Download Regional differences in climate factors controlling

ICES Journal of Marine Science (2011), 68(6), 1131–1137. doi:10.1093/icesjms/fsr033
Regional differences in climate factors controlling chum
and pink salmon abundance
Masa-aki Fukuwaka 1*, Toshiki Kaga 2‡, and Tomonori Azumaya1
1
Hokkaido National Fisheries Research Institute (HNFRI), Fisheries Research Agency (FRA), 116 Katsurakoi, Kushiro 085-0802, Japan
National Salmon Resources Center, FRA, 2-2 Nakanoshima, Toyohira-ku, Sapporo 062-0922, Japan
2
*Corresponding Author: tel: +81 154 92 1715; fax: +81 154 91 9355; e-mail: [email protected].
Present address: HNFRI, FRA, 116 Katsurakoi, Kushiro 085-0802, Japan.
‡
Fukuwaka, M., Kaga, T., and Azumaya, T. 2011. Regional differences in climate factors controlling chum and pink salmon abundance. – ICES
Journal of Marine Science, 68: 1131 – 1137.
Received 17 June 2010; accepted 20 February 2011; advance access publication 11 April 2011.
Chum and pink salmon abundances vary on a decadal time-scale. We examined the relationship between large-scale climate indices
(CIs), regional climate factors (RFs), and rates of change in regional catches (RCs) of chum and pink salmon in five regions of the North
Pacific. Correlation coefficients of RCs with RFs were larger than those of RCs with CIs, although the correlation coefficient of particular variables varied among regions. Climate affected salmon stocks as indicated by significant relationships with various terrestrial
and ocean climate factors on a regional scale. These results suggest that no single CI or RF controls salmon abundance in all regions;
however, global climate changes could affect regional climate directly and regional salmon abundance indirectly. A warming trend in
the North Pacific might affect the long-term change in salmon abundance. The mechanisms controlling regional salmon abundance
must be understood better to forecast successfully future conditions for Pacific salmon stocks, because the response of salmon stocks
to global climate change varies among regions.
Keywords: chum salmon, climate change, large-scale climate indices, pink salmon, regional process, regional stock dynamics.
Introduction
Pacific salmon (Oncorhynchus spp.) abundance, as indexed by
commercial catch, has changed on a decadal time-scale with
peaks in the 1930–1940s and 1990–2000s (Irvine and
Fukuwaka, 2011). Decadal changes in salmon catch followed
large-scale climate changes that were indicated by ocean climate
indices (CIs; Beamish and Bouillon, 1993; Mantua et al., 1997).
However, salmon catch can vary among regions. For example,
along the west coast of North America, salmon catches in southern
regions varied inversely with catches in northern regions (Hare
et al., 1999). Catch trends for Asian pink salmon (O. gorbuscha)
have been the opposite of those for Alaskan stocks (Nagasawa,
2000). Salmon abundance appears to be affected by large-scale
climate change, although regional variation exists in the ways
that salmon abundances respond.
Spatial patterns of recruitment covariation for North American
pink, chum (O. keta), and sockeye salmon (O. nerka), indicated
that regional processes during freshwater and early marine stages
might be more important for controlling salmon abundance
than ocean basin-scale processes (Peterman et al., 1998; Pyper
et al., 2001, 2002). Salmon survival and/or production have
been correlated with winter air temperature (AT), discharge and
flooding, sea surface temperature (SST) and salinity, and upwelling in coastal environments (Wickett, 1958; Levanidov, 1964;
Blackbourn, 1990; Holtby et al., 1990; Mueter et al., 2002a;
Morita et al., 2006). This is consistent with the concept that
early mortality and growth are important in determining the
# 2011
abundance of individual year classes (Parker, 1962, 1968;
Bradford, 1995; Beamish and Mahnken, 2001).
Chum and pink salmon are the two most abundant Pacific
salmon species, whose catches account for 67– 91% of the total
Pacific salmon catch (Irvine and Fukuwaka, 2011). These species
have a short freshwater life, migrate to the sea soon after their
emergence from gravel, and have similar early ocean lives in estuaries and subsequent ocean migration (Quinn, 2005). Therefore,
their population dynamics might be affected similarly by
climate. The objectives of this study were to clarify how climate
change affects chum and pink salmon differently in various
regions, and to determine whether recent climate change favours
chum and pink salmon. We examined the relationship between
rates of generational change at regional scales of chum and pink
salmon abundances, large-scale CIs, and regional climate factors
(RFs) around the North Pacific.
Material and methods
We analysed the relationship between the rates of change in
regional catches (RCs) of chum and pink salmon, large-scale
CIs, and RFs. Because salmon catch data were available by
nation or region, we set five regions around the North Pacific:
Japan, Russia, the US Northwest and British Columbia
(NWBC), southeastern Alaska (SEAK), and western and central
Alaska (WCAK). The study period included the 1925–2001
brood years of chum and pink salmon for which complete sets
of catch and climate data were available.
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1132
M. Fukuwaka et al.
Salmon catch and climate data
A time-series of Pacific salmon catches since 1925 was compiled by
the Working Group on Stock Assessment, North Pacific
Anadromous Fish Commission (Eggers et al., 2003; Irvine et al.,
2009). We used RCs for chum and pink salmon, except for high
seas catches, because those catches included fish from mixed
stocks. Catch is not the same as abundance, but catch trends can
be used as indices of abundance if changes in fishing effort are
small (Morita et al., 2006; Irvine and Fukuwaka, 2011). For RCs,
we used the rate of change in catch per salmon generation,
which should index population growth rates, assuming that
fishing effort did not change between generations:
RC = ln
Ct+T
,
Ct
where Ct is the salmon catch in numbers at year t, and T is a generation time, i.e. four for chum and two for pink salmon.
For CIs, we used the Aleutian Low Pressure (ALPI), Pacific
Decadal Oscillation (PDO) and Victoria (VI) indices, which
were closely related to the climate and ecosystems of the North
Pacific Ocean (Beamish et al., 1997; Mantua et al., 1997; Bond
et al., 2003). We obtained the annual ALPI from the Pacific
Biological Station, Fisheries and Oceans Canada (http://www.
pac.dfo-mpo.gc.ca/science/species-especes/climatology-ie/
cori-irco/alpi/index-eng.html). For monthly PDO and VI, we
used the first and second principal components of SST in the
North Pacific recalculated by S. McKinnell (pers. comm.).
Recalculated PDO correlated highly with the original PDO (r ¼
0.811, p , 0.001; http://jisao. washington.edu/pdo/). The PDO
and VI indices were averaged over the four seasons, i.e. winter,
January –March; spring, April –June; summer, July –September;
and autumn, October– December.
For RFs, we used precipitation, AT on the land surface and SST.
Monthly precipitation and AT data on a 0.58 latitude by 0.58 longitude grid until 2002 were available from the Climatic Research
Unit, University of East Anglia (Mitchell and Jones, 2005; CRU
TS 2.1 available at http://www.cru.uea.ac.uk/). Monthly SST
data on a 18 latitude by 18 longitude grid were obtained from
the Kobe Collection, Japanese Meteorological Agency. These
data were averaged annually for the four seasons (i.e. winter,
January –March; spring, April –June; summer, July –September;
and autumn, October–December) and over the same regions for
RCs, which were in the ranges 38 –468N 138–1468E for Japan;
46 –588N 135–1658E and 58 –638N 140–1808E for Russia; 45 –
558N 120– 1338W for NWBC; 55– 618N 130–1448W for SEAK;
and 55 –628N 144–1658W for WCAK (Figure 1).
Statistical analyses
To examine relationships among RCs, CIs, and RFs, we used
Pearson’s correlation coefficient with t-tests after adjusting the
degree of freedom as appropriate, considering temporal autocorrelation between variables (Pyper and Peterman, 1998). We analysed
the relationships among RCs for brood year t, CIs and RFs in
seasons during freshwater and early ocean life, i.e. autumn at
year t and winter, spring, and summer at year t + 1.
To interpret relationships among variables, we used structural
equation modelling (SEM) with unobserved latent variables
(recently reviewed by Grace et al., 2010). SEM is the process of
developing and evaluating structural equation models that can
test theoretical models consisting of multiple cause-and-effect
relationships with an observed dataset and evaluate causal effects
through the study of direct and indirect path relations. SEM
includes hypothesized linear relationships based on a theoretical
understanding of the system of interest. Different model configurations reflecting alternative hypotheses are fitted to the data and
evaluated. Path coefficients of the model demonstrate the strength
of the estimated effect of a single variable on the response. Overall,
model fit can be tested using the x 2-test. Computations were performed using the sem package for the statistical package R (version
2.9.2; Fox et al., 2009; R Development Core Team, 2009).
To test whether large-scale ocean climate affects RCs in broad
areas of the North Pacific, we analysed the relationships between
CIs and RCs in all five regions using SEM. In this analysis, we
included latent variables for decadal climate changes and for
broad-area RCs, because of the correlation between CIs and
RCs. The initial SEM model included all paths from latent
decadal climate changes to observed CIs, latent decadal climate
changes to latent broad-area RCs, observed CIs to latent broadarea RCs, and latent decadal climate changes to observed RCs.
To select the best model, we used the backward elimination of
these paths based on the Bayesian information criterion (BIC).
By region and by species, to evaluate the indirect and direct
effects of large-scale climate changes, we conducted an SEM
Figure 1. Map displaying the study regions around the North Pacific. Rectangles indicate the range of a region for collecting data on climate
factors.
1133
Climate controls of pink and chum salmon
with variables for each RC, CIs, latent decadal climate changes, and
RFs. RFs used in this analysis were chosen based on their significance in correlation analysis with the targeted RC. The starting
model of the SEM process included all paths from CIs, latent
decadal climate changes to RFs, and an RC. To select the best
model, we again used the backward elimination of paths based
on BIC. To evaluate the indirect effect of large-scale climate
through regional climate to regional salmon dynamics, we used
a compound path coefficient, which is a product of path coefficients along an indirect linkage from an independent variable to
a dependent variable. A compound path coefficient indicates the
strength of effect of an independent variable on a dependent variable through an indirect compound path, and the sign of the coefficient indicates the direction of the effect (i.e. positive or
negative).
In the analyses, because the correlation analysis included a total
of 21 tests between an RC and separated independent variables
(five variables by four seasons and ALPI), this multiple correlation
test increased the possibility of type I error, where at least one independent variable would be incorrectly detected as being correlated
significantly. An SEM for an RC could include all independent
variables in a test simultaneously and avoid such a multiple-test
problem. However, CIs and RFs might correlate with each other
and potentially be confounding. For help in interpreting the
results of SEMs considering multicollinearity, correlation matrices
of CIs and RFs for SEMs are presented in the Supplementary
material.
Results
Commercial catches of chum and pink salmon in the North Pacific
peaked in the 1930–1940s and in the 1990–2000s (Figure 2).
Recent catches have been at historically high levels. Pink salmon
catches varied from 200 million to ,100 million fish, fluctuating annually with a 2-year cycle. Because the pink salmon has a
2-year generation time, a dominant brood line tends to occur in
either odd- or even-numbered years. Chum salmon catches were
high in the 1930–1940s and 1990–2000s at 50 and 100
million fish, respectively, and low in the 1960–1970s at 20
million fish. No apparent cycles exist in the total catch of chum
salmon.
Significant correlations were detected between RCs and CIs/
RFs, except for chum salmon stocks off Japan (Table 1). The strongest correlation was observed between RCs and RFs, but season
and RFs varied among regions. In six of nine salmon stocks, the
strongest correlation was observed between RC and seasonal precipitation, but the sign of the correlation was inconsistent. In
the other three stocks, the strongest correlation was observed
between RC and seasonal thermal environment (i.e. AT or SST).
This indicates that regional salmon catches were not driven by a
common RF or CI, but were affected by different RFs in different
regions. However, RCs of both species correlated with a common
RF in NWBC (i.e. spring precipitation) and in SEAK (i.e. summer
precipitation), which suggests that similar mechanisms might
drive both chum and pink salmon catches on a regional scale.
The estimated effects of large-scale CIs on a given RC were weak
(Figure 3). RCs were combined into two groups, i.e. southern
stocks, including both species in Japan, NWBC, and SEAK, and
northern stocks, including both species in WCAK and chum
salmon in Russia. Because CIs were correlated with each other,
common temporal patterns were extracted from CIs using two
latent variables, i.e. a combined PDO index and a combined VI.
The ALPI was correlated with both PDO and VI indices. A path
from a combined PDO index to southern stocks remained in the
final model, but was non-significant. No paths from any CI to
northern stocks remained in the final model.
Climate effects were inconsistent among stocks, even in
southern or northern areas of the North Pacific (Table 2). Some
Table 1. Significant Pearson’s correlation coefficient (r) between
RC of chum and pink salmon and large-scale CIs or RFs in five
regions around the North Pacific: Japan, Russia, NWBC, SEAK, and
WCAK during the 1925– 2001 brood years (i.e. 77 years, the
maximum d.f. is 75).
Region
Japan
Russia
Species
Chum
Pink
Chum
Pink
NWBC
Chum
Pink
SEAK
Chum
Pink
WCAK
Chum
Pink
Figure 2. Commercial catch of chum and pink salmon in number of
fish around the North Pacific.
CI/RF
–
Winter AT
Winter
precipitation
Summer VI
Summer AT
Summer SST
Spring
precipitation
Spring
precipitation
Summer PDO
Spring PDO
Summer SST
Summer
precipitation
Spring AT
Spring PDO
Summer
precipitation
Summer
precipitation
Summer SST
Spring SST
Spring AT
Adjusted
d.f.
p-value
0.256
20.359
75
47
,0.05
,0.05
0.339
20.290
20.274
20.262
49
73
65
75
,0.05
,0.05
,0.05
,0.05
20.339
75
,0.01
20.303
20.277
20.258
0.318
59
75
66
64
,0.05
,0.05
,0.05
,0.01
0.315
0.290
0.332
47
47
68
,0.05
,0.05
,0.01
20.231
75
,0.05
0.367
0.286
0.266
75
75
71
,0.01
,0.05
,0.05
r
Statistical significance of the correlation was tested by Pyper and Peterman’s
(1998) t-test adjusting d.f.
–, no CIs or RFs with p , 0.05.
1134
M. Fukuwaka et al.
The signs of indirect and direct path coefficients of CIs to RCs
were also inconsistent: that of ALPI was positive in two stocks, that
of PDO was negative in two stocks, but positive in one stock, and
that of VI was negative in four stocks and positive in one stock.
The inconsistent effects of large-scale climate change indicated
that no single CI or RF controls salmon abundance in all
regions of the North Pacific. This demonstrates the importance
of using RFs when predicting the effects of climate change on
regional patterns of salmon stock survival and productivity.
Discussion
How do climate change effects on chum and pink salmon
vary among regions?
Effects of large-scale climate change
Figure 3. Path diagram indicating relationships among large-scale
CIs and RCs of chum and pink salmon around the North Pacific:
Japan, Russia, NWBC, SEAK, WCAK. A rectangle indicates an
observed variable and an ellipse indicates a latent variable. An arrow
indicates a significant path with a solid line or an insignificant path
with a broken line. Numbers beside arrows illustrate path
coefficients: ***p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05, n.s.p . 0.05. Errors
for variables are omitted in this figure.
Table 2. Compound path coefficients of indirect and direct effects
from large-scale CIs to rates of change in RC of chum and pink
salmon through RFs around the North Pacific.
Region
Russia
Species
Chum
WCAK
Pink
Chum
Pink
RF
Winter
precipitation
2
Summer AT
2
Summer SST
Summer SST
Japan
Pink
NWBC
Chum
Pink
SEAK
Chum
Pink
Winter AT
2
Spring
precipitation
Spring
precipitation
2
Summer
precipitation
Spring AT
Spring AT
Summer
precipitation
CI
ALPI
Compound
path
0.025
Summer VI
–
–
Summer
PDO
Summer VI
0.126
–
–
0.140
20.045
Winter VI
ALPI
Spring PDO
20.145
0.053
20.040
Spring PDO
20.044
Summer
PDO
Spring VI
20.174
20.045
Spring PDO
Autumn VI
Spring VI
0.078
20.042
20.063
Dash in the RF column indicates a direct effect from a CI. Northern stocks
(see the Results section) are displayed above the horizontal line and
southern stocks are displayed below the line.
CIs and RFs that correlated with RCs in Table 1 were deleted in the
SEM processes, because of correlations with other independent
variables (Table 2; see also Supplementary material). In seven of
the nine salmon stocks, large-scale climate change indexed by CIs
indirectly affected RCs through RFs. Direct effects of large-scale
climate change were also detected in three regional salmon stocks.
Environmental changes in early life could affect the population
dynamics of chum and pink salmon stocks strongly. Chum and
pink salmon have anadromous life histories, where fish live in a
freshwater habitat during the embryonic, larval, and juvenile
stages and in a marine habitat during juvenile to immature
stages (Quinn, 2005). Mortality in the freshwater life stage is
higher than during the marine life stage for Pacific salmon
(Bradford, 1995). In the marine life stage, juvenile mortality in
the early estuarine and coastal periods is much higher than in
the later oceanic period for chum and pink salmon (Parker,
1968; Fukuwaka et al., 2010). The range in variation of freshwater
and coastal environments could differ greatly among regions of the
North Pacific. Therefore, relationships between the population
dynamics of salmon and freshwater and coastal environments
could also differ among regions. Responses of such regional
environmental factors and salmon population dynamics to
global-scale climate change are expected to differ among regions.
The results of this study, however, indicate that CIs are related
directly and indirectly to regional chum and pink salmon stocks.
In terrestrial systems, CIs often predict population processes
better than local weather patterns. Most measures of local
weather used by researchers fail to capture complex associations
between bad weather and population processes, such as high rainfall, strong wind, or low temperature over a short period, and
pulsed and lagged mortality of Soay sheep (Ovis aries; Hallett
et al., 2004). The frequency of extreme weather events could be
related to CIs. In marine systems, because local environmental
events might continue for longer durations and affect broader
areas, the effects of local environmental change on a fish population might be detectable. The recruitment of two commercial
pelagic fish, the Japanese sardine (Sardinops melanostictus) and
the chub mackerel (Scomber japonicus), was correlated with SST
at spawning grounds in the Kuroshio Current region and also
with CIs (Yatsu et al., 2005). CIs could affect terrestrial and
ocean RFs, which could control the population processes of
chum and pink salmon, because of their anadromous nature.
Effects of regional-scale climate
The differences in catch fluctuations between southern and northern stocks could suggest the importance of the thermal environment to salmon population dynamics. In the Gulf of Alaska,
temporal changes in salmon catch in southern areas were opposite
to those in northern areas (Hare et al., 1999). This might be
explained by opposite responses of salmon recruitment to
coastal SST (Mueter et al., 2002b). On a smaller geographical
scale along the Japanese coast, the return rate of hatchery-released
1135
Climate controls of pink and chum salmon
chum salmon was correlated negatively with SST along the Japan
Sea coast of Honshu near the southern limit of chum salmon distribution, but correlated positively with SST along the Okhotsk Sea
coast and Nemuro Strait off Hokkaido in northern Japan
(Fukuwaka and Suzuki, 2000; Saito and Nagasawa, 2009). The
thermal environment might determine local environmental
factors controlling regional salmon stocks, such as SST or AT in
northern stocks, whereas precipitation and other factors might
control southern stocks.
Changes in freshwater, estuarine, or coastal environments
caused by precipitation were other important factors affecting
chum and pink salmon populations. However, the results of this
study demonstrated that the effect of precipitation was inconsistent and could act either positively or negatively on chum and
pink salmon stocks. During their freshwater life, river discharge
within a normal range correlated positively, but extremely high
discharge or flooding correlated negatively with Pacific salmon
production (Vernon, 1958; Wickett, 1958; Thorne and Ames,
1987; Beamish et al., 1994). Precipitation also affects coastal
marine environments, because river discharge supplies freshwater
and nutrients from terrestrial systems to the surface waters of
coastal seas. During their early marine life, chum salmon juveniles
stay within and may utilize riverine plumes as nursery areas
(Fukuwaka and Suzuki, 1998). However, the effects of surface
sea salinity have been found to be inconsistent, i.e. negative for
the total catch of Fraser River pink salmon, but positive for the
survival of several chum and pink salmon in British Columbia
and Washington State (Vernon, 1958; Blackbourn, 1990). The
mechanisms with which precipitation affects salmon populations
might differ among stocks and further investigation is needed to
clarify the effects of regional aquatic environments on salmon
stocks.
The relationship between environmental factors and salmon
population dynamics could also differ among regions. CIs or
RFs controlling chum and pink salmon catches were different
among regions. Some RFs correlated with RCs were consistent
with climate factors reported in previous studies on regional
salmon abundances, i.e. winter AT for Japanese pink, spring to
summer precipitation for NWBC chum and pink, and summer
coastal SST for WCAK chum and pink (Blackbourn, 1990;
Mueter et al., 2002a; Morita et al., 2006). Although the environmental conditions indispensable for salmon populations might
be similar among regional stocks, limiting environmental factors
probably differ among regional stocks, because the ranges of variable environmental factors differ among regions. Local adaptations could also cause the differences in environmental
conditions required by salmon stocks. Such local adaptations in
salmonids can develop rapidly, even on a contemporary time-scale
(reviewed by Kinnison and Hendry, 2004).
Factors masking the relationship between climate changes and
salmon dynamics
Human activities, such as artificial propagation, urbanization, or
water utilization, might affect or mask the relationship between
environmental factors and salmon population dynamics. Almost
all Japanese chum salmon stocks have been maintained by hatchery release (Hiroi, 1998). Here, we used the rate of change in catch
as an index of salmon population growth. Because the numbers of
fish released from hatcheries are controlled artificially, the adult –
offspring relationship could be violated even if the survival rate
after release is affected by climate. Hatchery fish are incubated
and reared artificially; they then migrate to the sea soon after
their release from hatcheries (Mayama et al., 1982). Hatchery
fish might be affected less by freshwater environments than wild
fish and improvements in hatchery techniques might not be
related to environmental fluctuation. Therefore, we might have
failed to detect effects of climate factors on Japanese chum
salmon because of improvements in hatchery techniques
(Mayama, 1985).
Another masking factor could be an artefact of this analysis. We
assumed a fixed generation time for chum salmon (i.e. 4 years).
However, the age at maturity for chum salmon ranges from 3 to
6 years, with 4-year-old fish predominant (60 –90%; Salo, 1991).
Where the proportion of 4-year-old fish was lower, the relationship between CIs or RFs and RCs might be less detectable.
Environmental factors controlling salmon population dynamics
can differ among regional populations, because of differences in
the ranges of environmental fluctuations, environmental requirements by salmon populations, and human influences on salmon
populations and/or their environment. However, in this study,
we might have failed to detect a relationship between environmental factors and salmon population dynamics for regional
populations where hatchery activity is extensive or the age at
maturity differs greatly from our assumption.
Does recent climate change favour chum and pink
salmon?
Interdecadal climate changes as quantified by the CIs examined in
this study might not be related directly to the recent large catches
of chum and pink salmon. Previous reports have pointed out that
ALPI and PDO correlated with local zooplankton production,
coastal and terrestrial climate, and also with salmon abundance
(Beamish and Bouillon, 1993; Mantua et al., 1997). Since the publication of these reports, although chum and pink salmon catches
have remained at high levels (Figure 2), ALPI changed greatly in
the early 1990s and PDO changed greatly in the early 2000s. In
this study, no single CI was found to affect changes in salmon
abundance in a similar way across regions. Although we examined
a limited number of CIs, we failed to find any interdecadal climate
changes favouring chum and pink salmon population dynamics in
the North Pacific.
Alternatively, warming trends might have contributed to the
recent increases in salmon catch in some regions. The major
common trend in the Pacific climate has been a persistent SST
warming tendency since 1900 (Schwing et al., 2010). In intracentennial trends, SST increased rapidly in the late 1980s to 1990s in
the Oyashio and Kuroshio current systems in the western North
Pacific and in the 1970s in the Gulf of Alaska and California
Current system in the eastern North Pacific. Radchenko et al.
(2007) demonstrated that the increase in Russian pink salmon
catch was coincident with an increase in the ocean heat content
for the 0 –700-m layer. The current results demonstrated that
temperature and SST correlated positively with RCs in some
regions of the North Pacific. However, in future, rapid warming
might affect salmon catches negatively. In southern and warm
areas of salmon distribution, chum and pink salmon survival or
productivity correlated negatively with SST (Fukuwaka and
Suzuki, 2000; Mueter et al., 2002b). The relationships between
salmon growth and distribution and water temperature have
been shown to be bell-shaped (Morita et al., 2010a, b). The
somatic growth and number of chum and pink salmon decreased
at temperatures higher than the optimum. In this study, we
1136
analysed linear relationships between RCs and environmental
factors using correlation analyses and SEM. If the true response
function of an RC to AT or SST is bell-shaped, the positive
effects of warming in the past might become negative effects in
future.
Conclusions
Responses of chum and pink salmon stocks to global climate
change vary among regions. Interdecadal climate changes might
not be related to recent large increases in catches of chum and
pink salmon. Because the thermal environment plays an important
role in salmon population dynamics, a warming trend in the North
Pacific might affect long-term changes in salmon abundance. In
addition, global warming could affect not only the thermal
environment, but also other freshwater and coastal environmental
conditions for Pacific salmon (Bryant, 2009). The mechanisms
controlling regional salmon abundance should be better understood to forecast successfully future conditions for Pacific
salmon stocks, because responses of salmon stocks to global
climate change vary among regions.
Supplementary material
The following supplementary material is available at the ICES JMS
online version of the paper. Table S1 is a correlation matrix for
large-scale climate indices used in the SEM for Figure 3;
Table S2 is the correlation coefficient between large-scale climate
indices (CIs) and regional climate factors (RFs; above the horizontal line) and correlation matrix for RFs (below the horizontal line)
used in the SEM for Russian chum and pink salmon in Table 2;
Table S3 is the correlation coefficient between large-scale climate
indices (CIs) and regional climate factors (RFs; above the horizontal line) and correlation matrix for RFs (below the horizontal line)
used in the SEM for chum and pink salmon in the western and
central Alaska in Table 2; Table S4 is the correlation coefficient
between large-scale climate indices (CIs) and winter air temperature (winter AT) used in the SEM for Japanese pink salmon in
Table 2; Table S5 is the correlation coefficient between large-scale
climate indices (CIs) and regional climate factors (RFs; above the
horizontal line) and correlation matrix for RFs (below the horizontal line) used in the SEM for chum and pink salmon in the
US Northwest and British Columbia in Table 2; Table S6 is the
correlation coefficient between large-scale climate indices (CIs)
and regional climate factors (RFs; above the horizontal line) and
correlation matrix for RFs (below the horizontal line) used in
the SEM for chum and pink salmon in the Southeast Alaska in
Table 2.
Acknowledgements
We thank members of the NPAFC Working Group on Stock
Assessment for compiling salmon catch data. We also thank
Dr Skip McKinnell for providing the PDO and VI indices, and
Drs McKinnell and James Irvine for their valuable comments on
an earlier version of the manuscript. This study was supported
financially by the Promotion Programme of International
Fisheries Resources Survey from the Fisheries Agency of Japan.
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