15NBase time-series in salmon, and compared the resultswith a known migration route of chum salmon from Japanto the Bering Sea.
© 2020 John Wiley & Sons Ltd/CNRS

Methods

MATERIALS AND METHODS

Zooplankton and chum salmon sample collection

Zooplankton samples were obtained from the Odate Collection(Odate 1994) (n = 305; sampled from 2000 to 2018) of Fisheries
Research and Education Agency (FRA), and additional samples were collected by the Hokkaido University training ship
(Oshoro Maru; n = 32) in 2002 and 2012. They were stored informalin until analysis; according to Ogawa et al. (2013), formalin preservation does not influence the d15N values of bulk
and compound-specific isotope analyses. We also obtained frozen zooplankton samples (n = 17) from the Hokkaido National
Fisheries Research Institute. These samples were collected during a 2017 cruise of the Hokko Maru in the Bering Sea. Fromeach sample bottle, we selected one of the following six copepod
species, which are dominant in the North Pacific: Neocalanus
cristatus, N. plumchrus, N. flemingeri, Oncaea venusta, Paracalanus parvus, and P. aculeatus (Table S1). Copepod samples
were rinsed with pure water twice and freeze-dried. Then, we
removed the gut of large copepods (N. cristatus, N. plumchrus,
and N. flemingeri) and placed the copepods in tin capsules forisotope analysis. Because it is technically difficult to excise thegut contents of small copepods (Oncaea venusta, Paracalanus
parvus, and P. aculeatus), we used whole body samples of these
species for isotope analysis. These six species have relatively
long lifespans ranging from 1 month to 2 years (Paffenh€
ofer
1993; Tsuda et al. 2001), which enables us to obtain timeaveraged isotopic information. We also collected d15Nbulk data
for these species from existing literature (Pomerleau et al. 2014).
Bone samples were obtained from three individuals of chumsalmon (IDs: OK1–3) collected at the Nukkibetsu River, Hokkaido, Japan (42.59 °N, 140.70 °E), in December 2016. We could
not identify the age, sex, or origin (wild or hatchery reared) of
these samples because we were not provided with any non-bonetissues. We also obtained three individuals of chum salmon fromthe Chitose River, Hokkaido (42.80 °N, 141.56 °E, IDs: OK4–6)
from the Hokkaido National Fisheries Research Institute, FRA,
as well as two individuals from the Otsuchi River (39.37 °N,
141.90 °E, IDs: OK7–8), Iwate, Japan. All salmon from the Chitose River were identified as hatchery salmon based on their otolith labels, whereas the origin of salmon from the Otsuchi Riverwas unclear. Age and sex were identified for samples of both origins (Table S2). We extracted vertebral centra from each salmon,
subdivided them into 10 sections and extracted bone collagenfrom the sections (see Text S1).
Stable nitrogen isotope analysis of bulk tissue

The stable isotope ratios of nitrogen for all samples were measured using one of two elemental analyser/isotope-ratio mass
spectrometry systems depending on the volume of each sample (see Text S2). Stable isotope ratios are expressed in dnotation in accordance with the international standard scale,on the basis of the following equation:
d15 NBulk ¼ Rsample =Rstandard 1;ð1Þ

where Rsample is the 15N/14N ratio of the sample, and Rstandard
is that of atmospheric nitrogen. The d15N values were

Methods

Isotope tracking of migration in salmon 883

calibrated against those of alanine A (d15N = –2.89&;
CERKU-01), alanine B (d15N = 22.71&; CERKU-02), andthreonine (d15N = –2.88&; CERKU-05) laboratory standards(Tayasu et al. 2011). The maximum analytical standard deviations (SD) of these standards were lower than 1.07& for alanine A (n = 40), 0.63& for alanine B (n = 54), and 0.15& forthreonine (n = 8).
Stable nitrogen isotope analysis of amino acids

The d15NAAs measurements were performed only for some
copepods and chum salmon samples to minimize experimental effort. Specifically, the sample size of salmon used for
d15NAAs measurements was strictly limited because it requires
a ten-fold increase in the number of bone collagen samples
due to retrospective isotope analysis. For this reason, we
selected a representative three to six samples from six copepod species for d15NAAs measurements so as to cover the
d15NBulk range of each species, and collagen samples fromthe bone sections of two individuals of O. keta (IDs: OK2
and OK8) were used for d15NAAs measurements. Samples for
d15NAAs were prepared based on amino acid derivatisation
procedures, and the d15NAAs values were determined by gas
chromatography/combustion/isotope-ratio mass spectrometry(See Text S3).
Calculation of trophic position and d15NBulk of each sample

Trophic position (TP) of samples with d15NAAs data were estimated based on differences in d15N between glutamic acidand phenylalanine as follows (Chikaraishi et al. 2009):

ð2Þ
TPGluPhe ¼ d15 NGlu d15 NPhe 3:4 =7:6 þ 1;
where d15NGlu and d15NPhe represent the d15N values of glutamic acid and phenylalanine, respectively. We also calculated
d15NBase by using the estimated TPGlu-Phe and the trophic discrimination factor of phenylalanine (0.4&) as follows:
d15 NBase ¼ d15 NPhe 0:4  ðTPGluPhe 1Þ:ð3ÞWe then performed linear regression analysis to explain
variations in the d15NBase of each species as a function of
d15NBulk as follows:
d15 NBase ¼ d15 NBulk  a þ b;

ð4Þ

where a and b were derived from the liner regression equation.
In cases where the correlation between d15NBase and d15NBulk
in each equation was high (i.e., r2 > 0.8; see Fig. S2), theregression equations were used to calculate d15NBase values ofcopepod samples for which d15NAAs was not measured.
Construction of a d15NBase isoscape

d15NBase values estimated from copepod d15NBulk and d15NAAs
were interpolated by using Data Interpolating Variational
Analysis (DIVA) gridding software (Barth et al. 2010), andwere visualized with Ocean Data View version 5.0.0 (ODV:
Brown 1998; Schlitzer 2002). DIVA takes into account coastlines and bathymetric features and subdivides the domain on
which estimation is performed. DIVA parameters for theinterpolation were as follows: mapping range = 30–75°N and
135 °E–140 °W; scale lengths (x, y) = 30, 30; quality limit(standard deviation) = 1.5; signal-to-noise ratio = 40; depth ofisosurface = 20 m; outliers excluded. Coastlines in the maps
were based on the Global Self-consistent Hierarchical Highresolution Shorelines database v 2.1 (Wessel & Smith 1996).
Deviations between measured (from copepod samples) andpredicted (using DIVA gridding software) d15NBase were also
calculated by using ODV.
As a potential explanatory variable for spatial gradients inzooplankton d15NBase, we also estimated phytoplankton d15N
(d15NesPhy) in the summer across the North Pacific using
nitrate utilisation data (see Text S4, Fig. S3). We estimatedd15NesPhy for each data point by using Supplementary Equation (1), and interpolated the data onto a 1° grid throughout
the North Pacific (Fig. S3). We did not estimate d15NesPhy inareas of the eastern Bering Sea where the water depth is lessthan 100 m, as sedimentary denitrification also influences
d15NesPhy in such regions. Then, we calculated average
d15NBase in each 1° grid so that d15NBase and d15NesPhy could
be compared at the same spatial scale. A scatter plot of these
gridded isotopic values suggested that d15NBase and d15NesPhy
are nonlinearly related (Fig. S4). For this reason, we fitted alinear regression using a squared term of d15NesPhy, and thebest model was selected based on the Akaike Information Criterion (AIC). The modeling procedure was conducted using R
version 3.5.3 (R Core Team 2019).
Estimation of chum salmon migration routes

We developed a state-space model to estimate the location(i.e., latitude and longitude) of each individual salmon at eachgrowth stage (i.e., vertebral section) using the d15NBase isoscape and retrospective d15NBase values of salmon. Our model
relies on several key assumptions. First, vertebral stable isotope ratios of chum salmon can be compared with those of
the isoscape. This assumption is needed in order to conclude
that a chum salmon existed at a location whose isotope valueon the isoscape is similar to that of the vertebral section, and
it is supported by our contention that the d15NBase cancels thetrophic discrimination between diet and consumer, and exclusively reflects the isotopic values in specific geographic locations. It is important to note that this assumption also
provides the implicit model constraint that chum salmon cannot migrate outside of the constructed isoscape. Second, isotope values in each vertebral section are unchanged from thevalues at the time when each section was created. Matsubayashi et al. (2017, 2019) provided evidence of a minimal
effect from replacement-based turnover in collagen from vertebral bone in fishes. Third, the natal river of individual salmon is the same as the sampled river. This assumption was
based on the highly developed homing ability of chum salmon. Lastly, we assumed that the spatial scale at which chumsalmon migrate is related to the growth stage of each individual, as has been shown in sockeye salmon (O. nerka) (Brett1965). Using these assumptions, our model describes the spatial scale of migration as a linear function of the number ofvertebral sections as counted from the vertebral centra (asexplained below). In this model, we did not account for© 2020 John Wiley & Sons Ltd/CNRS

884 J. Matsubayashi et al.

Methods

individual variations in migration spatial scale because thenumber of individuals used for d15NAAs was small (n = 2).
A time lag exists between the consumer feeding on a specificprey and the atoms from that prey being incorporated into
the fish’s bone collagen, although the length of this time lad
has not been assessed. Therefore, our model estimates thelocation of salmon at which the unknown assimilation time
lag has been subtracted from the time when each vertebral
section was created. Let xi;j be the location (i.e., latitude andlongitude) at which individual i consumed the prey before thelapse of the unknown assimilation time lag from vertebral section j was created (index j is the number of the vertebral section as counted from the centre of vertebral centra). Becausechum salmon migrated from xi;j1 to xi;j at their growth stagej  1, it is biologically plausible to assume that xi;j is likely to
be in the vicinity of xi;j1 . This assumption is often used in
ecological models that infer the movements of organisms.
Thus, the probability with which each individual at eachgrowth stage existed at location xi;j on an isoscape can be
written as follows using the formula for a Gaussian distribution:

2 !


d xi;j1 ; xi;j
1
2
;
ð5Þ
p xi;j jxi;j1 ; aj ¼ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ exp
2a2j12pa2
j1

where aj is the migration scale when section j was created,and dðx1 ; x2 Þ represents the geographic distance between x1
and x2 . Using the last assumption explained above, the migration spatial scale is formulated as a linear function of thenumber of vertebral sections from the vertebral centra:
aj ¼ expða þ b logðj þ 1ÞÞ;

ð6Þ

where a and b are the intercept and coefficient of the linear
function, respectively. We defined the migration spatial scaleat the initial growth stage as expðaÞ. Positive b values indicatethat the migration spatial scale increases with somatic growth,and vice versa. Brett (1965) used a similar exponential formulation to describe the relationship between cruising speed andbody size in salmon. To model the probability at which the
d15NBase of the vertebral sections, yi;j , is observed at a location, xi;j , on the isoscape, we used the formula for a Gaussian
distribution as follows: 2 !

yi;j  g xi;j
1
2
;
ð7Þ
p yi;j jxi;j ; ri;j ¼ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ exp
2r2i;j
2pr2
i;j

r2i;j

15

where
is the mean squared errors of the d NBase (bonecollagen and isoscape), and gðxÞ represents the d15NBase of
location x on the isoscape.
Our model was assimilated using a numerical integration filter algorithm (Kitagawa 1987), which allows us to analyticallycompute the likelihood of a non-linear state-space model. To
implement the numerical integration filter, the constructed isoscape was discretized into 5777 grid cells of dimensions 0.5
degrees latitude by 0.5 degrees longitude. The geographic distance between grid-cell centres was computed using the R
packages ‘geosphere’ (version 1.5-10, Hijmans 2019) and‘igraph’ (version 1.2.4.2, Csardi & Nepusz 2006). All statisticalanalyses were conducted using R version 3.5.3 (R Core Team© 2020 John Wiley & Sons Ltd/CNRS

2019). Migration routes were estimated for two individual salmon (IDs: OK2 and OK8) that were sampled for d15NAAs
analysis.

RESULTS

Construction of a d15NBase isoscape

We obtained d15NBulk and d15NAAs values for six copepod
species (N. cristatus: n = 123 and 6, respectively; N. plumchrus:n = 54 and 5; N. flemingeri: n = 27 and 3; O. venusta: n = 43
and 3; P. aculeatus: n = 11 and 5; P. parvus: n = 77 and 3),
from our own samples (n = 354) and data from the literature(Pomerleau et al. 2014) (n = 6). Based on d15NAAs, the mean
TPs of each copepod species were 2.7 (range: 2.5–2.9) for N.
cristatus, 2.6 (2.4–2.9) for N. plumchrus, 2.7 (2.4–2.8) for N.
flemingeri, 2.2 (2.1–2.5) for O. venusta, 2.3 (1.9–2.6) for P.
parvus, and 1.9 (1.9–2.0) for P. aculeatus (Table S3). The
d15NBase and d15NBulk were highly correlated in each species(r2 = 0.905 for N. cristatus, 0.827 for N. plumchrus, 0.993 for
N. flemingeri, 0.974 for O. venusta, 0.871 for P. parvus, and
0.818 for P. aculeatus; Fig. S2). Therefore, the d15NBase values
were calculated using the regression equations with d15NBulk
data (eqn 4). The slope and intercept of the species (a and b
in eqn 4) were 1.455 and –6.128 for N. cristatus, 1.229 and–7.010 for N. plumchrus, 1.866 and –11.496 for N. flemingeri,
2.400 and –11.028 for O. venusta, 0.977 and –5.242 for P. parvus, and 1.461 and –6.590 for P. aculeatus, respectively(Fig. S2).
The estimated isoscape showed a large spatial variation in
d15NBase in our study area (see Fig. 1). Specifically, thed15NBase was different among the Okhotsk Sea and coastal
area of Japan (2.5–5.0&), the central North Pacific (–5.0&
to 0.0&) and the Bering Sea Shelf (5.0–8.0&). Linear regression analysis showed that spatial gradients in d15NBase werelargely explained by d15NesPhy (explained deviance: 45.3%,
Fig. S4). Therefore, estimated d15NesPhy complemented
d15NBase values in areas of chum salmon habitat where zooplankton samples were not available, such as the Okhotsk
Sea and Gulf of Alaska (Fig. 1). The root-square-mean error
in d15NBase values between actual measurements (copepod
samples) and values predicted by DIVA gridding software
was 1.42&.
Retrospective stable isotope analysis of chum salmon

All sampled individuals shared common trends in d15NBulk
along the vertebral axis (Fig. 2): d15NBulk values were almost
identical (mean  SD: 10.5&  0.3&) in Section 1 (closest to
the middle of the centrum) among sampled individuals, thengradually decreased from the centre to middle sections,increased rapidly in the marginal sections, and finally reachedthe highest value in the last section (12.7&  0.7&;
Table S4).
The d15NAAs showed that the TP in the bone sections of
OK2 and OK8 ranged from 3.0 to 3.5 and from 3.0 to 3.4,
respectively, and no clear trends along the growing axis ofvertebral sections were found (Table S4). The d15NBase andd15NBulk values of vertebral sections of the two salmon

Methods

Isotope tracking of migration in salmon 885

Figure 1 Map of the estimated d15NBase isoscape showing the location of zooplankton sampling sites (white circles).

Figure 2 Patterns in d15NBulk along the vertebral sections of sampled Japanese chum salmon (IDs: OK1–8). Vertebral bone sections (x-axis) are numberedfrom the centre of the vertebral centrum and increase toward the margin.

individuals were highly correlated (r2 = 0.754 for OK2 and
0.916 for OK8).
Estimation of chum salmon migration routes

The estimated spatial scale of migration in salmon suggeststhat their mobility increases with somatic growth(a = 5.189  0.118 and b = 0.881  0.143, Fig. S5). The
state-space model successfully reproduced the long-distancemigration of two chum salmon individuals from the western
North Pacific (Fig. 3, orange areas in panels 1–5) to the Bering Sea (Fig. 3, orange areas in panels 6–9), supporting a previously reported migration pattern for this species (Urawa
et al. 2000, 2005; Seeb et al. 2004). In addition, we found thatboth salmon individuals migrated onto the Bering Sea Shelf
during the later stages of their growth (Fig. S5).

DISCUSSION

d15NBase isoscapes in the North Pacific

Our isoscape shows that the eastern Bering Sea continental
shelf has higher d15NBase (d15NBase > 7.0&; Fig. 1) than any
other potential habitat range of chum salmon. This highd15NBase is likely to be a result of coupled partial nitrification–denitrification in this region (Brown et al. 2015). Highrates of sedimentary denitrification fueled by active nitrification preferentially remove 14N from the system, thereby© 2020 John Wiley & Sons Ltd/CNRS

886 J. Matsubayashi et al.

Methods

Figure 3 Estimated chum salmon migration areas. Mean presence probabilities for two salmon individuals (IDs: OK2 and OK8) at growth stages 1–10

(panels 1–10, respectively). The colour gradients (tints of orange) indicate presence probability (low to high). The grey area shows the extent of theisoscape.

enriching 15N in the remaining nitrate (Granger et al. 2011).
Spatial patterns of d15NBase in the pelagic subarctic North
Pacific are largely determined by nitrate utilisation, which is
the ratio of nitrate assimilation by phytoplankton to nitrate
supply in the euphotic layer, and is inversely correlated withsurface water nitrate concentrations because of isotope fractionation during nitrate assimilation by phytoplankton(Fig. S3). In the western subarctic North Pacific, which is anutrient-rich, low-chlorophyll region, nitrate is not depleted
throughout the year and phytoplankton growth is instead limited by the availability of other micronutrients, such as iron
(Nishioka & Obata 2017). This results in less fractionation
associated with nitrate utilisation, which in turn accounts forthe low d15NBase in the Western Subarctic North Pacific(Yoshikawa et al. 2018). Nitrate concentrations decrease with
distance from the nutrient-rich, low-chlorophyll region andnitrate d15N increases with nitrate depletion (Yoshikawa et al.
© 2020 John Wiley & Sons Ltd/CNRS

2018), which may account for the increased d15NBase aroundthe eastern coast of Japan and the Gulf of Alaska. Unfortunately, the isoscape compiled in this study suffers from a paucity of data in the Okhotsk Sea and the western Bering Sea
Shelf. Although previous studies have suggested that nitrate
d15N in the Okhotsk Sea is similar to that of the eastern coastof Japan and not as high as that of the Bering Sea Shelf(Yoshikawa et al. 2006), it remains possible that d15NBase in
the western Bering Sea Shelf resembles that of the eastern
Bering Sea Shelf. Thus, we cannot discount the possibility
that the salmon analysed in this study migrated to the western
Bering Sea Shelf, although this would not affect the substanceof our conclusions.
Our results also suggest that d15NBase can be used as adirect indicator of the origin of marine organisms as suggested
by previous studies (Hetherington et al. 2017; McMahon &Newsome 2019). This is supported by d15NBase values in theMethods

first vertebral section (Section 1) of two salmon (OK2: 4.4&
and OK8: 3.6&) similar to that of the isoscape in coastal
Japan and the Okhotsk Sea (ca. 4&; Fig. 1). A previous study
also confirmed that Japanese chum salmon initially move into
the Okhotsk Sea just after their seaward migration (Urawa
et al. 2009). Large seasonal variations in the isotopic composition of primary producers (Bronk et al. 1994; Rolff 2000;
Hannides et al. 2009) often complicate estimations of the isotopic baseline in the ocean. However, copepods effectively
provide time-averaged d15NBase values in a given region
because they have relatively long lifespans (from 1 month to2 years) (Paffenh€
ofer 1993; Tsuda et al. 2001), low dispersal
ability, and low intra-species TP variation (Table S3). In ourstudy, copepod samples from the pelagic North Pacific were
mostly composed of large Neocalanus species, which have alifespan of 1–2 years (Tsuda et al. 2001). In coastal areas, we
sampled copepods that have a shorter lifespan across different
seasons to take any seasonal variation into account, and to
capture the annual mean d15NBase of each region.
Chum salmon migration routes

Our estimates of chum salmon migration routes successfully
reproduced a known migration pattern from coastal Japan to
the Bering Sea (Urawa et al. 2000, 2001; Seeb et al. 2004;
Whittle et al. 2018). More importantly, it is likely that chumsalmon move onto the Bering Sea Shelf at the last stage of
their somatic growth. Although the number of chum salmon
used for the d15NAAs analysis is limited (n = 2), we observed
similar isotopic profiles of d15NBulk in the vertebral sections of
all the salmon we sampled (n = 8). Furthermore, some ofthese salmon born in different years (Table S2) shared themigration onto the Bering Sea Shelf, indicating that thismigration pattern is not influenced by annual variation in thediets of chum salmon. Thus, our results suggest that the Bering Sea Shelf plays an important role for the growth at laterstages of chum salmon in Japan. Note, however, that the
migration routes estimated by stable isotope analysis did notreflect homing migrations to the Japanese coast from the Bering Sea, possibly because the vertebrae of the sampled individuals did not exhibit substantial growth during this homing
migration.
Given that fish somatic growth generally stops after sexual
maturation because they devote surplus energy to reproduction (Lester et al. 2004), salmon vertebrae are not likely togrow substantially after maturation; in other words, the timing of maturation corresponds to that of the formation of thelast vertebral section (Section 10). Ishida et al. (1998) investigated the relationship between age and fork length in chumsalmon and showed that they exhibit substantial somatic
growth during sexual maturation (Fig. S6). Other physiological studies have also shown higher levels of growth hormone
in maturing salmon than those in immature salmon(Bj€ornsson 1994; Stead et al. 1999), although growth rates
rapidly decrease in the late phase of maturation (Stead et al.
1999). Thus, our data indicate that salmon migration to theBering Sea Shelf is strongly related to sexual maturation. The
Bering Sea Shelf supports high phytoplankton productivity(Lester et al. 2004), which feeds one of the highest benthic

Isotope tracking of migration in salmon 887

faunal biomass densities in the world’s oceans (Highsmith
1990; Grebmeier et al. 1995). Specifically, large euphausiid
populations in the region should be an important foodresource for maturing chum salmon (Moss et al. 2009). For
this reason, this region is a suitable habitat to fulfill the nutritional demands of sexual maturation in salmon, and its
productivity and environmental conditions may be key determinants of the age and size at maturation of the Japanesechum salmon, which have a large influence on individual fitness (Roff 1984; Mangel 1996).
Potential usefulness of isotope tracking for the study of marine
animal migration

Our results demonstrate that an isotope tracking method
using copepod-based d15NBase isoscapes coupled with retrospective isotope analysis of vertebral sections has the potential
to provide new insights into the migration routes of highly
migratory marine fishes. So far, several studies have publishedmarine isoscapes of light elements such as d13C, d15N, andd34S (McMahon et al. 2013; St. John Glew et al., 2019). However, bulk tissue analysis of these elements is generally unsuitable for migration tracking. This is because trophic
discrimination factors between diet and consumer sometimes
differ among species or tissues (Caut et al. 2010), making it
difficult to calibrate isotopic values across these categories.
This uncertainty in trophic discrimination factors is an important source of error in migration route or habitat estimatescalculated from bulk tissue analysis of several species.
d15NBase, on the other hand, sidesteps this issue because it
exclusively reflects site-specific d15N values independently ofthe diet and species of target organisms.
In addition to the use of isoscapes of d15NBase, retrospective isotope analysis also greatly improves the precision of
migration route estimates generated by isotope tracking whentarget animals move into an isotopically distinct area. One
advantage of using the sequential analysis of vertebral sections is that it can provide a larger number of samples
needed for multi-isotope analysis than otolith analysis. One
disadvantage of using vertebral sections, however, is that isotopic records are only available for periods of skeletal
growth. This is why no vertebral isotopic records were available for chum salmon migrating back to Japan from theBering Sea Shelf, since chum salmon do not exhibit substantial skeletal growth after sexual maturation. Similarly, ourisotope tracking did not detect winter migrations into theGulf of Alaska, which are described in previous studies(Urawa et al. 2005, 2009). Several studies have revealed significantly lower growth hormone levels in chum salmon in
the Gulf of Alaska than those in the Bering Sea (e.g. Onuma
et al. 2010), suggesting that chum salmon do not exhibit
substantial somatic growth in the Gulf of Alaska. Consequently, our results suggest that isotope tracking using bone
collagen is a sensitive method for detecting foraging migrations, but may overlook other types such as winter andhoming migrations.
Because the North Pacific is a hotspot for highly migratory
marine animals (Block et al. 2011), our reported isoscape inthis area can be a powerful tool for investigating long-distance© 2020 John Wiley & Sons Ltd/CNRS

888 J. Matsubayashi et al.

ocean migrations of other animals. One advantage of the isotope tracking method is that it can reveal migration routes ofindividual animals over a prolonged timescale (the timescaleof bone growth). Although the spatial resolution of themethod depends on the spatial scale of isotopic gradientsamong habitats, the use of additional isoscapes based onradiocarbon content (Larsen et al. 2018) or neodymium stable
isotope ratios (Saitoh et al. 2018) could help enable finer-scale
determinations of fish migration routes.
ACKNOWLEDGEMENTS

We thank I. Itoh and C. Kamata at the Tohoku National
Fisheries Research Institute for the preparation of copepod
samples; C. Yoshimizu at the Research Institute for Humanity
and Nature for stable isotope analysis of bulk tissues; A. Matsuda and T. Kawakami at the University of Tokyo and K.
Nakata and S. Yoshimasu at the Japan Fisheries Researchand Education Agency for providing salmon samples for analysis; Y. Takano, T. Blattmann and anonymous reviewers fortheir helpful comments on the draft of this paper; the Foundation for the Japan Science and Technology Agency Core
Research for Evolutional Science and Technology (JSTCREST) Grant JPMJCR13A4 and JPMJCR13A3; the Japan
Society for the Promotion of Science (JSPS) KAKENHI
Grant 17J04991 and 19K20495 to J.M.; the Japanese Ministryof Education, Culture, Sports, Science and Technology(MEXT) the Arctic Challenge for Sustainability (ArCS) Project,and MEXT; Tohoku Ecosystem-Associated Marine Sciences
Project Grant JPMXD1111105259 and JPMXD1111105260.
CONFLICT OF INTEREST

The authors declare no conflict of interest.
AUTHORSHIP

J.M., I.T. and Y.O. conceived this study; K.T., A.Y., K.S.,
K.H., H.M. and S.N. provided copepod samples and contextual information; J.M. and T.N. provided chum salmon samples and contextual information; J.M. performed sectioning of
salmon vertebrae and collagen extraction from bone samples;
J.M., N.O.O., N.F.I., N.O. and I.T. performed bulk and compound-specific stable nitrogen isotope analyses; C.Y. provided
data for nitrate utilisation and phytoplankton stable nitrogenisotope ratios in the North Pacific; Y.O. developed a statistical model to estimate salmon migration routes; J.M., Y.O.
and C.Y. wrote the first draft of the paper; all authors wereinvolved in interpreting the results and contributed to the final
draft of the paper; J.M. and Y.O. contributed equally to thiswork.
DATA AVAILABILITY STATEMENT

All stable isotope data for zooplankton and fish bone collagenare provided in the Supplementary Information. Raw isotopic
data of zooplankton samples and fish bone collagen will beavailable from the Dryad Digital Repository: https://doi.org/
10.5061/dryad.r6p27. The custom code used to estimate chum© 2020 John Wiley & Sons Ltd/CNRS

Methods

salmon migration routes is provided in the Supplementary
Information.
REFERENCES
Abrahms, B., Hazen, E.L., Aikens, E.O., Savoca, M.S., Goldbogen, J.A.,
Bograd, S. et al. (2019). Memory and resource tracking drive blue
whale migrations. Proc. Natl Acad. Sci. USA, 116, 5582–5587.
Barth, A., Alvera-Azcarate, A., Troupin, C., Ouberdous, M. & Beckers,
J.M. (2010). A web interface for gridding arbitrarily distributed in situ
data based on Data-Interpolating Variational Analysis (DIVA). Adv.
Geos., 28, 29–37.
Bauer, S. & Hoye, B.J. (2014). Migratory animals couple biodiversity andecosystem functioning worldwide. Science, 344, 1242552.
Berger, J. (2004). The last mile: how to sustain long-distance migration in
mammals. Conserv. Biol., 18, 320–331.
Bj€
ornsson, B.T., Taranger, G.L., Hansen, T., Stefansson, S.O. & Haux,
C. (1994). The interrelation between photoperiod, growth hormone,and sexual maturation of adult Atlantic salmon (Salmo salar). Gen.
Comp. Endocr., 93, 70–81.
Block, B.A., Jonsen, I.D., Jorgensen, S.J., Winship, A.J., Shaffer, S.A.,
Bograd, S.J. et al. (2011). Tracking apex marine predator movements ina dynamic ocean. Nature, 475, 86–90.
Bowen, G.J. (2010). Isoscapes: Spatial pattern in isotopic
biogeochemistry. Annu. Rev. Earth. Planet. Sci., 38, 161–187.
Bowen, G.J., Wassenaar, L.I. & Hobson, K.A. (2005). Global applicationof stable hydrogen and oxygen isotopes to wildlife forensics. Oecologia,
143, 337–348.
Brett, J.R. (1965). The relation of size to rate of oxygen consumption andsustained swimming speed of sockeye salmon (Oncorhynchus nerka). J.
Fish. Res. Board. Can., 22, 1491–1501.
Bronk, D.A., Gilbert, P.M. & Ward, B.B. (1994). Nitrogen uptake,dissolved nitrogen release, and new production. Science, 265, 1843–
1856.
Brower, L.P. (1995). Understanding and misunderstanding the migrationof the monarch butterfly (Nymphalidae) in North America: 1857–1995.
J. Lepid. Soc., 49, 304–385.
Brown, M. (1998). Ocean data view 4.0. Oceanography, 11, 19–21.
Brown, Z.W., Casciotti, K.L., Pickart, R.S., Swift, J.H. & Arrigo, K.R.
(2015). Aspects of the marine nitrogen cycle of the Chukchi Sea shelf
and Canada Basin. Deep Sea Res. Part II Top. Stud. Oceanogr., 118,
73–87.
Carey, F.G. & Scharold, J.V. (1990). Movements of blue sharks (Prionace
glauca) in depth and course. Mar. Biol., 106, 329–342.
Caut, S., Angulo, E., Courchamp, F. & Figuerola, J. (2010). Trophic
experiments to estimate isotope discrimination factors. J. Appl. Ecol.,47, 948–954.
Chikaraishi, Y., Ogawa, N.O., Kashiyama, Y., Takano, Y., Suga, H.,
Tomitani, A. et al. (2009). Determination of aquatic food-web structure
based on compound-specific nitrogen isotopic composition of aminoacids. Limnol. Oceanogr. Meth., 7, 740–750.
Cooke, S.J., Midwood, J.D., Thiem, J.D., Klimley, P., Lucas, M.C.,
Thorstad, E.B. et al. (2013). Tracking animals in freshwater withelectronic tags: past, present and future. Anim. Biotelem., 1, 5.
R Core Team (2019). R: A language and environment for statisticalcomputing. Retrieved from, R Foundation for Statistical Computing(Vienna, Austria). https://www.R-project.org/.
Csardi, G. & Nepusz, T. (2006). The igraph software package forcomplex network research. Inter. Journal, Complex Systems, p1695,
http://igraph.org.
DeNiro, M.J. & Epstein, S. (1981). Influence of diet on the
distribution of nitrogen isotopes in animals. Geochim. Cosmochim.
Ac., 45, 341–351.
Granger, J., Prokopenko, M.G., Sigman, D.M., Mordy, C.W., Morse,
Z.M., Morales, L.V. et al. (2011). Coupled nitrification-denitrificationin sediment of the eastern Bering Sea shelf leads to 15N enrichment of
fixed N in shelf waters. J. Geophys. Res. Oceans., 116, C11006.

Methods

Grebmeier, J.M., Smith, W.O. Jr & Conover, R.J. (1995). Biologicalprocesses on Arctic continental shelves: ice-ocean biotic interactions. In:
Smith, O. S. Jr, Grebmeier, J. M. (eds) Arctic oceanography: marginal
ice zones and continental shelves. American Geophysical Union,
Washington, DC, pp. 231–261.
Hannides, C.C.S., Popp, B.N., Landry, M.R. & Graham, B.S. (2009).
Quantitative determination of zooplankton trophic position using
amino acid-specific stable nitrogen isotope analysis. Limnol. Oceanogr.,54, 50–61.
Hetherington, E.D., Olson, R.J., Drazen, J.C., Lennert-Cody, C.E.,
Ballance, L.T., Kaufmann, R.S. et al. (2017). Spatial food-web structurein the eastern tropical Pacific Ocean based on compound-specificnitrogen isotope analysis of amino acids. Limnol. Oceanogr., 62, 541–560.
Highsmith, R.C. & Coyle, K.O. (1990). High productivity of northern
Bering Sea benthic amphipods. Nature, 344, 862.
Hijmans, R.J. (2019). Geosphere: Spherical trigonometry. R package
version 1.5-10. https://CRAN.R-project.org/package=geosphere.
Hobson, K.A. (1999). Tracing origins and migration of wildlife using
stable isotopes: a review. Oecologia, 120, 314–326.
Hobson, K.A., Barnett-Johnson, R. & Cerling, T. (2010). Using Isoscapesto Track Animal Migration. Isoscapes: Understanding Movement,
Pattern and Processes on Earth Through Isotope Mapping, Springer,
New York, NY, pp. 273–298.
Huntsman, A.G. (1948). Salmon and animal migration. Nature, 161, 300–
302.
Ishida, Y., Ito, S., Ueno, Y. & Sakai, J. (1998). Seasonal growth patternsof Pacific salmon (Oncorhynchus spp.) in offshore waters of the North
Pacific Ocean. N. Pac. Anadr. Fish. Comm. Bull., 1, 66–80.
Kitagawa, G. (1987). Non-gaussian state-space modeling of nonstationary
time series. J. Amer. Stat. Assoc., 82, 1032–1041.
Larsen, T., Yokoyama, Y. & Fernandes, R. (2018). Radiocarbon in
ecology: Insights and perspectives from aquatic and terrestrial studies.
Meth. Ecol. Evol., 9, 181–190.
Lester, N.P., Shuter, B.J. & Abrams, P.A. (2004). Interpreting the vonBertalanffy model of somatic growth in fishes: the cost of reproduction.
Proc. R. Soc. Lond. Ser. B., 271, 1625–1631.
Mangel, M. (1996). Life history invariants, age at maturity and the ferox
trout. Evol. Ecol., 10, 249–263.
Matsubayashi, J., Saitoh, Y., Osada, Y., Uehara, Y., Habu, J., Sasaki, T.
et al. (2017). Incremental analysis of vertebral centra can reconstruct
the stable isotope chronology of teleost fishes. Meth. Ecol. Evol., 8,
1755–1763.
Matsubayashi, J., Umezawa, Y., Matsuyama, M., Kawabe, R., Mei, W.,
Wan, X. et al. (2019). Using segmental isotope analysis of teleost fishvertebrae to estimate trophic discrimination factors of bone collagen.
Limnol. Oceanogr. Meth., 17, 87–96.
McMahon, K.W. & Newsome, S.D. (2019). Amino Acid Isotope Analysis:
A New Frontier in Studies of Animal Migration and Foraging Ecology.
Tracking Animal Migration with Stable Isotopes, London, UK,
Academic Press, pp. 173–190.
McMahon, K.W., Hamady, L.L. & Thorrold, S.R. (2013). A review of
ecogeochemistry approaches to estimating movements of marine
animals. Limnol. Oceanogr., 58, 697–714.
Minagawa, M. & Wada, E. (1984). Stepwise enrichment of 15N alongfood chains: further evidence and the relation between d15N and animal
age. Geochim. Cosmochim. Ac., 48, 1135–1140.
Moss, J.H., Murphy, J.M., Farley, E.V., Eisner, L.B. & Andrews, A.G.
(2009). Juvenile pink and chum salmon distribution, diet, and growthin the northern Bering and Chukchi seas. N. Pac. Anadr. Fish. Comm.
Bull., 5, 191–196.
Nishioka, J. & Obata, H. (2017). Dissolved iron distribution in thewestern and central subarctic Pacific: HNLC water formation andbiogeochemical processes. Limnol. Oceanogr., 62, 2004–2022.
Odate, T. (1994). Plankton abundance and size structure in the northern
North Pacific Ocean in early summer. Fish. Oceanogr., 3, 267–278.
Ogawa, N.O., Chikaraishi, Y. & Ohkouchi, N. (2013). Trophic position
estimates of formalin-fixed samples with nitrogen isotopic compositions

Isotope tracking of migration in salmon 889

of amino acids: an application to gobiid fish (Isaza) in Lake Biwa.
Japan. Ecol. Res., 28, 697–702.
Ohkouchi, N., Chikaraishi, Y., Close, H.G., Fry, B., Larsen, T.,
Madigan, D.J. et al. (2017). Advances in the application of amino acidnitrogen isotopic analysis in ecological and biogeochemical studies.
Org. Geochem., 113, 150–174.
Onuma, T.A., Ban, M., Makino, K., Katsumata, H., Hu, W., Ando, H. et al.
(2010). Changes in gene expression for GH/PRL/SL family hormones in
the pituitaries of homing chum salmon during ocean migration throughupstream migration. Gen. Comp. Endocr., 166, 537–548.
Paffenh€ofer, G.A. (1993). On the ecology of marine cyclopoid copepods(Crustacea, Copepoda). J. Plank. Res., 15, 37–55.
Pomerleau, C., Nelson, R.J., Hunt, B.P., Sastri, A.R. & Williams, W.J.
(2014). Spatial patterns in zooplankton communities and stable isotope
ratios (d13C and d15N) in relation to oceanographic conditions in the
sub-Arctic Pacific and western Arctic regions during the summer of2008. J. Plank. Res., 36, 757–775.
Quinn, T.P. & Myers, K.W. (2004). Anadromy and the marine migrationsof Pacific salmon and trout: Rounsefell revisited. Rev. Fish Biol. Fish.,
14, 421–442.
Robinson, R.A., Crick, H.Q., Learmonth, J.A., Maclean, I.M., Thomas,
C.D., Bairlein, F. et al. (2009). Travelling through a warming world:
climate change and migratory species. Endang. Species Res., 7, 87–99.
Roff, D.A. (1984). The evolution of life history parameters in teleosts.
Can. J. Fish. Aquat. Sci., 41, 989–1000.
Rolff, C. (2000). Seasonal variation in d13C and d15N of size-fractionatedplankton at a coastal station in the northern Baltic proper. Mar. Ecol.
Prog. Ser., 203, 47–65.
Saitoh, Y., Nakano, T., Shin, K.C., Matsubayashi, J., Kato, Y.,
Amakawa, H. et al. (2018). Utility of Nd isotope ratio as a tracer ofmarine animals: regional variation in coastal seas and causal factors.
Ecosphere, 9, e02365.
Schell, D.M., Barnett, B. A. & Vinette, K. A., (1998). Carbon andnitrogen isotope ratios in zooplankton of the Bering, Chukchi and
Beaufort seas. Mar. Ecol. Prog. Ser., 162, 11–23.
Schlitzer, R. (2002). Interactive analysis and visualization of geoscience
data with Ocean Data View. Comput. Geosci., 28, 1211–1218.
Seeb, L.W., Crane, P.A., Kondzela, C.M., Wilmot, R.L., Urawa, S.,
Varnavskaya, N.V. et al. (2004). Migration of Pacific Rim chumsalmon on the high seas: insights from genetic data. Environ. Biol.
Fish., 69, 21–36.
Semmens, J.M., Pecl, G.T., Gillanders, B.M., Waluda, C.M., Shea, E.K.,
Jouffre, D. et al. (2007). Approaches to resolving cephalopod
movement and migration patterns. Rev. Fish Biol. Fish., 17, 401.
St. John Glew, K., Graham, L. J., McGill, R. A. and Trueman, C. N.
(2019). Spatial models of carbon, nitrogen and sulphur stable isotope
distributions (isoscapes) across a shelf sea: An INLA approach. Meth.
Ecol. Evol., 10, 518–531.
Stead, S.M., Houlihan, D.F., McLay, H.A. & Johnstone, R. (1999). Foodconsumption and growth in maturing Atlantic salmon (Salmo salar).
Can. J. Fish. Aquat. Sci., 56, 2019–2028.
Swain, A., Hartley, W.G. & Davies, R.B. (1962). Long-distance migrationof salmon. Nature, 195, 1122.
Tayasu, I., Hirasawa, R., Ogawa, N.O., Ohkouchi, N. & Yamada, K.
(2011). New organic reference materials for carbon and nitrogen-stable
isotope ratio measurements provided by Center for EcologicalResearch, Kyoto University, and Institute of Biogeosciences, Japan
Agency for Marine-Earth Science and Technology. Limnology, 12, 261–
266.
Trueman, C.N. & St. John Glew, K.(2019). Isotopic tracking of marine
animal movement. In Tracking Animal Migration with Stable Isotopes(eds Hobson, K.A., & Wassenaar, L.I.). Academic Press, Cambridge.
Trueman, C.N., MacKenzie, K.M. & Palmer, M.R. (2012). Identifying
migrations in marine fishes through stable-isotope analysis. J. Fish
Biol., 81, 826–847.
Tsuda, A., Saito, H. & Kasai, H. (2001). Geographical variation of bodysize of Neocalanus cristatus, N. plumchrus and N. flemingeri in the© 2020 John Wiley & Sons Ltd/CNRS

890 J. Matsubayashi et al.

subarctic Pacific and its marginal seas: implications for the origin oflarge form of N. flemingeri in the Oyashio area. J. Oceanogr., 57, 341–
352.
Urawa, S., Kawana, M., Anma, G., Kamei, Y., Shoji, T., Fukuwaka,
M.A. et al. (2000). Geographic origin of high-seas chum salmon
determined by genetic and thermal otolith markers. N. Pac. Anadr. Fish
Comm. Bull., 2, 283–290.
Urawa, S., Azumaya, T., Crane, P.A. & Seeb, L.W. (2005). Origin anddistribution of chum salmon in the Bering Sea during the early fall of
2002: estimates by allozyme analysis. N. Pac. Anadr. Fish Comm. Tech.
Rep., 6, 67–70.
Urawa, S., Sato, S., Crane, P.A., Agler, B., Josephson, R. & Azumaya, T.
(2009). Stock-specific ocean distribution and migration of chum salmon
in the Bering Sea and North Pacific Ocean. N. Pac. Anadr. Fish Comm.
Tech. Rep., 5, 131–146.
Urawa, S., Ueno, Y., Ishida, Y., Seeb, L.W., Crane, P.A., Abe, S. et al.
(2001). A migration model of Japanese chum salmon during earlyocean life. N. Pac. Anadr. Fish. Comm. Tech. Rep., 2, 45–46.
Wessel, P. & Smith, W.H.F. (1996). A global, self-consistent, hierarchical,
high-resolution shoreline database. J. Geophys. Res., 101, 8741–8743.
Whittle, J., Kondzela, C.M., Nguyen, H.T., Hauch, K., Cuadra, D. &Guyon, J.R. (2018). Genetic stock composition analysis of chumsalmon from the prohibited species catch of the 2016 Bering Sea© 2020 John Wiley & Sons Ltd/CNRS

Methods

walleye pollock trawl fishery and Gulf of Alaska groundfish fisheries.
United States Department of Commerce, NOAA Technical.
Memorandum, NMFS-AFSC-366.
Yoshikawa, C., Nakatsuka, T. & Wakatsuchi, M. (2006). Distribution of
N* in the Sea of Okhotsk and its use as a biogeochemical tracer of the
Okhotsk Sea Intermediate Water formation process. J. Mar. Syst., 63,
49–62.
Yoshikawa, C., Makabe, A., Matsui, Y., Nunoura, T. & Ohkouchi, N.
(2018). Nitrate isotope distribution in the subarctic and subtropicalNorth Pacific. Geochem. Geophys. Geosyst., 19, 2212–2224.

SUPPORTING INFORMATION

Additional supporting information may be found online in
the Supporting Information section at the end of the article.

Editor, Elizabeth Jeffers
Manuscript received 18 November 2019
First decision made 10 January 2020
Manuscript accepted 25 February 2020