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ICES Journal of
Marine Science
ICES Journal of Marine Science (2012), 69(9), 1549–1562. doi:10.1093/icesjms/fss153
Relationships between North Atlantic salmon, plankton,
and hydroclimatic change in the Northeast Atlantic
Grégory Beaugrand1 and Philip C. Reid 2,3,4*
1
CNRS, Université des Sciences et Technologies de Lille, 62930 Wimereux, France
Sir Alister Hardy Foundation for Ocean Science, Plymouth PL1 2PB, UK
3
Marine Institute, Plymouth University, Plymouth PL4 8AA, UK
4
Marine Biological Association of the UK, Plymouth PL1 2PB, UK
2
*Corresponding Author: tel: +44 1752 633269; fax: +44 1752 600015; e-mail: [email protected]
Beaugrand, G. and Reid, P. C. 2012. Relationships between North Atlantic salmon, plankton, and hydroclimatic change in the Northeast
Atlantic – ICES Journal of Marine Science, 69: 1549 – 1562.
Received 11 January 2012; accepted 11 August 2012.
The abundance of wild salmon (Salmo salar) in the North Atlantic has declined markedly since the late 1980s as a result of increased
marine mortality that coincided with a marked rise in sea temperature in oceanic foraging areas. There is substantial evidence to show
that temperature governs the growth, survival, and maturation of salmon during their marine migrations through either direct or
indirect effects. In an earlier study (2003), long-term changes in three trophic levels (salmon, zooplankton, and phytoplankton)
were shown to be correlated significantly with sea surface temperature (SST) and northern hemisphere temperature (NHT).
A sequence of trophic changes ending with a stepwise decline in the total nominal catch of North Atlantic salmon (regime shift
in 1986/1987) was superimposed on a trend to a warmer dynamic regime. Here, the earlier study is updated with catch and abundance data to 2010, confirming earlier results and detecting a new abrupt shift in 1996/1997. Although correlations between
changes in salmon, plankton, and temperature are reinforced, the significance of the correlations is reduced because the temporal
autocorrelation of time-series substantially increased due to a monotonic trend in the time-series, probably related to global
warming. This effect may complicate future detection of effects of climate change on natural systems.
Keywords: Climate change, North Atlantic, northern hemisphere temperature, plankton, regime shift, salmon, sea surface temperature.
Introduction
In recent decades, the abundance of the Atlantic salmon (Salmo
salar) has declined for each of the three stock complexes assessed
by ICES (North America, northern Europe, and southern Europe),
particularly multi-sea-winter (MSW) salmon in the southern parts
of the species’ range (ICES, 2011). Marine survival indices in the
North Atlantic have declined and remain low despite major reductions in fishing effort, particularly in marine fisheries (ICES, 2011;
Russell et al., 2012). Several factors have been implicated both in
freshwater (e.g. barriers to fish migration and poor water
quality) and in the ocean (e.g. predation, poor feeding conditions,
and poor growth), and these have contributed to the continuing
low abundance of wild Atlantic salmon (Friedland et al., 2009,
2012; Otero et al., 2011).
Over the period of declining salmon abundance, the ocean
climate of the North Atlantic has undergone marked changes
# 2012
that include rising sea temperature (Hughes et al., 2010),
changes in circulation (Hátún et al., 2009), biogeographic
changes in plankton (Beaugrand et al., 2009) and fish (Brander,
2010), and changes in the composition and abundance of plankton (Beaugrand, 2009). There is a growing body of literature demonstrating a close relationship between the growth, maturation,
survival, and distribution of salmon at sea and ocean climate as
reflected in sea temperature (Reddin, 1988; Friedland et al.,
2005; Todd et al., 2008). However, these relationships are still
poorly understood, and different factors may govern the successful
return of smolts to rivers in Europe and North America (Friedland
et al., 2005). In the Northeast Atlantic, survival of European stocks
has been linked to the growth of post-smolts and to feeding
conditions in their summer nursery grounds (growth hypothesis),
in contrast to the Northwest Atlantic, where, for North
American stocks, recruitment is independent of post-smolt
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1550
growth (growth-independent hypothesis) and is considered to be
linked to predation (Hogan and Friedland, 2010; Friedland et al.,
2012). The commonality and widespread nature of the decline in
salmon abundance suggest that a changing climate is responsible.
Against this background, average global surface air and sea temperatures have increased by 0.78C since the 1880s, and especially
rapidly since the mid 1980s (Hansen et al., 2010), with the average
temperature in 2010 (Figure 1) closely matching the record high
temperature in 2005. When averaged for the northern hemisphere,
which has a much larger land area, the temperature has shown an
even greater increase.
Beaugrand and Reid (2003) attributed a major part of the
decline in salmon abundance to changes in the carrying capacity
of North Atlantic ecosystems caused by rising sea surface temperature (SST), linked to climate change. The increased SST caused
alterations in the composition, phenology, biomass, and distribution of the planktonic food of salmon and its prey. A sharp
decline in salmon catches followed a large-scale shift in hydroclimatic variables, including SST, in the mid 1980s and coincided approximately with a stepwise shift in northern hemisphere
temperature after 1987. On the basis of the scale and timing of
the suite of changes, it was concluded that climate change is governing the dynamic equilibrium of pelagic ecosystems in the Northeast
Atlantic. Although the results of the study by Beaugrand and Reid
(2003) provided evidence for a link between the decline in salmon
catches and changes in temperature, the time-series available were
not long enough to draw any definitive conclusions.
Here, we provide an update to 2010 and expansion of the information and statistical analyses presented in Beaugrand and Reid
(2003). The purpose here is to try to identify common trends or
patterns in environmental indices, plankton abundance, and
salmon stocks in the Northeast Atlantic, and thereby to infer
links between them. Summed national catches have been used as
a proxy for stock levels, supported by estimates of the abundance
of maturing and non-maturing one-sea-winter (1SW) salmon for
southern and northern European stocks.
In the analyses, we first assessed the relationships between longterm changes in the abundance of salmon in the Northeast
Atlantic, SST, and hydroclimatic indices [northern hemisphere
temperature (NHT), North Atlantic Oscillation (NAO), and
Atlantic Multidecadal Oscillation (AMO)]. Second, we examined
linkages between salmon, the hydroclimatic indices, and a
number of plankton taxonomic groups [phytoplankton colour
G. Beaugrand and P. C. Reid
(PHC), total copepods, Calanus finmarchicus, and euphausiids.
Third, we performed a time-constrained cluster analysis on all
first and second principal components (PCs) of salmon, the plankton groups, SST, and the NAO and AMO indices.
Material and methods
Large-scale hydroclimatic indices
Global land–ocean temperature anomalies, based on the reference
period 1951– 1980, were obtained from the Goddard Institute for
Space Studies (Hansen et al., 2010). Satellites were used to identify
meteorological stations that were situated in areas of extremely low
light pollution at night and to adjust temperature trends of urban
and peri-urban stations for non-climatic factors. This new procedure checks to ensure that urban effects on analysed global change
are negligible (Hansen et al., 2010).
The winter NAO index used in this study is based on a principal
component analysis (PCA) of sea level pressure over the North
Atlantic sector for the months December–March (Hurrell et al.,
2001). The NAO is a basin-scale alternation of atmospheric mass
over the North Atlantic between high pressure centred on the subtropical Atlantic and low pressure around Iceland. This phenomenon, detected in all months of the year, is particularly strong in
winter and explains 37% of the variability in monthly sea level
pressure from December to February (Marshall et al., 2001).
The AMO is an index of multidecadal ocean/atmosphere
natural variability, which has an amplitude range of 0.48C in
many areas of the North Atlantic (Enfield et al., 2001). The
index we used is constructed from extended reconstruction SST
(ERSST) data, averaged for the area 25 –608N 7 –758W, minus a
regression on global mean temperature (National Climate Data
Center). Time-series of the unsmoothed AMO index were downloaded from the website http://www.esrl.noaa.gov/psd/data/
timeseries/AMO/. This oceanic oscillation may have a great influence on SST changes in the Atlantic as well as globally (Enfield
et al., 2001; Keenlyside et al., 2008).
NHT anomalies from 1960 to 2010 were provided by the Hadley
Centre for Climate Prediction and Research, Meteorological Office,
Exeter, UK.
Sea surface temperature
SST data were obtained from the ERSST version 3 (ERSST_V3)
dataset. The data were provided by the NOAA/OAR/ESRL PSD,
Boulder, CO, USA (http://www.esrl.noaa.gov/psd/). The
ERSST_V3 dataset is derived from a reanalysis on a 28 × 28
spatial grid based on the most recently available International
Comprehensive Ocean –Atmosphere Data Set (ICOADS) SST
data, and it uses improved statistical methods to produce a stable
monthly reconstruction based on sparse data (Smith et al., 2008).
Salmon data
Figure 1. Long-term changes in global surface temperature
anomalies (combined sea and land) from 1881 to 2010.
Nominal salmon catch data for the North Atlantic Salmon
Conservation Organization (NASCO) Northeast Atlantic
Commission (NEAC) area (both southern and northern
European areas) for the period 1960–2010 were obtained from
table 2.1.1.1 in ICES (2011) and used as a proxy for stock levels
in the Northeast Atlantic. Three countries (Denmark, Finland,
and France) that did not report data to ICES for the early period
of the time-series were excluded from the analysis. Salmon catch
data can be strongly affected by changes in fishing methods, such
as development of monofilament driftnets, and regulation/
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Salmon, plankton, and hydroclimatic change in the NE Atlantic
management. Therefore, estimates (median values) developed by
ICES (2011) of the numbers of maturing and non-maturing 1SW
salmon, i.e. fish that would return to home waters as 1SW and
MSW salmon, respectively, were used to confirm the findings
based on catches over the shorter periods for which the abundance
data were available. These abundance estimates are available by
country or jurisdiction for both northern and southern European
stock complexes from 1971 to 2010 (see tables 3.8.12.1 and
3.8.12.2 in ICES, 2011). Data for all countries in the tables were
included in the analysis, although estimates were not available for
Norway before 1983.
Plankton data
The plankton data used in this study (1960–2009; 50 years) were
obtained from the Continuous Plankton Recorder (CPR)
survey. This monthly survey uses voluntary merchant ships to
tow CPR sampling machines along standard transects at a depth
of 7 m (Richardson et al., 2006). PHC was used as a proxy for
primary production and phytoplankton composition (see Batten
et al., 2003). As in the earlier study (Beaugrand and Reid, 2003),
long-term spatial changes in the distribution of some key zooplankton groups for salmon were also investigated: these included the
total abundance of copepods (less than 2 mm) and the abundance of the copepod C. finmarchicus as indicators of secondary
production, and the abundance of euphausiids because they may
represent a significant part of the diet of post-smolt salmon
(Jacobsen and Hansen, 2000).
Spatial interpolation of plankton and SST data
Because the CPR sampling is irregular in space, biological data from
the CPR were interpolated on a regular grid of 18 longitude × 18
latitude in a spatial domain ranging from 40 to 708N and from
308W to 208E. Salmon extend over a much greater area of the
Northeast Atlantic than delimited above and also feed on nekton
and fish not sampled by the CPR. It has been demonstrated,
however, that the patterns of change over time in CPR data are representative of large regions (Hátún et al., 2009) and are likely to
extend outside the area defined above as indicators of environmental change. Spatial interpolations of the plankton data were performed for each year in the period 1960–2009, each 2-month
period, and for daylight and dark periods using the function
given in Beaugrand et al. (2000). In each case, interpolation by
the inverse square distance method was realized on a grid of 18
longitude × 18 latitude (the same grid as for SST) geographic
cells (pixels) using a search radius of 250 km. Estimates were not
calculated for pixels with , 5 samples. The number of neighbours
was fixed at between 5 and 15. SST data were interpolated using the
same procedure, with no distinction for day and night observations.
Annual means for both SST and plankton were then calculated for
each pixel. Maps were rearranged in a new matrix, with the abundance of a taxon (or SST) for each year in columns and the pixels
in rows. The resulting annual mean matrix included missing
values for some pixels. The following five matrices were used: (i)
SST; (ii) PHC; (iii) total copepods; (iv) C. finmarchicus; and (v)
euphausiids.
Standardized PCA
To examine long-term changes (1960–2010) in the abundance of
European salmon stocks, standardized PCAs were carried out on
catches (matrix 51 years × 9 Northeast Atlantic countries or
jurisdictions) and on estimates (median values) of maturing and
non-maturing 1SW fish, but for a shorter period ranging from
28 to 40 years.
A fourth standardized PCA, based on an algorithm that takes
into account missing data (Bouvier, 1977), was performed on
each plankton data matrix (four matrices), with the objective of
identifying major long-term changes (1960 –2009), i.e. examination of PCs. A similar procedure was applied to analyse SST.
The first two eigenvectors and PCs of the biological variables
were related to the winter NAO index, NHT anomalies, and to
the first two PCs of the analysis of SST. Relationships between
PCs and large-scale hydroclimatic features and long-term
changes in the biological environment were also investigated by
correlation analysis. Probabilities were calculated with consideration of temporal autocorrelation. The Box and Jenkins (1976)
autocorrelation function modified by Chatfield (1996) was calculated. The autocorrelation function was then applied to adjust the
degrees of freedom using Chelton’s formula (Chelton, 1984) as
applied by Pyper and Peterman (1998). Such a correction can,
however, be too conservative and increases the type II error (i.e.
acceptance of the null hypothesis of an absence of linear correlation between two variables when the two variables are actually
correlated).
When autocorrelation in a time-series is strong, the reduction
in the degrees of freedom is so extreme that it can lead to nonsignificant correlations at the threshold of 0.05. Rodionov and
Overland (2005) and Luczak et al. (2011) used a threshold of 0.1
when temporal autocorrelation was accounted for. Knowing that
the correction to account for temporal autocorrelation is conservative, we also considered some correlations with probabilities
,15% after accounting for temporal autocorrelation, providing
that the coefficient of correlation explained .50% of the total
variance. The rationale for this approach is considered further in
the Discussion.
Relationships between the first PCs of catch
and returning salmon (1960– 2010)
Results of the first PC of salmon catch and the first PCs of maturing and non-maturing 1SW salmon were examined graphically
and by correlation analysis. The time-series were detrended by singular spectrum analysis (SSA; Vautard et al., 1992). SSA was
applied on the three first PCs, and the corresponding non-linear
trend was subtracted from each original time-series. This
method is described in Beaugrand and Reid (2003) and presented
mathematically in Ibañez and Etienne (1991).
Time-constrained hierarchical cluster analysis
(1960– 2009)
A time-constrained hierarchical cluster analysis was performed on
the first two PCs that originated from the PCA performed on
plankton variables, the first and third PCs that originated from
the PCA performed on salmon catch data, and the first two PCs
that originated from the PCA performed on annual SST data.
Anomalies of NHT and both the AMO and NAO indices were
also added. In total, 15 variables (1960–2009) were considered
in this analysis conducted in mode Q to cluster years. A 1-year
simple moving average was applied to annual mean data to
induce a slight temporal contiguity and focused on temporal discontinuities. As data were heterogeneous, a chord distance was
used (Legendre and Legendre, 1998).
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G. Beaugrand and P. C. Reid
Effects of the abrupt shifts at the species level
(1960– 2009)
The effect of the discontinuities detected by the time-constrained
cluster analysis was investigated individually for NHT, AMO, PC1
SST, PC1 PHC, PC1 total copepods, PC1 C. finmarchicus, PC1
euphausiids, and PC1 salmon catch, but not the estimates of maturing and non-maturing 1SW salmon, because there were too many
missing data. As the focus was on discontinuities and not cyclic
variability, the entities examined in relation to the second components of the plankton and salmon catch were not considered in this
analysis. First, a standardized PCA was used to establish relationships between the variables and the main changes observed when
all biological entities were combined. The standardized PCA was
performed on a table of 50 years (1960 –2009) × 5 biological
variables, adding the physical variables (NHT, AMO, and PC1
SST) as supplementary variables. Therefore, the PCs reflected
only changes in the five biological variables. Second, a multivariate
multiscale –split moving window boundary (MMS–SMW) analysis (details available from G.B.) was applied to detect the effects
of the temporal discontinuities on each variable (eight variables).
This technique is a variation of the split moving window (SMW)
analysis created by Webster (1973) that overcomes the problem
of the dependence of SMW results on the size of the time-window
(Cornelius and Reynolds, 1991) by applying a range of timewindows, in this case varying from 3 to 10 years. The principle of
the technique is to perform a Kruskal–Wallis test between each
time-window along a time-series. The procedure is repeated for
each variable, then a colour diagram is used to show the number
of variables that present a significant discontinuity at the threshold
of p ¼ 0.05 for each time-window and year. The combined analyses
identify the relationships between the variables investigated (normalized eigenvectors), show the main pattern of long-term
change (first PCs), and provide both an identification and quantification of the effect of the discontinuities on each variable as a
colour diagram.
Temporal changes in spatial patterns of correlations: SST
vs. NAO and NHT
Long-term changes in spatial patterns of correlation (Pearson
linear coefficient) between annual SST and the winter NAO
index and NHT anomalies were investigated. The number of
years selected (17 years; 15 d.f.) was kept constant to compare
the intensity of the correlations. Three periods of equal length
were therefore used: 1960–1976, 1977–1993, and 1994–2010.
Results
Long-term changes in salmon abundance (1960 –2010)
A standardized PCA was applied to time-series from 1960 to 2010 of
salmon catches from nine countries or jurisdictions (Figure 2).
Catches in countries such as the UK, Ireland, and Spain were
related positively to the first PC (Figure 2a). The long-term
changes in the first PC (62.79% of the total variance) revealed a
substantial reduction in salmon catches, with two phases of faster
decline (Figure 2b), the first after 1986 and a second less
pronounced reduction in catches after 1994–1997, with a continuing
decline after 2001 to 2010. Correlations between long-term changes
in the first PC and NHT anomalies were negative (r ¼ –0.84; pACF ¼
0.15; d.f.c. ¼ 2), explaining 71% of the total variance. This was
also the case when correlation between the first PC and the AMO
index was calculated (r ¼ –0.60; pACF ¼ 0.15; d.f.c. ¼ 6), although
Figure 2. Long-term changes in salmon catches from 1960 to 2010
revealed by standardized principal component analysis (PCA) based
on data from Norway, Russia, Iceland, Sweden (west), Ireland, the UK
(England and Wales), the UK (Northern Ireland), the UK (Scotland),
and Spain. (a) First two eigenvectors, with circles of correlation and
equilibrium contribution superimposed. (b) Long-term changes in
the first principal component (black; inverted) and NHT anomalies
(grey).
the correlation explained less variance ( 36%). However, it
should be noted that the probability of these correlations was
higher than the traditional threshold of 0.05 after correction to
account for temporal autocorrelation because of the presence of a
strong monotonic trend in the time-series. The correlation with
the NAO index was not significant (r ¼ –0.21; pACF ¼ 0.43;
d.f.c. ¼ 14). The second PC (11.88% of the total variance) exhibited
a pronounced increase from the end of the 1960s to the end of the
1970s, a reduction until the mid 1990s, and an increase thereafter
(not shown). Catches on the west coast of Sweden were positively
related to the second PC (Figure 2a).
Long-term changes in the first PC of salmon catches highly covaried positively with long-term changes in the estimated number of
maturing and non-maturing 1SW salmon (Figure 3a, Table 1).
Correlations between long-term changes in salmon catches and maturing (r ¼ 0.86, p , 0.05) and non-maturing (r ¼ 0.98, p , 0.05)
1SW salmon were significant. As temporal autocorrelation was high
in each time-series, we detrended the first three PCs with SSA
(Figure 3b). The three detrended time-series exhibited the same
pseudo-cyclic variability and correlations between long-term
changes in salmon catches, and returning maturing (r ¼ 0.61, p
,0.05) and non-maturing (r ¼ 0.84, p , 0.05) 1SW salmon
remained highly significant. These results allowed us to use
salmon catches in the time-constrained hierarchical cluster analysis
to cover a longer period (1960 2 2010 vs. 1971 or 1983 2 2010).
Long-term changes in annual SST (1960 –2010)
A standardized PCA was used to investigate spatiotemporal
changes in time-series of annual SST (1960 –2010; 51 years)
from the North Atlantic (Figure 4). The first PC (60.59% of the
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Salmon, plankton, and hydroclimatic change in the NE Atlantic
total variance) reflected an increase in annual SST in areas positively correlated with the first PC (Figure 4a). As all areas were
positively correlated with the PC, this pattern prevailed in all
regions of the Northeast Atlantic, but was stronger west and
north of the British Isles and in the northern part of the North
Sea. Long-term changes in the first PC were positively correlated
with NHT anomalies (r ¼ 0.88; pACF ¼ 0.05; d.f.c. ¼ 3) and the
AMO index (r ¼ 0.85; pACF ¼ 0.03; d.f.c. ¼ 4), but not with the
NAO index (r ¼ 0.13; pACF ¼ 0.62; d.f.c. ¼ 14). The second PC
(12.09% of the total variance) reflected an opposition between
negative regions located south of the Oceanic Polar Front (including the North Sea) and positive regions located south of Iceland
(Figure 4b). Long-term changes in that component were negatively correlated with the NAO index (r ¼ –0.65; pACF ,0.001;
d.f.c. ¼ 34), indicating a positive effect of the NAO on SST
south of the Oceanic Polar Front and a negative effect in the
region south of Iceland. There was no significant correlation
between the second component and NHT anomalies (r ¼ –0.13;
pACF ¼ 0.63) and the AMO index (r ¼ 0.11; pACF ¼ 0.66).
Long-term changes in salmon and hydroclimatic
and plankton fluctuations (1960 –2009)
Figure 3. Long-term changes in the first principal component of
salmon catch (black, 1960 – 2010) in relation to the first principal
components (1971 – 2010) of the estimated number of maturing
(light grey) and non-maturing 1SW (dark grey): (a) original,
(b) detrended.
We examined changes in salmon (catches and estimates of the
abundance of maturing and non-maturing 1SW salmon) and
plankton (first PCs) in relation to hydroclimatic fluctuations
(Figure 5, Table 2). As all first PCs reflected changes appearing
around the UK, including the North Sea, but excluding the area
south of Iceland (see Figure 4b), all first PCs have been plotted
on the same figure, together with NHT anomalies, the AMO,
and the first PCs originating from the PCA calculated on annual
SST. A long-term decrease in the first components of salmon
(catch and returning maturing and non-maturing 1SW salmon)
paralleled a concomitant increase in PHC and a reduction in
total copepods, C. finmarchicus, and euphausiids. These biological
changes were accompanied by an increase in both the first PC of
annual SST, NHT anomalies, and the AMO index. All correlations
were high, explaining between 29% and 72% of the total variance
(Table 2), and all were significant when the degree of freedom was
not adjusted to account for temporal autocorrelation. This indicates that their trend was similar. However, after adjusting for temporal autocorrelation, some values were above the classically
chosen significance level of 0.05. The strong temporal autocorrelation strongly reinforced the reduction of the degrees of freedom
(up to 94%; Table 2). Because the trend in global temperature
and its effect on regional temperatures has become linear, the
responses of biological variables can be expected to show the
same trend. Removing this important information, which may
explain .50% of the total variance, may greatly increase type II
error. However, correlations between some indices of temperature
Table 1. First and second normalized eigenvectors, i.e. linear correlation with the corresponding principal components, originating
from two standardized PCAs performed on maturing 1SW and non-maturing 1SW Atlantic salmon, 1971– 2010.
PCA on estimated numbers of maturing 1SW
salmon
PCA on estimated numbers of non-maturing
1SW salmon
Variable
Finland
Iceland (northeast)
Norway
Russia
Sweden
France
Iceland (southwest)
Ireland
UK, England and Wales
UK, Northern Ireland
UK, Scotland
Values in bold are .0.5.
First eigenvector
0.2155
–0.4828
0.9181
0.1637
0.7375
0.8375
–0.2849
0.8453
0.86
0.5279
0.7002
Second eigenvector
– 0.859
–0.1849
–0.3014
– 0.8589
–0.2933
0.3237
0.2066
0.0335
0.1645
0.184
0.3572
First eigenvector
0.2656
0.7702
0.6529
0.6367
–0.8184
0.7561
0.9054
0.8908
0.8085
0.5584
0.8808
Second eigenvector
–0.8668
0.1307
–0.0595
–0.5956
–0.1505
0.3616
0.1383
–0.1203
0.1592
–0.2274
0.149
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G. Beaugrand and P. C. Reid
Figure 4. Long-term changes in annual SST examined by standardized PCA. (a) First normalized eigenvector plotted on a map (left) and a
graph of its associated principal component (black). Long-term changes in NHT anomalies (red) are also shown. (b) Second normalized
eigenvector plotted on a map (left) and its associated principal component (black). Long-term changes in the NAO index (red; inverted)
are also shown.
were significant (1SW, Table 2) or close to significance (e.g. MSW
and NHT at p ¼ 0.06; Table 2) when the traditional threshold of
p ¼ 0.05 is applied.
Long-term changes in salmon catches (second PC) were then
investigated in relation to planktonic and hydroclimatic changes
(Figure 6). As these data exhibited more pronounced year-to-year
variability, we used an order-1 simple moving average to attenuate
the high frequency variability. Pseudo-cyclic variability was
detected on each variable. All time-series plotted in Figure 6 had
pronounced values between 1984 and 2000. These values
were positive for the NAO index and euphausiids, and negative
for the other variables.
Time-constrained cluster analysis (1960– 2009)
A time-constrained cluster analysis, combining all time-series
extending from 1960 examined in Figures 5 and 6 (but not including estimates of abundance of maturing and non-maturing 1SW
salmon in Figure 5 which have a shorter time-series), was used
to detect the main discontinuities (Figure 7). Two discontinuities
were identified, in 1986/1987 and 1996/1997.
Effects of the abrupt shifts at the species level
(1960– 2009)
Using most of the variables examined in Figure 5, the implications
of the abrupt shifts detected by the time-constrained cluster analysis were investigated in more depth by a standardized PCA and
MMS–SMW. The standardized PCA summarized the long-term
changes of all the biological variables (except estimates of the
abundance of maturing and non-maturing 1SW salmon; see
above) shown in Figure 5 in relation to changes in the physical entities, which were added in the analysis as supplementary variables
(Figure 8a, b). The first PC, which explains 80.06% of the total
variance, exhibits two shifts and was clearly similar to the longterm changes in NHT anomalies. The component shows two accelerating phases of change, the first centred on 1987 and the second
on 1996. The examination of the normalized eigenvectors indicates that all variables are strongly correlated with the first PC.
The use of MMS– SMW (Figure 8c) reveals the two shifts and
shows that it concerned between five (62.5%) and six (75%) of
the eight variables considered in the analysis at large timewindows. At shorter time-windows, the first shift was stronger
than the second in terms of implicated variables. The variables
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Salmon, plankton, and hydroclimatic change in the NE Atlantic
of global warming in regions that were typically strongly
influenced by natural sources of hydroclimatic variability, such
as the NAO.
Discussion
Figure 5. Long-term changes in salmon in relation to hydroclimatic
and planktonic fluctuations: (a) NHT anomalies and (b) AMO index.
(c) First principal component on annual SST (PC1 SST). (d) First
principal component on PHC (PC1 PHC). (e) First principal
component (PC1) on maturing 1SW salmon. (f) First principal
component on total copepods (PC1 total copepods). (g) First principal
component on C. finmarchicus (PC1 C. finmarchicus). (h) First
principal component on euphausiids (PC1 euphausiids). (i) First
principal component on salmon catch (PC1 salmon catch). (j) First
principal component on non-maturing 1SW (i.e. MSW) salmon.
that were most correlated were PC1 on salmon catch, PC1 on
C. finmarchicus, and PC1 on euphausiids, and to a lesser extent
PC1 on PHC and PC1 on total copepods.
Temporal change in spatial patterns of correlation
between SST, NAO, and NHT (1960– 2010)
The spatial patterns of correlations between annual SST and the
winter NAO index remained similar during the three periods of
equal length selected, although the relationships were stronger
and more evident during the period 1977–1993, when the NAO
index was in a sustained positive phase (Figure 9). In contrast,
the spatial pattern of correlations between annual SST and NHT
anomalies was not constant. Correlations were highly reinforced
in the last period, probably reflecting the northward propagation
Beaugrand and Reid (2003) examined long-term changes in phytoplankton, zooplankton, and salmon catches in relation to hydroclimatic changes in the Northeast Atlantic. Highly significant
relationships were identified between long-term changes in all
three trophic levels, SST in the Northeast Atlantic, and NHT. The
winter NAO had a positive influence on SST in the North Sea
and a negative influence in the Subpolar Gyre, but a significant biological correlation with the NAO was only found for the copepod C.
finmarchicus. The similarities detected between plankton, salmon,
and temperature were seen not only in their long-term changes,
but also in some changes that started after an increase in NHT
anomalies at the end of the 1970s. In the 2003 analysis, all biological
variables exhibited a pronounced change that started after 1982
for euphausiids (decline), 1984 for the total abundance of small
copepods (increase), 1986 for PHC (increase) and C. finmarchicus
(decrease), and 1988 for salmon catches (decrease). This sequence
of biological events led to an exceptional period after 1986 that
coincided with a marked decline in salmon catches and a stepwise
shift in large-scale hydroclimatic variables (NHT in 1987, NAO in
1988) and SST. An equivalent stepwise change (regime shift) in the
North Sea ecosystem (Reid et al., 2001) at that time was attributed
by Reid et al. (2003) to enhanced oceanic inflow, especially from the
slope current, generated by regional changes in the climate of
northwest Europe. Subsequently, Beaugrand et al. (2008) also
observed these changes in the North Sea and attributed them to a
northward movement of a critical thermal boundary, indicated
by the annual 9– 108C isotherms, that separates temperate from
Subarctic ecosystems.
By extending the time period to 2010, our present study confirms the earlier results based on the period 1960–2000. The correlation between long-term changes in salmon and NHT
anomalies is reinforced (r ¼ –0.72, 1960–2000 vs. r ¼ –0.86,
1960–2010), and a similar but less significant relationship was
found with the AMO. Our results confirm the findings of
Condron et al. (2005), who showed that salmon abundance in
the Northwest Atlantic fluctuated negatively in parallel with the
AMO. However, although the correlation with NHT increased
between the two studies, the probability of significance decreased
because of the strong temporal autocorrelation of both time-series
that is related to a strong linear trend.
Although the correlations between changes in salmon catches
and temperature were reinforced in terms of the earlier results of
Beaugrand and Reid (2003), many of the correlations were not significant at the traditional level of significance, p ¼ 0.05. The mathematical theory on tests of significance does not outline an
appropriate level of significance (Tullock, 1970). The choice of
what is considered to be the universal level of significance (p ,
0.05) is, therefore, not based on mathematical theory. The most
serious consequence of this arbitrary choice is that, in some circumstances, it may lead to the acceptance of the null hypothesis when it
should be rejected (type II error). In environmental research, this
can have substantial consequences (Dayton, 1998; Gerrodette
et al., 2002). Dayton (1998) took the fictitious example of a proposal
to protect the benthic ecosystems of the Gulf of Maine. If fishing was
not the cause of the collapse of the benthic ecosystems, limiting this
activity would result in a type I error. However, if fishing was the
1556
G. Beaugrand and P. C. Reid
Table 2. Correlations between long-term changes in salmon, plankton, and the hydroclimatic environment, with the degrees of freedom
adjusted to account for temporal autocorrelation.
Variablea
NHT
AMO
PC1 SST
PC1 PHC
PC1 copepods
PC1 C. finmarchicus
PC1 euphausiids
Salmon catch
– 0.84 (p ¼ 0.15, d.f.c. ¼ 2)
– 0.60 (p ¼ 0.15, d.f.c. ¼ 6)
– 0.74 (p ¼ 0.15, d.f.c. ¼ 3)
– 0.75 (p ¼ 0.09, d.f.c. ¼ 4)
0.72 (p ¼ 0.07, d.f.c. ¼ 5)
0.82 (p ¼ 0.09, d.f.c. ¼ 3)
0.81 (p ¼ 0.09, d.f.c. ¼ 3)
1SW
–0.77 (p ¼ 0.04, d.f.c. ¼ 5)
–0.70 (p ¼ 0.03, d.f.c. ¼ 7)
–0.79 (p ¼ 0.05; d.f.c. ¼ 4)
–0.64 (p ¼ 0.12, d.f.c. ¼ 5)
0.73 (p , 0.01, d.f.c. ¼ 10)
0.54 (p ¼ 0.21, d.f.c. ¼ 5)
0.66 (p ¼ 0.10, d.f.c. ¼ 5)
MSW
–0.85 (p ¼ 0.06, d.f.c. ¼ 3)
–0.79 (p ¼ 0.06, d.f.c. ¼ 4)
–0.78 (p ¼ 0.11, d.f.c. ¼ 3)
–0.78 (p ¼ 0.12, d.f.c. ¼ 3)
0.63 (p ¼ 0.12, d.f.c. ¼ 5)
0.74 (p ¼ 0.14, d.f.c. ¼ 3)
0.78 (p ¼ 0.12, d.f.c. ¼ 3)
a
NHT, northern hemisphere temperature anomalies, 1960–2009 (50 years); AMO, Atlantic Multidecadal Oscillation, 1960– 2009 (50 years); PC1 SST, first
principal component of the PCA performed on SSTs, 1960–2009 (50 years); PC1 PHC, the same on phytoplankton colour, 1960– 2006 (47 years); PC1
copepods, the same on copepods, 1960–2009 (50 years); PC1 C. finmarchicus, the same on C. finmarchicus, 1960–2009 (50 years); PC1 euphausiids, the same
on euphausiids, 1960–2009 (50 years).
Salmon catch, 1960–2009 (50 years); 1SW, the number of returning maturing 1SW salmon, 1971–2009 (39 years); MSW, the number of returning maturing
and non-maturing 1SW salmon, 1971–2009 (39 years).
p, probabilities corrected for temporal autocorrelation; d.f.c., degrees of freedom after correction for temporal autocorrelation. All uncorrected probabilities
were highly significant (p , 0.0001), suggesting that the long-term trends between time-series were highly correlated. Note that the corrected degree
of freedom was low despite the time-series being between 36 (d.f. ¼ 34) and 50 years (d.f. ¼ 48) long.
Figure 7. Time-constrained cluster analysis on all time-series shown
in Figures 5 and 6, showing major discontinuities at 1986/87 and
1996/97. In the analysis, time-series of estimated numbers of
returning maturing and non-maturing 1SW salmon were not used
because there were no data for the period 1960 –1971.
Figure 6. Long-term changes in salmon catch in relation to
hydroclimatic and planktonic fluctuations, with an order-1 simple
moving average used to attenuate the high-frequency variability in
all time-series: (a) NAO index, and second principal component on
(b) annual SST (PC2 SST), (c) PHC (PC2 PHC), (d) total copepods
(PC2 total copepods), (e) C. finmarchicus (PC2 C. finmarchicus),
(f) euphausiids (PC2 euphausiids), and (g) salmon catch (PC2
salmon catch).
cause of the alteration, the absence of action (type II error) would
have a dramatic effect on the benthic communities.
Buhl-Mortensen (1996) used the example of the lack of detection
of a pollution event on a natural system, simply because the emphasis is put on minimizing the risk of type I error whereas application of the precautionary principle would place the emphasis
on minimizing the type II error. In this study, the type II error
would fail to attribute a climatic effect when there is one. This
issue is particularly acute where corrections to account for temporal
autocorrelation or multiple testing (Legendre and Legendre, 1998;
Luczak et al., 2011), which are conservative, are applied. This led
some authors to use statistical thresholds .0.05 (Rodionov and
Overland, 2005; Luczak et al., 2011).
In this study, the absence of significance at p ¼ 0.05 for some
correlations was related to the strong autocorrelation of
time-series, both physical and biological (see the reduction
Salmon, plankton, and hydroclimatic change in the NE Atlantic
Figure 8. The effects of abrupt shifts at species level investigated by
standardized PCA and MMS– SMW: (a) first and second normalized
eigenvectors representing the correlations between the first two
principal components of a PCA performed on the table 50 years × 5
biological variables, adding the physical entities NHT, AMO, and PC1
SST as supplementary variables; (b) long-term changes 1960 –2010 in
the first principal component in relation to NHT anomalies;
(c) colour diagram of the number of variables that showed a
significant discontinuity as a function of time-windows and years,
1960 –2009. Eight variables (five biological, three physical) were
considered in the analysis, and the colour bar shows the number of
variables that had a significant shift on a maximum of eight (100%).
The two vertical grey bars in (b) show the centre of the two major
discontinuities revealed by MMS–SMW. SAL, PC1 on salmon catch;
CFIN, PC1 on C. finmarchicus; EUPH, PC1 on euphausiids; COP, PC1
on total copepods; PHC, PC1 on phytoplankton colour; NHT,
northern hemisphere temperature anomalies; SST, PC1 on annual
SST; AMO, Atlantic Multidecadal Oscillation.
of the corrected degrees of freedom in Table 2). When there is
strong autocorrelation in a time-series, the reduction in the
degrees of freedom is so extreme that it can lead to non-significant
correlations at the threshold of 0.05. Some authors have stressed
that “an arbitrary division of results, into ‘significant’ or ‘nonsignificant’ according to the P value, was not the intention of the
1557
founders of statistical inference” (Sterne and Smith, 2001).
Rodionov and Overland (2005) and Luczak et al. (2011) used a
threshold of 0.1 when temporal autocorrelation was accounted
for. Knowing that the classical (unsubstantiated) threshold of
5% results in five false positives on 100 tests and that the correction to account for temporal autocorrelation is conservative, we
also considered some correlations with probabilities ,15% after
accounting for temporal autocorrelation, providing that the coefficient of correlation explained .50% of the total variance. Such
high probabilities were, however, only observed for 3 of 20 correlations we considered to be significant (Table 2). In all three cases,
the high probability was related to a large degree of freedom lost
because of the strong linear temporal autocorrelation in the
data. For example, NHT anomalies have exhibited a strong
quasi-linear trend since the 1960s. This greatly reduces the
degrees of freedom and may substantially increase the type II
error. Other correlations were closer to the traditional level of
probability used in ecology (Table 2). In particular, all probabilities of correlations calculated between physical variables and
1SW salmon were ,0.05.
Climate warming may lead to a linear or monotonic increase in
both climatic and biological variables, which is likely to be associated with a reduction in other sources of variability, such as
pseudo-cyclic and year-to-year variability (Beaugrand and Reid,
2003). If this is the case, the reduction in the degrees of freedom
may become extreme and lead to non-significant correlations if
the emphasis is made on the type I error, whereas we believe the
goal should be to reduce the type II error. As global warming is
expected to become stronger in decades to come, all time-series
will exhibit a monotonic trend, and so will the responses of
many biological variables influenced by temperature. As the
year-to-year variability is expected to diminish relative to the
effect of global warming, this will lead to high correlations
between time-series such as those observed in the present study,
which would not be significant after considering the effect of temporal autocorrelation. Our results suggest that one needs to interpret with great caution the results from correlation analysis and
that one needs to have a balanced view when quantifying the statistical relationships between climate and biological and ecological
variables. We expect this effect (i.e. high correlation and a low level
of significance) to be more frequently reported in future bioclimatological studies.
The spatial patterns of correlations between annual SST and the
winter NAO index remained similar, supporting the persistent
spatial pattern of correlation between these two variables shown
previously (see figure 9 in Beaugrand and Reid, 2003). In contrast,
correlations between SST and NHT showed a reinforcing trend in
recent years, probably reflecting a northward propagation of global
warming signals.
The main causative mechanisms behind the correlations with
salmon shown here are considered to be via North Atlantic SST,
the trend of which was correlated positively with NHT, acting directly on the physiology and distributions of both the plankton and
the salmon, as well as indirectly through a reduction in the size and
abundance of the salmon’s planktonic food. Changes in the
thermal regime of the natal rivers may also have contributed to
the correlations because they affect a wide range of factors, including physiology, distribution, and population traits, that include
migration (Jonsson and Jonsson, 2009; Moore et al., 2012).
The review by Jonsson and Jonsson (2009) also suggests that as
temperature changes have intensified over land, the effects in
1558
G. Beaugrand and P. C. Reid
Figure 9. Correlation analyses between annual SST and winter NAO index (left) and NHT anomalies (right) for three periods, 1960 – 1976,
1977 –1993, and 1994 – 2010. All periods had the same number of years (17) and therefore the same number of degrees of freedom.
rivers are likely to be more pronounced. This suggestion is supported by a study of brown trout (Salmo trutta) in Swiss rivers,
which has shown a pronounced decline in catches (50%) after
a step-change in river temperature in 1987 (Hari et al., 2006).
The decline in the trout is attributed to a combination of effects
from rising temperature, including physiology, temperature
thresholds, feeding, and disease.
In the current analysis, the first PCs derived from the plankton
data exhibited a second synchronous abrupt shift in 1996/1997 as
well as the previously documented shift in 1986/1987. We stress,
however, that as we found previously, the timing of these shifts
varies from one species to another (C. finmarchicus vs. euphausiids), from one oceanic region to another (areas around the UK
vs. the Subpolar Gyre region to the south of Iceland), and also
from one statistical method to another (Beaugrand, 2004). Here,
MMS–SMW and the time-constrained cluster analysis identified
the two shifts at the same time, although MMS–SMW shows
that the time of the shift occurred through several years at larger
temporal scales (i.e. time-windows .6 years). Some preliminary
evidence for the second shift south of Iceland was noted by
Beaugrand and Reid (2003) in the form of an increase in PHC.
The period after the 1996/1997 shift was characterized by a reduction in salmon catches, C. finmarchicus, euphausiids, and the
number of copepods, and an increase in PHC. It should be
noted that the increase in the number of small copepods found
in the earlier study changed to a clear decline from the end
of the 1990s.
The 1996/1997 shift coincided with the pronounced mid
1990s decline in the circulation of the Subpolar Gyre and the associated changes in phytoplankton, zooplankton, blue whiting
(Micromesistius poutassou), and pilot whales (Globicephala melas)
described by Hátún et al. (2009). The warmer water from the
Subtropical Gyre gradually spread around the margins of the
Subpolar Gyre and up the west coast of Greenland (Hughes et al.,
2010). The changes they detected after 1995 saw “some of the saltiest and warmest waters ever found” on the banks off West
Greenland, with a larger transport of Irminger Water into the
Labrador Sea that appears to be close to record levels (Myers
et al., 2007). These increases in temperature led to a reduction in
sea ice and more favourable feeding conditions for cod (Gadus
morhua), capelin (Mallotus villosus), and probably salmon,
reflected in an increase in the residence time and condition of
harbour porpoises (Phocoena phocoena) in the region
(Heide-Jørgensen et al., 2011). The timing of the 1996/1997 shift
also appears to be linked to the initiation of a rapid decline in the
mass balance of the Greenland ice sheet, warmer subsurface sea
temperature west of Greenland, and acceleration in the retreat of
its peripheral glaciers (Holland et al., 2008; Myers et al., 2009;
Motyka et al., 2011; Post et al., 2011). The timing of these events
also coincides with the regime shift noted by Raitsos et al. (2010)
1559
Salmon, plankton, and hydroclimatic change in the NE Atlantic
in the Aegean Sea and the abrupt ecosystem shift found in the Bay of
Biscay, the Celtic Sea, and the North Sea for three trophic levels:
zooplankton, anchovy Engraulis encrasicolus and sardine Sardina
pilchardus, and the Balearic shearwater Puffinus mauretanicus
(Luczak et al., 2011). Finally, the shift is also evident in the
pattern and later timing of smolt emigration from the River
Bush in Northern Ireland, with a highly significant difference
between the periods 1978–1996 and 1997–2008 (Kennedy and
Crozier, 2010).
Although the time-constrained cluster analysis clearly identified two shifts ( 1986/1987 and 1996/1997), a close examination of Figures 5 and 6 shows the existence of subtle variations
in the timing between time-series that become more evident
when MMS– SMW is applied. The differences between time-series
(among physical or biological time-series) are mentioned regularly
in the literature (e.g. Beaugrand, 2004). The examination of PCs
can be subjective and depends on whether one looks at the start
of the new regime, the end of the previous regime, or the
middle of the phase transition. We therefore applied both a timeconstrained cluster analysis and MMS–SMW to identify the
timing of the shift. However, the difference can originate from
the inherent biological properties of taxonomic groups. Species
of different taxonomic groups will not react to or be influenced
by the same environmental factors. The difference in timing was
also obvious in the analysis performed by Lindegren et al.
(2010). The timing of a shift rarely coincided from one trophic
group to another. This was especially the case for plankton,
benthic organisms, and fish using the sequential t-test method
of Rodionov (2004). Beaugrand (2004) stressed that the nonsynchronicity between taxonomic groups often originates from
the type of statistical techniques applied to detect a shift and
that this should not be related to the robustness of a statistical
technique, but instead to its nature. For example, a cluster analysis
is expected to reveal homogeneous periods, whereas an analysis of
the local variance in a time-series focuses on temporal heterogeneity (Beaugrand et al., 2008).
The synchrony of the changes seen in different natural systems
of the North Atlantic suggests that a common atmospheric forcing
might be implicated, and it is very likely that global warming is the
agent responsible for the detected teleconnections. In an investigation of long-term monthly changes of SST in shelf/slope waters of
the world from 1960 to 2008, Reid and Beaugrand (2012) found
that SST increased in three progressive steps in 1976/1977,
1986/1987, and 1997/1998, the last two closely coinciding with
the changes described here. The abrupt climatic shift at the end
of the 1990s was detected in all ten continental shelves examined
and was associated with increasing SST in eight shelves and a decrease in the Northeast and Southeast Pacific, both of which are
strongly influenced by the El Niño Southern Oscillation. The
changes seem to be a response to a globally synchronous pattern
of stepwise increases in SST emanating from the tropics (Reid
and Beaugrand, 2012). Poleward transport of warm water in
shelf slope currents on the eastern margins of continents, important migratory corridors for salmon, is considered to be a strong
candidate to explain the physics behind the changes (Reid and
Beaugrand, 2012).
There is substantial evidence to show that temperature governs
the growth, survival, and maturation of salmon during their
marine migrations, either through direct physiological or behavioural responses or indirectly through temperature effects on the
ecosystem and food of the salmon (Beaugrand and Reid, 2003;
Friedland et al., 2005; Todd et al., 2008). New observations,
obtained using data-storage tags applied to kelts, suggest that
diel vertical migrations of salmon at sea from warm surface
waters to cooler waters with abundant prey may confer metabolic
benefits to salmon (Reddin et al., 2011) and further emphasize the
importance of temperature in salmon ecology. Temperature also
strongly influences the life history of salmon in their natal rivers
(Jonsson and Jonsson, 2009; Moore et al., 2012). In particular, a
temperature of 108C is considered to be the releasing trigger
that initiates migration to the sea (McCormick et al., 1998;
Björnsson et al., 2011).
The Atlantic Ocean plays a key role in the circulation of global
heat as part of the Atlantic Meridional Circulation. There is a
much greater penetration of heat into the Arctic via the North
Atlantic than via the North Pacific. This is, in part, why the
annual mean temperature of London is 118C warmer on
average than in the coastal town of West St Modeste at a similar
latitude in Labrador and why the thermally limited geographic distribution of North Atlantic salmon has a much shorter latitudinal
range in the western than in the eastern North Atlantic. It may also
be the reason, with possibly the rate of change, why salmon in the
more southerly rivers of their range on both sides of the Atlantic
are declining most rapidly, with the expectation (Jonsson and
Jonsson, 2009) that this pattern will continue with reduced production and population extinction at the southern edge of their
distribution.
Conclusions
The updated results presented here confirm and reinforce those
presented in the earlier study by Beaugrand and Reid (2003). A
pronounced and parallel decline in zooplankton (total copepods,
C. finmarchicus, and euphausiids) from 1960 to 2009 and
salmon catches paralleled an increasing trend in SST, AMO,
NHT, and phytoplankton. The relationship noted previously
between SST in the North Atlantic and NHT was especially
strong and enhanced in recent years. The trends in all examined
variables were interrupted by two pronounced ecosystem shifts
associated with a stepwise increase in temperature and an alteration in planktonic ecosystems that might have influenced
salmon survival and growth at sea. The shifts coincided with acceleration in the decline in salmon stocks reflected in greater mortality at sea and poor return rates to spawning rivers.
There is now little doubt that the hydroclimatic and associated
ecosystem changes in the Northeast Atlantic over the past three
decades (Beaugrand, 2009) have played a major part in the
decline of salmon stocks over the same period. The changes have
happened rapidly, and it may be that the salmon cannot adapt
quickly enough to the speed of change. Recent high temperatures
are considered to be anomalous within the context of the past 20
000 years (Björck, 2011), and there is “very high confidence”
(IPCC, 2007a) that the warming is forced by human activities
linked to the global carbon cycle. Temperatures are predicted by
IPCC (2007b) to continue to increase over the next century, so
the pattern of declining salmon stocks is likely to be reinforced
with a northerly expansion to colonize new, and to abandon
more southerly, spawning rivers. More ecosystem shifts and
changes in migration routes and feeding areas are also likely,
with unknown consequences for the total stock, and it will be difficult to predict and model future changes on the basis of present
knowledge.
1560
Our study also raises an important potential issue for future bioclimatological research. Although correlations between changes in
salmon, plankton, and large-scale hydroclimatic indices, including
temperature, have been reinforced, the significance of the correlations has been reduced after accounting for temporal autocorrelation. As global warming is expected to lead to more monotonic
changes in temperature and perhaps fewer associated effects of climatic variability, the autocorrelation will increase. If natural
systems respond to climate change, it is likely that they will also
exhibit the same trend. Therefore, detecting significant correlations
at the traditional threshold level of 0.05 may become impossible,
even for correlations .0.8 and based on 50 uncorrected degrees
of freedom. To ensure the correct detection of the effects of
climate change on natural systems, one possibility may be to alleviate the level of significance when correlations are very high, so as
not to miss any effect of climate change on marine ecosystems.
Another possibility is to decompose time-series into components
(Beaugrand et al., 2003). More studies are clearly needed on this
issue, and we expect such an effect to be more frequently reported
in years to come.
Management approaches that can be applied to salmon stocks
in the marine environment in response to a predicted continuing
rise in sea temperature attributable to climate change are limited.
Bycatches may be reduced by applying new understanding of the
salmon’s thermal habitat and vertical and lateral migration, especially in known migratory corridors. Otherwise, the most appropriate approach to conserving salmon stocks is to ensure the
maximum output of wild smolts from all rivers.
Acknowledgements
We thank the owners and crews of the ships that tow CPRs on a
voluntary basis, and the funders of SAHFOS who help to maintain
the long-term time-series of the CPR survey. This work was in part
funded by SAHFOS, The Marine Institute, Plymouth University,
MBA, and the EU regional programme BIODIMAR. Permission
from the ICES General Secretary for the use of data from tables
in ICES (2011) is gratefully acknowledged, as is the help of
Henrik Sparholt, ICES Secretariat, in their provision. We thank
the two referees and Peter Hutchinson for their constructive
suggestions that helped improve the original manuscript.
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