Climate variability and marine survival of coho salmon (Oncorhynchus kisutch) in the Oregon...

Climate variability and marine survival of coho salmon(Oncorhynchus kisutch) in the Oregon production area

J. ANTHONY KOSLOW,1,2,* ALISTAIRJ. HOBDAY1,2 AND GEORGE W. BOEHLERT1

1Paci®c Fisheries Environmental Laboratory, 1352 LighthouseAvenue, Paci®c Grove, CA 93950 USA2CSIRO Marine Research, GPO Box 1538, Hobart, Tasmania7001 Australia

ABSTRACT

Time series of adult recruitment for natural runs ofcoho salmon from the Oregon coastal region (1970±94) and marine survival of hatchery-reared cohosalmon from California to Washington (1960±94) aresigni®cantly correlated with a suite of meteorologicaland oceanographic variables related to the biologicalproductivity of the local coastal region. These vari-ables include strong upwelling, cool sea surfacetemperature (SST), strong wind mixing, a deep andweakly strati®ed mixed layer, and low coastal sealevel, indicating strong transport of the CaliforniaCurrent. Principal component analysis indicates thatthese variables work in concert to de®ne the dom-inant modes of physical variability, which appear toregulate nutrient availability and biological produc-tivity. Multiple regression analysis suggests that cohomarine survival is signi®cantly and independentlyrelated to the dominant modes acting over thisregion in the periods when the coho ®rst enter theocean and during the overwintering/spring periodprior to their spawning migration. Linear relation-ships provided good ®ts to the data and were robust,capable of predicting randomly removed portions ofthe data set.

Key words: California Current, climate, coho,Oncorhynchus kisutch, recruitment, salmon, survival

INTRODUCTION

Salmon of the genus Oncorhynchus have comprisedmajor commercial ®sheries from California to Alaskafor over the past 100 years, and provided a mainstayfor coastal aboriginal peoples for millennia (Magnusonet al., 1996). Recent years, however, have seen adecline in salmon abundance from the Strait ofGeorgia to California, whereas Alaskan salmon runshave been generally at their highest historical levels.Native stocks of coho salmon (Oncorhynchus kisutch)in the southern part of its range have displayed aparticularly worrisome trend, having disappeared fromapproximately half the streams that historically con-tained this species; abundance is presently estimatedto be less than 6% that in 1940 (Brown et al., 1994).Coho salmon are presently listed as threatened inCalifornia and part of Oregon (NMFS, 1999).

Several factors contribute to the decline in salmonabundance off the Paci®c coast of North America:over®shing, freshwater habitat degradation, loss ofgenetic integrity due to interactions of native andhatchery ®sh, and climatic in¯uences, the latter par-ticularly during the marine phase of the life cycle(Moyle, 1994). The critical importance of marine cli-mate on Paci®c salmon production is indicated by:(i) the continued decline in adult coho and chinook(O. tshawytscha) salmon returns despite massive increa-ses in hatchery smolt releases of these species (Brownet al., 1994; Beamish et al., 1995); (ii) synchronoustrends in salmon production affecting several speciesover large geographical areas (i.e. the northernAlaskan extent of the range or from the Strait ofGeorgia south) with northern and southern salmonpopulations often appearing to be out of phase witheach other (Beamish and Bouillon, 1993; Francis andHare, 1994; Hare and Francis, 1995; Gargett, 1997;Mantua et al., 1997; Hare et al., 1999; Welch et al.,2000); and (iii) correlations of salmon productionwith indices of marine climate, such as upwelling(Nickelson, 1986), sea-surface temperature (Lawson,1997; Cole, 2000) and the position and intensity ofthe Aleutian Low pressure system in winter and spring(Beamish, 1993; Beamish and Bouillon, 1993; Hareand Francis, 1995).

*Correspondence. e-mail: [email protected]

Received 17 April 2000

Revised version accepted 11 May 2001

FISHERIES OCEANOGRAPHY Fish. Oceanogr. 11:2, 65±77, 2002

Ó 2002 Blackwell Science Ltd. 65

Changes in the winter/spring Aleutian Low,sometimes called the Paci®c Decadal Oscillation, havebeen linked with regime shifts in the subarctic andCalifornia Current ecosystems, in which the physicaland biological states of these systems appear to shiftabruptly and out of phase with each other (Mantuaet al., 1997). A recent regime shift, which occurredaround the winter of 1976/77, led to a massive declineof zooplankton and salmon in the California Currentregion and their increase in waters off Alaska, alongwith other changes in the pelagic ecosystem (Brodeurand Ware, 1992; Roemmich and McGowan, 1995;Brodeur et al., 1996; Francis et al., 1998; Anderson andPiatt, 1999).

Several hypotheses have been proposed to explainhow changes in the Aleutian Low in winter/springmay lead to opposite changes in biological produc-tion in the northern and southern sectors of theNorth-east Paci®c. One hypothesis points to opposingtrends in the strength of the northward Alaskan andsouthward California Current systems, which lead toopposing trends in the advection of nutrients andplankton into those regions (Wickett, 1967; Cheltonet al., 1982; Chelton, 1984; Francis and Hare, 1994).A second hypothesis points to the in¯uence ofchanges in mixed-layer depth and factors such as SSTon water column stability, which may have oppositeimpacts on productivity in the light-limited northernpelagic ecosystem and the nutrient-limited southerlysystem (Polovina et al., 1995; Gargett, 1997). In avariant on these hypotheses, Parrish et al. (2000)proposed that changes in mid-latitude winter windsacross the North Paci®c basin in recent years led toincreased input of warm, nutrient-poor subtropicalwater into the California Current, thereby account-ing for declining productivity there, whereasincreased Ekman pumping in the central Gulf ofAlaska and onshore transport may have enhancednutrients and productivity in the coastal portion ofthis region.

Most of the signi®cant correlations between large-scale physics and salmon survival at sea occur atdecadal rather than interannual time scales and arerelated to winter rather than spring/summer condi-tions, when the smolts migrate to sea, indicating thatthe mechanisms underlying variability in salmonmarine survival, particularly on interannual timescales, remain to be elucidated (Baumann, 1998).

Furthermore, much of the research on marine cli-mate variability has focused on the open ocean eco-systems of the North Paci®c. However, during their®rst summer at sea, coho in the Paci®c north-westreside in nearshore waters and do not appear to

migrate far from their point of entry to the sea (Pearcyand Fisher, 1988). Coho distribution during theirsecond year at sea is not well understood, but a portionof the populations appears to remain in this coastalzone, while another portion may migrate northwardinto Alaskan waters (Hartt and Dell, 1986). Bothjuveniles and adults prey on a mix of juvenile ®shes(e.g. scorpaenids and clupeids) and macrozooplankton,such as euphausiids and decapod larvae (Brodeur andPearcy, 1990). Most coho complete their life cycle bymigrating to spawn in their natal streams during theirsecond year.

Correlations of salmon production trends withlarge-scale oceanographic processes have been usefulin pointing to climate's critical impact, but elucida-tion of mechanisms will require understandingoceanographic links on the scale of the salmon popu-lations themselves. However, there are no time seriesof abundance for the prey of coho, so studies of localconditions in relation to marine survival have per-force used proxy variables, such as upwelling and sea-surface temperature (SST) (Scarnecchia, 1981;Nickelson, 1986; Fisher and Pearcy, 1988; Lawson,1997; Cole, 2000). Unfortunately the relationshipbetween upwelling and coho survival appearedto break down during periods of low upwelling(Nickelson, 1986; Lawson, 1997), and the role of SSTin coho survival is not transparent, since it may affectbioenergetics, the movements of predators or indicatethe productivity of waters in the region of the Cali-fornia Current (Roemmich and McGowan, 1995;Lawson, 1997).

The use of simple indicator variables as predictorsof ®shery recruitment has a checkered historybecause variables, such as SST, may affect therecruitment process in several ways, whose relativein¯uence may vary through time; also, the in¯uenceof any single factor may vary from one set of con-ditions to another (Parrish et al., 2000). For example,strong upwelling may be present during an El NinÄobut not lead to expected levels of production,because warm surface conditions or a deep thermo-cline restrict the normal upward ¯ux of nutrients intonear-surface waters.

We therefore examine here the use of moresophisticated indices of ocean conditions than can beobtained from single variables. An adequate index ofproductive conditions may require data on upwelling,SST, mixed layer depth and strati®cation, nutrients,wind mixing and possibly other variables. Ourapproach here is to synthesize a broad suite of envi-ronmental variables, to seek out the dominant modesor patterns of oceanographic variability in the coho

66 J. Anthony Koslow et al.

Ó 2002 Blackwell Science Ltd., Fish. Oceanogr., 11:2, 65±77.

coastal habitat and to examine whether and how thesepatterns may be related to coho marine survival.

METHODS

Physical data

The physical variables used in this study and theirsources are listed in Table 1. All data were obtainedfor the period 1960±94, because marine survival datafor the coho were not available prior to 1960, andsubsurface oceanographic data from the World OceanDatabase were not consistently available post-1994.Monthly means for SST, salinity, nitrate, indices ofmixed layer depth, strati®cation and temperature atthe base of the mixed layer were computed initially for`boxes' extending 0.5° longitude outward from thecoast and 1.0° in latitude. These `boxes' were subse-quently combined into areas extending 3.0° in latitudealong the coastal zone, based on a preliminary prin-cipal component analysis that indicated this was the

scale of coherence in the coastal oceanography, con-sistent with the climatology of Strub et al. (1987): Pt.Conception ± San Francisco (latitude 34.5°±37.5°N);San Francisco ± Cape Mendocino (latitude 37.5°±40.5°N); Cape Mendocino ± Cape Blanco (latitude40.5°±43.5°N); Cape Blanco ± Columbia River (lati-tude 43.5°±46.5°N); Columbia River ± Puget Sound(latitude 46.5°±49.5°N) (Fig. 1). Time series formonthly means of the Bakun upwelling index (Bakun,1973; Schwing et al., 1996), wind speed, the east±westand north±south components of pseudo-wind stress(including the Trenberth et al. (1990) nonlinear dragcoef®cient and the da Silva et al. (1994) correction tothe Beaufort scale), the curl of the wind stress(a measure of Ekman pumping), wind speed-cubed(a measure of wind mixing) were also obtained forevery 3° in latitude along the coast.

The depth to the base of the thermocline was usedas an index of mixed layer depth (MLD). Thermoclinedepth was de®ned as the depth where the change intemperature was most rapid, i.e. dT/dz was maximum,starting from the bottom of the cast and selecting thedepth at which the temperature difference was max-imal. Temperature has often been used to de®ne MLD,particularly in areas such as ours where salinity data

Table 1. Variables used in this study and their sources. Seetext for sources of biological variables.

VariableVariablename

Physical variablesSea-surface temperature* SSTNitrate NO3

Salinity S

Strati®cationMaximum temperature gradient MaxdTdZ

Mixed-layer depth (MLD)Depth to maximum temperature gradient Z_maxdTdZ

Temperature at the base of the mixed layer T_maxdTdZUpwelling Indexà UpwellScalar wind speed* WdPseudo wind stress (S±N component)* WdSNPseudo wind stress (E±W component)* WdEWWind mixing (wind speed cubed)* Wd3Wind stress curl* CurlSea level height at San Francisco§ SL_SF

Biological variablesOregon Production Area marine survival OPIOregon coastal natural coho returns OCN

*Comprehensive Ocean Atmosphere Data Set (COADS)(web-site: HTTP://www.pfeg.noaa.gov) (Woodruff et al.,1998) extracted using Mendelssohn and Roy (1996). NOAA Ocean Climate Laboratory World Ocean Database(1998) website: HTTP://www.nodc.noaa.gov. àBakun (1973),Schwing et al. (1996). §Website: HTTP://uhslc.soest. hawaii.edu/.

Figure 1. Chart of the study region off the west coastof North America showing the areas (1±5) within whichenvironmental data were averaged.


Coho marine survival and ocean climate 67

are scarce. Although this index is problematic inregions of relatively high freshwater input, such as thesub-Arctic Paci®c (Freeland et al., 1997), it appearsreasonable for the southern portion of our study area,where there were suf®cient data to calculate thermo-cline depth. The calculation was based on castsarchived in the World Ocean Database 98 StandardLevel Temperature Pro®les (Levitus et al., 1998). Onlycasts to depths greater than 100 meters were includedto remove shallow casts where the MLD is often poorlyresolved.

Missing values were a major problem for the sub-surface oceanographic time series, particularly salinity,nitrate and chlorophyll concentrations at 10 m depth.Chlorophyll was dropped entirely from the analysisbecause > 75% of monthly values were missing in allareas. Nitrate values were missing for 58% of monthlyvalues for the ®rst area (latitudes 34.5±37.5°S) and> 75% of monthly values in all other areas. Salinitywas missing for 44±46% of monthly values in the ®rsttwo areas, but > 65% elsewhere.

Biological data

Marine survival of hatchery-reared coho was based ontotal hatchery releases within the Oregon ProductionIndex (OPI) area [Monterey Bay, California to Lead-better Point (Willapah Bay), Washington (36.5°±46.5°N latitudes)] and the adult coho returns, adjustedfor the wild salmon returns but not adjusted for returnsof jacks in the previous year (< 10% of the adultreturn) (Paci®c Fishery Management Council, 1998,T. Nickelson, Oregon Department of Fish and Wild-life, unpublished data). The number of Oregon coastalnatural (OCN) adult coho returning was used as anindex of survival of wild populations (Lawson, 1997).Smolt production is not known for the natural popu-lations, so this index may be affected by changes inspawning biomass, as well as by changes in freshwaterhabitat. The OCN time series is considered to over-estimate true abundance by about 50±80% (Jacobs andNickelson, 1998), but as a recruitment index it isbelieved to capture interannual variations in recruit-ment (Lawson, 1997). All coho indices were lagged tothe year of release.

The physical data were examined over a somewhatlarger area than the region of coho release because thecoho migrate north and south of their release pointsoon after ocean entry, although the range of theirultimate distribution is not well known (Pearcy andFisher, 1988).

Coho marine survival data are also available basedon the return of coded-wire tagged smolts releasedfrom hatcheries in California, Oregon and

Washington (e.g. Ryding and Skalski, 1999). However,these data sets commence in the mid-1970s, so theycannot be used to compare survival before and afterthe oceanographic regime shift of 1976±77. These datasets were therefore not used in the present study.

The OPI index did not deviate signi®cantly from anormal distribution [Kolmogorov±Smirnov (K-S) test,P > 0.10]. However, the OCN recruitment indexexhibited a marginally nonsigni®cant deviation fromnormality (K-S Z � 1.33, P � 0.06), so this variablewas log-transformed for statistical analysis, whichnormalized the distribution.

Data analysis

Relationships between marine survival of coho smoltsand environmental variables were examined duringtwo periods of the life history: during the April±Juneperiod of peak release and early marine residence,which has been previously identi®ed as a `critical'period (Scarnecchia, 1981; Nickelson, 1986; Fisherand Pearcy, 1988) and over the 6-month winter±spring period (January±June) of the following yearprior to the return migration, when signi®cant corre-lations with coho survival have also been observed(Lawson, 1997; Cole, 2000). Initial exploratory ana-lysis was based on examination of scatterplots andcorrelations between pairs of variables. However,potentially there were far more independent variables(> 10 variables ´ ®ve areas) than degrees of freedom,with varying degrees of correlation among the physicalvariables. Principal component (PC) analysis wastherefore carried out on the physical variable data setto reduce its dimensionality and obtain a relatively feworthogonal regressors that contained most of thevariance of the original data set (Wonnacott andWonnacott, 1979, p. 419). The sign of PCs is arbitrary.To facilitate ease of interpretation, the sign of the PCswas set, so far as possible, so they were positivelyassociated with conditions conducive to biologicalproductivity. Correlation and multiple regressionanalyses were then carried out between the OCN andOPI indices of coho marine survival and the dominantPCs for physical variables during both the April±Juneperiod of smolt outmigration and the January±Juneperiod of the second year at sea.

To test the power of multiple regressions to predictcoho marine survival, approximately half the yearswere randomly removed from the time series and anew regression relationship obtained with the dom-inant PCs. This new relationship was used to `predict'marine survival for the missing years; predicted valueswere then correlated with actual values. This proce-dure was repeated 100 times.



RESULTS

Initial correlation analyses

The marine survival indices for hatchery (OPI) andwild (OCN) coho were signi®cantly correlated(r � 0.77, n � 25, P < 0.001) (Fig. 2). Both smoltsurvival indices were signi®cantly and positively cor-related with a number of physical variables averagedover the April±June period of smolt outmigration:in particular, strong upwelling, low strati®cation, highnutrient and salinity, low sea level (i.e. strong south-ward transport of the California Current), low SSTand low temperature at the base of the thermocline,wind stress curl and winds from the north (Table 2).Correlations are shown for time series centred atapproximately 45°N latitude, where coho smolt pro-duction is concentrated, but correlations weregenerally signi®cant with physical variables fromnorthern California (Cape Mendocino) to theColumbia River (~ 40°±46°N latitude) and often withtime series south and north as well. This could be dueto several factors: coherence of environmental vari-ability throughout the region of the California Cur-rent, the extent of the Oregon Production Area, andthe apparent utilization by coho smolts of the coastalregion from northern California to Washington duringtheir ®rst summer at sea (Pearcy and Fisher, 1988).Bivariate scatterplots (not shown) indicated thatwhere correlations were signi®cant, relationshipsconsistently appeared linear.

Many of the physical variables were intercorrelated,particularly within each local area, with correlations ofthe order of 0.3±0.7. Many of the correlationsappeared to be of similar sign to the correlations withcoho smolt survival, such that upwelling, cold seatemperatures, strong wind mixing as well as winds

from the north and east, low strati®cation, high nitrateand salinity appeared interrelated.

Principal component analyses

An initial PC analysis of the April±June physical dataset included all variables considered important tobiological productivity or as indicators of physical andbiological conditions, including salinity and nitrate,which contained a high proportion of missing values.Nitrate concentration loaded highly on the ®rst PC(r � 0.72). However, even after restricting the salinityand nutrient variables to the more data-rich southernportion of the study area, the resulting PC time seriescontained values for only 11 years, which was insuf®-cient for further analysis. Nitrate and salinity, alongwith variables based on subsurface temperature forlatitudes north of 40.5 (i.e. mixed layer depth, strati-®cation and temperature at the base of the mixedlayer), were therefore deleted from the analysis, andthe resulting PC time series contained 33 values. PC 1,which explained 25% of the variance, was highlycorrelated (r � 0.97) with the ®rst PC from the initialanalysis that contained all variables, indicating thatthe dominant mode obtained from these analyses wasessentially identical, and that it is a good indicator ofsubsurface nutrient concentration in the April±Juneperiod, at least in the southern portion of the region.The PC analysis based on the reduced set of variableswas used in all subsequent analyses.

Table 2. Correlations of indices of coho marine survivalwith physical variables averaged over April±June of the yearof smolt release.

OPI OCN

MaxdTdZ )0.32 )0.44*NO3 0.39 0.61**S 0.32 0.49*SL_SF )0.42* )0.49*SST )0.52*** )0.56**T_maxdTdZ )0.40* 0.63**Upwell 0.47** 0.53**WdEW 0.36* 0.58**WdSN )0.50** )0.42*Curl )0.40* )0.23

OPI, survival of hatchery-released smolts from the OregonProduction Area; OCN, the log-transformed Oregon coastalnatural (i.e. wild) returns. The physical variables are from~ 45°N latitude, except salinity (S) and nitrate (NO3),which were only available for latitudes 37.5±40.5°, and sealevel at San Francisco (SL_SF). Variable names as inTable 1. Signi®cance: P < 0.10; *P < 0.05; **P <0.01;***P < 0.001.

Figure 2. Time series for survival of coho marine survivalbased on hatchery smolts from the Oregon Production Index(OPI) area and Oregon coast natural (OCN) returns. Dataare plotted against the year ocean entry.



The ®rst 10 PCs had eigenvalues > 1.0, indicatingthat they explained more of the variance than anysingle variable. However only the pattern of loadingsfor the ®rst two PCs were readily interpretable. Thesewere also the only PCs signi®cantly correlated withcoho survival, so we retained only PC 1 and PC 2 forfurther analysis. They explained 25.0% and 14.6% ofthe variance, respectively, of the physical time series.The dominant mode (PC 1) was dominated by phys-ical variability in the southern half of the data range(between » 34.5°and 43.5°S latitude), although it wasstrongly associated with the pattern of SST throughoutthe region. Most strongly represented were winds fromthe north-east, strong wind mixing and wind-inducedupwelling, low sea level at San Francisco (a measure ofstrong southward transport of the California Current),weak strati®cation, and cool SST and temperatures atthe base of the mixed layer (Table 3). This mode wasalso highly correlated with nitrate concentration andsalinity in the southernmost portion of the study area,where there were suf®cient data to test the relation-ship (r � 0.74 and 0.75, respectively; P < 0.001). PC 1displays a declining trend after 1984 (Fig. 3a).

The second PC was associated with upwelling, coolSST and northerly winds, from Oregon northward(42°± 50°N latitude) (Table 3). Although PC 2 wasnot signi®cantly correlated with upwelling, SST or sealevel off California, it was associated with weak windsand weak wind mixing and south-westerly winds in thesouthernmost portion of the study area south of SanFrancisco (34.5°± 37.5°N latitude). PC 2 exhibited nosigni®cant trend through the study period (Fig. 3b).

PC analysis of the January±June environmentaldata set produced similar results (Table 3). Again, 10PCs had eigenvalues > 1.0. However, the ®rst PCexplained somewhat more of the variance (30.1%)than in the analysis of April±June conditions, becauseconditions were more coherent in the combinedwinter±spring period: PC 1 was highly associated withupwelling, cool SST and temperature at the base ofthe thermocline, weak strati®cation, low sea level atSan Francisco (i.e. strong transport of the CaliforniaCurrent), and north-easterly wind anomalies, condu-cive to enhanced upwelling, throughout the studyregion. Unlike the analysis based on April±June dataalone, wind mixing and overall wind strength were notsigni®cantly associated with PC 1. These variableswere the dominant physical variables underlying PCs2±4. However, when the PCs were lagged ahead oneyear, only the ®rst PC (PC 1 L) was signi®cantlycorrelated with survival of hatchery and wild coho inthe Oregon Production area, so it was the only PCretained for further analysis. PC 1 L, unlike the

dominant modes based on April±June data alone,exhibits a signi®cant step decline after the winter of1976 (Fig. 3c) (ANOVA, F1,31 � 12.83, P � 0.001),consistent with other descriptions of regional winterconditions over this time period (Beamish, 1993;Francis et al., 1998). PC 1 L was not signi®cantlycorrelated (P > 0.10) with either PC 1 or PC 2 for thespring of the previous year; these three PCs thus rep-resent independent modes of physical variability overthe region.

The Oregon production index (OPI), the longesttime series of coho marine survival, was signi®cantlypositively correlated with PC 1, PC 2 and PC 1 L,whereas the OCN time series was signi®cantly corre-lated with PC 1 and PC 1 L. Coho survival indices arethus positively correlated with meteorological andoceanographic conditions conducive to high biologi-cal productivity (i.e. upwelling, strong transport of theCalifornia Current, strong wind mixing, cool SST, andweak strati®cation of the upper mixed layer) in nor-thern and southern portions of the region during thesmolts' initial period at sea and prior to the cohoreturn migration (Table 4).

Although most correlations with the dominantmodes of environmental variability were signi®cant,these relationships explained only 10±42% of thevariance in marine survival. Scatterplots indicatedthat when conditions related to productivity were poorduring either the coho's early marine or overwintering/return migration period (i.e. low PC 1, PC 2 or PC1 L), survival was always poor (Figs 4 and 5). How-ever, the variability in marine survival generallyincreased when conditions related to biologicalproductivity, indicated by any single PC, appearedfavourable. The scatterplots indicate, however, thatmuch of this increased variability follows from yearswhen favourable conditions in a particular sector(north or south) or part of the year were offset byconditions that were among the poorest in anothersector or part of that year. Thus, most apparent outliersto a linear relationship between good survival of OPIhatchery coho and favourable productivity conditionsin the southern portion of the region [i.e. years of poorsurvival and moderate-to-high PC 1: 1976, 1981,1982, 1984, 1991, 1994 (Fig. 4a)] were almost all yearsof poor conditions either in the north [i.e. low PC 2:1981, 1984, 1994 (Fig. 4B)] or during the followingyear [low PC 1 L: 1982, 1991 (Fig. 4c)]. Similarly, theyears of anomalously poor survival following years withconditions favourable to biological productivity in thenorth [i.e. years of poor survival and high PC 2: 1982and 1992 (Fig. 4b)] were both years when productivityconditions were extremely unfavourable the following



year (low PC 1 L) (Fig. 4c). Conversely, the years ofhighest marine survival (1966, 1970, 1975) were yearsof favourable conditions across the region during boththeir ®rst and second year at sea. (Note: datum for1966 was missing from the PC 1 L time series.) Thedata are thus consistent with a linear but multivariate

relationship between marine survival in the OregonProduction Area and environmental conditions.

The relationship of the wild OCN runs withenvironmental conditions also appeared linear(Fig. 5). Again, when the returns were relatively poordespite favourable conditions during the period of

Table 3. Factor matrix for PC 1 and 2from principal component (PC)analyses showing the factor loading(= correlation) between each variableand the PCs.

PC 1 PC 2 PC 1L

% variance explained 25.0 14.6 30.1CURL36 0.46** )0.07 0.22CURL39 )0.38* 0.28 0.06CURL42 )0.52** )0.01 )0.39*CURL45 )0.33 )0.59*** )0.44**CURL48 )0.15 )0.32 )0.22MaxdTdZ.1 )0.63*** 0.16 )0.64***MaxdTdZ.2 )0.52** )0.08 )0.36*SL_SF )0.60*** 0.14 )0.78***SST.1 )0.73*** 0.21 )0.90***SST.2 )0.76*** )0.24 )0.91***SST.3 )0.70*** )0.41* )0.85***SST.4 )0.64*** )0.35* )0.86***SST.5 )0.69*** )0.36* )0.86***T_maxdTdZ.1 )0.46** 0.11 )0.74***T_maxdTdZ.1 )0.58*** )0.02 )0.72***UPWELL36 0.77*** )0.02 0.66***UPWELL39 0.78*** 0.13 0.73***UPWELL42 0.61*** 0.65*** 0.84***UPWELL45 0.39* 0.81*** 0.76***UPWELL48 0.23 0.81*** 0.72***WdEW.1 0.55** )0.69*** 0.59***WdEW.2 0.62*** )0.30 0.42*WdEW.3 0.63*** )0.22 0.51**WdEW.4 0.28 )0.15 0.54**WdEW.5 0.34* 0.07 0.55**WdSN.1 )0.55** 0.72*** )0.67***WdSN.2 )0.62*** 0.16 )0.70***WdSN.3 )0.56*** )0.41** )0.72***WdSN.4 )0.18 )0.50** )0.37*WdSN.5 )0.32 )0.65*** )0.47**Wd3.1 0.55** )0.69*** 0.31Wd3.2 0.47** )0.24 )0.11Wd3.3 0.40* )0.08 )0.23Wd3.4 )0.08 )0.32 )0.08Wd3.5 0.05 )0.16 )0.04Wd.1 0.60*** )0.63*** 0.36*Wd.2 0.52** )0.33 )0.05Wd.3 0.27 )0.05 )0.25Wd.4 )0.13 )0.24 )0.09Wd.5 0.11 )0.08 )0.07Z_maxdTdZ.1 0.19 )0.06 )0.16Z_maxdTdZ.2 0.15 )0.15 )0.15

PC 1 & 2 from PC analysis of April±June data; PC 1L from analysis of January±June data. Variable names as in Table 1 with number indicating area as in Fig. 1.The numbers after Curl and Upwell indicate the latitude where the variable iscentred. *P < 0.05; **P < 0.01; ***P < 0.001.



outmigration [e.g. 1981, 1984, 1991 and 1994(Fig. 5a)], conditions were poor during the second yearat sea or in the northern sector (Fig. 5b,c). The1993 years-class, which appears anomalously poorrelative to conditions during the second year at sea(Fig. 5c), encountered poor conditions during theperiod of outmigration (Fig. 5a,b). The scatterplotsthus indicate that the dominant modes of environ-mental variability, representing conditions in the dif-ferent sectors and periods of the coho marine lifehistory, are independently in¯uencing coho marinesurvival, without signi®cant deviation from linearity.

Multiple regression analysis indicated that thedominant PCs contributed signi®cantly and inde-

pendently to marine survival for both hatchery andwild coho (Table 5). PC 1, PC 2 and PC 1 L enteredboth regressions signi®cantly, consistent with ourassessment of the correlations and scatterplots, indica-ting that conditions favourable to biological produc-tivity in both northern and southern sectors andduring both the initial period of smolt outmigrationand during the second year at sea all contribute sig-ni®cantly and independently to coho marine survival.Regressions combining all these terms explained 47%and 83% of the variance in OPI marine survival andOCN coho returns, respectively. Residuals to themultiple regressions were examined using partial-probability and scatterplots in relation to standardizedpredicted values to determine adequacy of ®t.Although there were indications of slight nonlineari-ties at the edge of the distributions, linear ®ts to thedata appeared adequate for prediction within the rangeof upwelling conditions observed. Comparison of thehindcast and observed time series of OPI and OCNreturns indicated that the regressions ®t much of theinterannual variability, as well as the overall trend,with a few exceptions: 1969 and 1985 for the OPIindex and 1978 for the OCN time series (Fig. 6).However, the OPI datum for 1985 appears to be anoutlier in comparison with the OCN data as well(Fig. 2).

The predictive power of the OPI and OCNregressions were examined by randomly removing halfthe years in the time series, re-forming the regressionswith PC 1, PC 2, PC 1 L and `predicting' survival forthe missing years. This procedure was carried out 100times. The mean correlation between the `predicted'and actual OPI values was 0.57 (SD � 0.18). 82% ofcorrelations were signi®cant at P < 0.05. The meancorrelation between `predicted' and actual OCN

Figure 3. Time series for (a) PC 1 and (b) PC 2 for April ±June environmental data and (c) PC 1 based on January ±June environmental data, lagged one year. See Table 3 forvariable loadings in relation to environmental variables.

Table 4. Correlations between indices of coho marinesurvival and marine environmental conditions.

OPI OCN

PC1 0.36* 0.54**PC2 0.47** 0.32PC1L 0.45** 0.65**

OPI, survival of total hatchery-released coho salmon fromthe Oregon Production Area (OPA); OCN, the log--transformed returns of Oregon coastal natural (i.e. wild)salmon stocks; PC1, PC2, the ®rst two PCs based on April±June environmental data from the period of smolt release;PCIL, the ®rst PC based on January±June environmentaldata, lagged 1 year to the period of overwintering and thereturn migration. Number of cases: 33. Signi®cance levels asin Table 3.



values was 0.83 (SD � 0.14), with 99% of correlationssigni®cant. For both time series, the PCs appear to berobust predictors.

DISCUSSION

Decadal-scale coherence of the Paci®c DecadalOscillation (PDO) (Mantua et al., 1997), phyto-plankton abundance in the central North Paci®c

Figure 4. Scatterplots of marine survival in the OregonProduction Area (OPA) in relation to (a) PC 1 (produc-tivity related conditions in the southern portion of the studyarea, April±June of the year of smolt outmigration), (b) PC 2(productivity related conditions in the northern portion ofthe study area, April±June of the year of smolt outmigra-tion), and (c) PC 1 L (productivity related conditions in thestudy area, January±June of the year of the return migration).The ®lled symbols represent years when survival appearedpoor in relation to the selected PC, due to unfavourablemarine conditions in another sector or year as indicated byanother PC.

Figure 5. Scatterplots of Oregon coastal natural (OCN)recruits in relation to (a) PC 1 (productivity related conditionsin the southern portion of the study area, April±June of theyear of smolt outmigration), (b) PC 2 (productivity relatedconditions in the northern portion of the study area, April±June of the year of smolt outmigration), and (c) PC 1 L(productivity related conditions in the study area, January±June of the year of the return migration). The ®lled symbolsrepresent years when survival appeared poor in relation to theselected PC, due to unfavourable marine conditions in anothersector or year as indicated by another PC.



(Polovina et al., 1995), zooplankton biomass in theSouthern California Bight and subarctic (Brodeur andWare, 1992; Roemmich and McGowan, 1995), andsalmon production along the west coast of NorthAmerica (Beamish and Bouillon, 1993; Hare andFrancis, 1995) support the hypothesis of tight couplingbetween the meteorology, physical and biologicaloceanography, and ®sheries production over much ofthe north-east Paci®c (Francis and Hare, 1994; Garg-ett, 1997). However, the local mechanisms that mightlink salmon survival with decadal-scale shifts in theAleutian Low pressure system (i.e. the PDO) haveremained unclear (Baumann, 1998).

Several hypotheses have been put forward toexplain the observed regional variability in biologicalproductivity: changes in mixed layer depth, strati®ca-tion and consequently the vertical ¯ux of nutrientsinto near-surface waters (Roemmich and McGowan,1995; Gargett, 1997); changes in upwelling and thetransport of the California Current, which advectsnutrient-rich water from high latitudes into the rel-atively nutrient-poor southern region (Chelton et al.,1982); or variability in the quality of the wateradvected (Parrish et al., 2000).

Examining this last hypothesis ®rst, Parrish et al.(2000) show that over the period 1881±1995, therelationship between local wind forcing (southwardwind stress, an index of upwelling off San Francisco)and local SST has varied substantially, such thatperiods of relatively warm SST have occurred duringperiods of both high and low locally wind-forcedupwelling (see their Fig. 11). As a result, the rela-

tionship between these two variables is highly non-linear over this entire time period; but not, notably,over the period 1960±94. Our data also indicate nosigni®cant nonlinearities in the relationship betweenSST and upwelling over the period of our study,suggesting that coastal water mass composition andthe relation between local upwelling and water masscharacteristics did not shift signi®cantly in the regimeshift between 1960±76 and 1977±94 [ANCOVA, testingfor signi®cant interaction between SST and upwellingin Area 2 between San Francisco and Cape Mendo-cino between those periods, F1,33 � 0.74, P � 0.40)(Fig. 7)].

Our PC analysis, based on local atmospheric andoceanographic variables potentially related to nutrientdynamics and biological productivity, indicates thatthese variables have largely worked in concert in theCalifornia Current over the period since 1960. Thus, itis not a matter of choosing between, say, upwelling,mixed layer depth, wind mixing, strati®cation, SST ortransport of the California Current to determine thefactor(s) regulating productivity in the region. Rather,these variables jointly de®ne the dominant mode ofoceanographic variability in the coastal zone offwestern North America, all contributing signi®cantlyto it. Presumably these variables all contribute to the¯ux of nutrients into the euphotic zone and hence toproductivity in the California Current, although theirrelative contributions remain unclear.

This dominant mode, closely associated withnutrient availability and presumably biological produc-tivity in the California Current, is signi®cantly

Variables in regression B SE (B) P

Marine survival for coho salmon based on the Oregon Production IndexPC 1 0.71 0.33 0.04PC 2 1.12 0.37 0.004PC 1L 1.11 0.34 0.003Constant 4.41 0.33 0.000

F5,29 � 8.61, P < 0.001; R2 � 0.47

Log-transformed returns of Oregon coastal natural (OCN) cohoPC 1 0.11 0.02 0.000PC 2 0.14 0.03 0.000PC 1L 0.15 0.02 0.000Constant 2.61 0.02 0.000

F3,20 � 33.15, P < 0.001, R2 � 0.83

PC 1, 2, ®rst two PCs from April±June of the period of smolt outmigration; PC1L,the ®rst PC from January±June of the following year of the return migration. Thecriteria for entry and for variables to remain in the regression were P < 0.05 andP < 0.10, respectively; B, regression coef®cient; SE, standard error; P, signi®cancelevel.

Table 5. Results of multiple regressionanalyses for marine survival for cohosalmon based on the Oregon ProductionIndex (OPI) and (B) log-transformedreturns of Oregon coastal natural (OCN)coho. The independent variables are thePCs from analyses of coastal environ-mental conditions.



correlated on the interannual time scale with cohosalmon marine survival. Most of the variability incoho marine survival may be accounted for by con-sidering the in¯uence of this dominant mode ofoceanographic variability on the coho during both theperiod of smolt outmigration and their second year atsea. Not surprisingly, single local variables, such asupwelling and SST, that contribute signi®cantly tothis dominant mode of oceanographic variability mayalso be successfully correlated with interannual vari-ability in coho salmon survival (Nickelson, 1986;Lawson, 1997; Cole, 2000). However, any singlevariable represents the contribution of only a singlefactor or is only a single indicator of what is clearly acomplex multivariate process. It is therefore not sur-prising that the relationship with upwelling brokedown during recent periods of low upwelling (Nick-elson, 1986; Lawson, 1997). Composite indices, suchas our ®rst PCs, may prove a more robust indicator of

marine conditions conducive to productivity in theCalifornia Current.

Several authors have indicated that there may bean `optimal window' of environmental conditions thatregulates regional productivity and coho marine sur-vival (Gargett, 1997; Ryding and Skalski, 1999), ashas been observed for some clupeoid populations inupwelling systems (Cury and Roy, 1989). They pointout that the relation between, say, upwelling or windmixing and productivity may be dome-shaped, suchthat productivity is maximal at some intermediatelevel. Working with coded-wire tag (CWT) data fromWashington hatcheries, Ryding and Skalski (1999)reported a dome-shaped relation between coho marinesurvival and sea surface temperature, upwelling andthe date of the spring transition. However, thisapparently nonlinear relationship appears to be anartefact of the limited CWT time series (Fig. 8). Thesetime series begin variously in the early to mid-1970sfor California, Oregon and Washington, unlike theOPI time series, which dates back to 1960. The CWTtime series thus do not contain the years of strongestupwelling in the period which, rather than leading todecreased survival, were in fact the years of strongestmarine survival, thereby indicating a predominantlylinear relationship with marine survival (Fig. 8). Asnoted previously, the years that are apparent outliersto this relationship (1982, 1992) were years of par-ticularly poor conditions prior to the return migration(Fig. 4b,c). Over the period of our study, coho marinesurvival is adequately predicted by a linear relationship

Figure 6. Observed (solid lines with data points) andhindcast (dashed lines) time series for (a) the Oregon Pro-duction Index (% marine survival) and (b) the logarithm ofOregon coastal natural returns. The hindcasts are frommultiple linear regressions, with principal components (PC 1,PC 2, PC 1 L) indicative of ocean productivity in thesouthern and northern sectors of the region during the coho'sinitial period at sea and the overwintering/spring conditionsprior to their return migration, respectively, as independentvariables.

Figure 7. A scatterplot of sea surface temperature (SST) inArea 2 (San Francisco to Cape Mendocino) in relation toupwelling at 39°N latitude during the April±June period.Data from 1960 to 76 are plotted as solid circles and 1977±94as ´s. Temperatures are marginally warmer and upwellingmarginally lower in the latter period (ANCOVA, effect ofregime shift, F1,32 � 3.26, P � 0.08), but the slope of therelationship between upwelling and SST is not signi®cantlydifferent between periods (see text).



with the physical conditions that promote nutrientinput into near-surface waters, so long as conditionsthroughout the area of distribution and during boththe ®rst and second years at sea are incorporated intothe relationship.

Overall, our ®ndings support the hypothesis thatcoho marine survival is regulated by marine produc-tivity and food availability during their periods of earlymarine residence and prior to their return migration.However, there remain many critical gaps in ourunderstanding of the links between salmon recruit-ment and environmental processes: the impacts ofclimate variability on the zooplankton and micro-nekton that comprise the salmon prey community, onthe coho's predators, and on the distribution of cohosalmon during much of their time at sea.

ACKNOWLEDGEMENTS

We thank L. de Witt and R. Mendelssohn for assist-ance with the data extraction and compilation, and

R. Brodeur, P. Lawson, J. Mason, R. Mendelssohn,R. Parrish, F. Schwing, D. Welch and an anonymousreviewer for useful comments on an earlier draft. JAKwas supported by a National Research Council SeniorResearch Fellowship and AJH by an NRC postdoc-toral fellowship to carry out this project.

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