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# Relationships between North Atlantic salmon, plankton, and hydro-climatic change in the Northeast Atlantic

Gregory Beaugrand, P.C. Reid

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Gregory Beaugrand, P.C. Reid. Relationships between North Atlantic salmon, plankton, and hydro-climatic change in the Northeast Atlantic. ICES Journal of Marine Science, 2012, 69 (9), pp.1549-1562. 10.1093/icesjms/fss153. hal-00806726

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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 Beaugrand<fnref n="1" /> and Philip C. Reid<fnref n="2" />,<fnref n="3" />,<fnref n="4" />*

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

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

<footnote n="1">¹CNRS, Université des Sciences et Technologies de Lille, 62930 Wimereux, France</footnote>

<footnote n="2">²Sir Alister Hardy Foundation for Ocean Science, Plymouth PL1 2PB, UK</footnote>

<footnote n="3">³Marine Institute, Plymouth University, Plymouth PL4 8AA, UK</footnote>

<footnote n="4">⁴Marine Biological Association of the UK, Plymouth PL1 2PB, UK</footnote>

<footnote>*Corresponding Author: tel: +44 1752 633269; fax: +44 1752 600015; e-mail: pcre@sahfos.ac.uk</footnote>
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 ~0.7°C 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 long-term 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

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Long-term changes in global surface temperature anomalies (combined sea and land) from 1881 to 2010.</figcaption></figure>

(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 0.4°C 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 25–60°N 7–75°W, 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 2° × 2° 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

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/
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 1° longitude × 1° latitude in a spatial domain ranging from 40 to 70°N and from 30°W to 20°E. 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 1° longitude × 1° 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 non-significant 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).
## 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 time-windows, 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 $p = 0.05$ 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 ($r = -0.84$; $p_{ACF} = 0.15$; d.f.c. = 2), explaining ~71% of the total variance. This was also the case when correlation between the first PC and the AMO index was calculated ($r = -0.60$; $p_{ACF} = 0.15$; d.f.c. = 6), although

<figure><img src="image_4.png" /><figcaption>**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).</figcaption></figure>

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 ($r = -0.21$; $p_{ACF} = 0.43$; d.f.c. = 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 ($r = 0.86$, $p < 0.05$) and non-maturing ($r = 0.98$, $p < 0.05$) 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 ($r = 0.61$, $p < 0.05$) and non-maturing ($r = 0.84$, $p < 0.05$) 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 – 2010 vs. 1971 or 1983 – 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
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 ($r = 0.88$; $p_{ACF} = 0.05$; d.f.c. = 3) and the AMO index ($r = 0.85$; $p_{ACF} = 0.03$; d.f.c. = 4), but not with the

NAO index ($r = 0.13$; $p_{ACF} = 0.62$; d.f.c. = 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 ($r = -0.65$; $p_{ACF} < 0.001$; d.f.c. = 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 ($r = -0.13$; $p_{ACF} = 0.63$) and the AMO index ($r = 0.11$; $p_{ACF} = 0.66$).

<figure><img src="image_5.png" /><figcaption>**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.</figcaption></figure>

## Long-term changes in salmon and hydroclimatic and plankton fluctuations (1960–2009)

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><caption>**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.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">PCA on estimated numbers of maturing 1SW salmon</th><th colspan="2">PCA on estimated numbers of non-maturing 1SW salmon</th></tr><tr><th>First eigenvector</th><th>Second eigenvector</th><th>First eigenvector</th><th>Second eigenvector</th></tr></thead><tbody><tr><td>Finland</td><td>0.2155</td><td><strong>-0.859</strong></td><td>0.2656</td><td><strong>-0.8668</strong></td></tr><tr><td>Iceland (northeast)</td><td>-0.4828</td><td>-0.1849</td><td><strong>0.7702</strong></td><td>0.1307</td></tr><tr><td>Norway</td><td><strong>0.9181</strong></td><td>-0.3014</td><td><strong>0.6529</strong></td><td>-0.0595</td></tr><tr><td>Russia</td><td>0.1637</td><td><strong>-0.8589</strong></td><td><strong>0.6367</strong></td><td>-0.5956</td></tr><tr><td>Sweden</td><td><strong>0.7375</strong></td><td>-0.2933</td><td><strong>-0.8184</strong></td><td>-0.1505</td></tr><tr><td>France</td><td><strong>0.8375</strong></td><td>0.3237</td><td><strong>0.7561</strong></td><td>0.3616</td></tr><tr><td>Iceland (southwest)</td><td>-0.2849</td><td>0.2066</td><td><strong>0.9054</strong></td><td>0.1383</td></tr><tr><td>Ireland</td><td><strong>0.8453</strong></td><td>0.0335</td><td><strong>0.8908</strong></td><td>-0.1203</td></tr><tr><td>UK, England and Wales</td><td><strong>0.86</strong></td><td>0.1645</td><td><strong>0.8085</strong></td><td>0.1592</td></tr><tr><td>UK, Northern Ireland</td><td><strong>0.5279</strong></td><td>0.184</td><td><strong>0.5584</strong></td><td>-0.2274</td></tr><tr><td>UK, Scotland</td><td><strong>0.7002</strong></td><td>0.3572</td><td><strong>0.8808</strong></td><td>0.149</td></tr></tbody></table>

Values in bold are >0.5.
<figure><img src="image_6.png" /><figcaption>**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.</figcaption></figure>

were significant (1SW, Table 2) or close to significance (e.g. MSW and NHT at $p = 0.06$; Table 2) when the traditional threshold of $p = 0.05$ 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 long-term 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 time-windows. At shorter time-windows, the first shift was stronger than the second in terms of implicated variables. The variables
<figure><img src="image_7.png" /><figcaption>**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.</figcaption></figure>

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

of global warming in regions that were typically strongly influenced by natural sources of hydroclimatic variability, such as the NAO.

## Discussion

Beaugrand and Reid (2003) examined long-term changes in phyto-plankton, 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 9–10°C 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 ($r = -0.72, 1960–2000$ vs. $r = -0.86, 1960–2010$), 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, $p = 0.05$. 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
<table><caption>**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.</caption><thead><tr><th>Variable<sup>a</sup></th><th>Salmon catch</th><th>1SW</th><th>MSW</th></tr></thead><tbody><tr><td>NHT</td><td>-0.84 (p = 0.15, d.f.c. = 2)</td><td>-0.77 (p = 0.04, d.f.c. = 5)</td><td>-0.85 (p = 0.06, d.f.c. = 3)</td></tr><tr><td>AMO</td><td>-0.60 (p = 0.15, d.f.c. = 6)</td><td>-0.70 (p = 0.03, d.f.c. = 7)</td><td>-0.79 (p = 0.06, d.f.c. = 4)</td></tr><tr><td>PC1 SST</td><td>-0.74 (p = 0.15, d.f.c. = 3)</td><td>-0.79 (p = 0.05, d.f.c. = 4)</td><td>-0.78 (p = 0.11, d.f.c. = 3)</td></tr><tr><td>PC1 PHC</td><td>-0.75 (p = 0.09, d.f.c. = 4)</td><td>-0.64 (p = 0.12, d.f.c. = 5)</td><td>-0.78 (p = 0.12, d.f.c. = 3)</td></tr><tr><td>PC1 copepods</td><td>0.72 (p = 0.07, d.f.c. = 5)</td><td>0.73 (p &lt; 0.01, d.f.c. = 10)</td><td>0.63 (p = 0.12, d.f.c. = 5)</td></tr><tr><td>PC1 C. finmarchicus</td><td>0.82 (p = 0.09, d.f.c. = 3)</td><td>0.54 (p = 0.21, d.f.c. = 5)</td><td>0.74 (p = 0.14, d.f.c. = 3)</td></tr><tr><td>PC1 euphausiids</td><td>0.81 (p = 0.09, d.f.c. = 3)</td><td>0.66 (p = 0.10, d.f.c. = 5)</td><td>0.78 (p = 0.12, d.f.c. = 3)</td></tr></tbody></table>

<sup>a</sup>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. = 34) and 50 years (d.f. = 48) long.

<figure><img src="image_8.png" /><figcaption>**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).</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**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.</figcaption></figure>

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* = 0.05 for some correlations was related to the strong autocorrelation of time-series, both physical and biological (see the reduction
<figure><img src="image_10.png" /><figcaption>**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.</figcaption></figure>

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 ‘nonsigni-
ficant’ according to the *P* value, was not the intention of the

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 dir-
ectly 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, includ-
ing 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
<figure><img src="image_11.png" /><figcaption>**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.</figcaption></figure>

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)
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 time-constrained 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 non-synchronicity 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 ~10°C 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 ~11°C 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.
Our study also raises an important potential issue for future bio-
climatological research. Although correlations between changes in
salmon, plankton, and large-scale hydroclimatic indices, including
temperature, have been reinforced, the significance of the correla-
tions has been reduced after accounting for temporal autocorrel-
ation. As global warming is expected to lead to more monotonic
changes in temperature and perhaps fewer associated effects of cli-
matic 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 allevi-
ate 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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Handling editor: Emory Anderson