Impact of spring upwelling variability off southern-central Chile on common sardine (Strangomera...

Impact of spring upwelling variability off southern-centralChile on common sardine (Strangomera bentincki)recruitment

FABIAN GOMEZ,1,2*� ALDO MONTECINOS,2

SAMUEL HORMAZABAL,2� LUIS A.CUBILLOS,3 MARCO CORREA-RAMIREZ2�

AND FRANCISCO P. CHAVEZ4

1 Master of Science Program with Specialty in Fisheries,University of Concepcion, Casilla 160-C, Concepcion, Chile2 Department of Geophysics, University of Concepcion, Casilla

160-C, Concepcion, Chile3 Department of Oceanography, University of Concepcion,Casilla 160-C, Concepcion, Chile

4 Monterey Bay Aquarium Research Institute, 7700 SandholdtRd., Moss Landing, CA 95039, U.S.A.

ABSTRACT

Off southern-central Chile, the impact of springupwelling variability on common sardine (Strangomerabentincki) recruitment was examined by analyzingsatellite and coastal station winds, satellite chlorophyll,and common sardine recruitment from a stock assess-ment model. In austral spring, the intensity of wind-driven upwelling is related to sea surface temperature(SST) from the Nino 3.4 region, being weak duringwarm periods (El Nino) and strong during cold periods(La Nina). Interannual changes in both spring upwell-ing intensity and SST from the Nino 3.4 region arerelated to changes in remotely sensed chlorophyll overthe continental shelf. In turn, year-to-year changes incoastal chlorophyll are tightly coupled to common sar-dine recruitment. We propose that, in the period 1991–2004, interannual changes in the intensity of springupwelling affected the abundance and availability ofplanktonic food for common sardine, and consequentlydetermined pre-recruit survival and recruitmentstrength. However, the importance of density-depen-

dent factors on the reproductive dynamic cannot beneglected, as a negative association exists betweenspawning biomass and recruitment-per-spawningbiomass. Coastal chlorophyll, upwelling intensity, andSST anomalies from the Nino 3.4 region could poten-tially help to predict common sardine recruitmentscenarios under strong spring upwelling and El NinoSouthern Oscillation (ENSO)-related anomalies.

Key words: El Nino Southern Oscillation, recruitment,remote sensing, sardine, upwelling

INTRODUCTION

Eastern boundary upwelling systems (EBUSs) are re-gions with enhanced biological productivity (Ryther,1969) due to upwelling of nutrient-rich subsurfacewaters driven by equatorward wind stress. In the Peru-Chile Current System (PCCS), the equatorward windderived from satellite sensors has a marked seasonalcycle, with maximum values in winter (hereafter allannual seasons referred to are in the southern hemi-sphere) at 13�S, spring at 30�S, and summer at 35�S(Shaffer et al., 1999). However, northward of 30�S,the maximum equatorward wind derived from coastalmeteorological stations (i.e., wind from the near-shoreregion) is in summer, linked to the increased land–seathermal gradient triggered by a seasonal decrease incloud cover (Rutllant et al., 2003). Persistent equa-torward wind off Peru and northern Chile results inyear-round coastal upwelling. Nevertheless, offsouthern-central Chile (34�–42�S, Fig. 1), the south-ern part of the PCCS, equatorward wind dominates inspring–summer and poleward wind dominates in fall–winter, defining an alternating cycle of upwelling anddownwelling seasons (Shaffer et al., 1999). Off Chile,the annual cycle of coastal phytoplankton biomassappears closely related to upwelling seasonality, withmaximum chlorophyll concentrations occurring inspring–summer, and minimum in fall–winter (Yuraset al., 2005). Thus, in the seasonal timescale, coastalwind seems to be the main driver of the coastalphytoplankton biomass.

*Correspondence. e-mail: [email protected]�Present address: 104 CEOAS Admin. Bldg., Corvallis, OR

97331-5503, U.S.A.�Present address: Escuela de Ciencias del Mar, Universidad

Catolica de Valparaiso, Casilla 1020, Valparaiso, Chile.

Received 5 May 2011

Revised version accepted 5 May 2012

FISHERIES OCEANOGRAPHY Fish. Oceanogr.

� 2012 Blackwell Publishing Ltd. doi:10.1111/j.1365-2419.2012.00632.x 1

In EBUSs, the life cycle of pelagic fish is closelylinked to the seasonal cycle of coastal winds. Favorableupwelling winds have a fertilizing effect, enhancingplanktonic production but, at the same time, theyaffect the concentration of planktonic food throughturbulence and the retention of early life fish stages inthe nursery zones through coastal circulation patterns(Cury and Roy, 1989). Small pelagic fish utilize diversestrategies for assuring reproductive success given thevariability of the environment. For example, to avoidexcessive offshore advection and turbulent mixing,spawning takes place during seasons that favor reten-tion and in zones that are protected from strong winds(Parrish et al., 1983; Bakun, 1996). Empirical rela-tionships between environmental variability and pe-lagic fish recruitment reveal that reproductive successincreases with intermediate levels of equatorwardwind (around 5–6 m s)1), which, along with promot-ing enrichment through upwelling, also constrainsplanktonic food dispersal because of a moderate wind-induced turbulence (Cury and Roy, 1989). Offsouthern-central Chile, the life cycle of common sar-dine (Strangomera bentincki) is coupled to the seasonalcycle of coastal upwelling (Cubillos et al., 2001).Common sardine has a marked spawning period in latewinter (July–August), when the prevailing onshoretransport over the continental shelf favors larvalretention (Cubillos et al., 2001). Later, in spring,

common sardine pre-recruits (i.e., specimens youngerthan 4 months), which are characterized by their highnutritional demand, take advantage of the increasedplanktonic food abundance promoted by coastalupwelling (Cubillos et al., 2001). Consequently, thereproductive cycle of common sardine reflects theinfluence of upwelling seasonality on pelagic popula-tions from EBUSs.

Off southern-central Chile, the interannual vari-ability of the upwelling season is modulated by the ElNino-Southern Oscillation (ENSO) (Montecinos andGomez, 2010). During El Nino years, the upwellingseason starts late and ends early-to-normal, and themean upwelling intensity is weaker. On the otherhand, during La Nina years, the upwelling season startsearly-to-normal and ends late, and the mean upwellingintensity is stronger. In the California Current System,interannual changes in upwelling seasonality have astrong impact on the coastal upwelling ecosystem.These alterations include changes in biological pro-ductivity, plankton structure and reproductive successof several marine populations (e.g., Barth et al., 2007).Because southern-central Chile experiences upwellingdynamics similar to those of the California Current,we could expect that changes in upwelling seasonalityinduced by ENSO can also affect the coastal upwellingecosystem off southern-central Chile. We hereinpropose that, in spring, interannual changes in wind-driven upwelling intensity linked to ENSO variabilityhave a significant impact on food abundance forcommon sardine pre-recruits, determining the recruit-ment strength of common sardine. To show thisimpact, we carried out an integrated analysis of zonalEkman transport derived from meteorological stationsand satellite data, satellite chlorophyll-a concentra-tion, and common sardine recruitment estimated froma stock assessment model.

MATERIALS AND METHODS

Daily wind records, from 1990 to 2008, were obtainedfrom two meteorological stations located near thecoast, in Carriel Sur (CS, 36�46¢S, 73�03¢W) andPichoy (PC, 39�45¢S, 73�14¢W) airports (Fig. 1). Inaddition, weekly ERS winds, from 1991 to 2000 andwith a spatial resolution of 1� · 1�, were acquired fromthe Center for Satellite Exploration and Research(http://cersat.ifremer.fr/data), and daily QuikSCATwinds (QSCAT), from the period 1999 to 2008 andwith a spatial resolution of 0.25� · 0.25�, wereacquired from the Jet Propulsion Laboratory SeaWindsProject (http://podaac.jpl.nasa.gov). Off southern-central Chile, ERS and QSCAT vary consistently in

Figure 1. Study region. CS and PC mark the location ofCarriel Sur and Pichoy airports, respectively.

2 F. Gomez et al.

� 2012 Blackwell Publishing Ltd, Fish. Oceanogr.

the period when both products are available, i.e., fromJuly 1999 to January 2001 (Croquette et al., 2007).However, differences exist between their means andbetween their variances. Therefore, the time series ofERS and QSCAT were merged using the followingprocedure: (i) a low-pass cosine Lanczos filter wasapplied to the QSCAT time series to eliminate syn-optic variability that is not present in ERS; (ii) thedaily QSCAT time series were averaged weekly andspatially merged on a 1� · 1� grid; (iii) for the periodJuly 1999 to January 2001, at each grid point (i, j), andfor both zonal and meridional wind components, themean and standard deviation of ERS (lERS, rERS) andQSCAT (lQSCAT, rQSCAT) were estimated; (iv) forthe period July 1991 to July 1999, a corrected ERSwind velocity was estimated as

Vcði; j; tÞ ¼ Vði; j; tÞ � lERSði; jÞð Þ � rQSCATði; jÞrERSði; jÞ

� �

þ lQSCATði; jÞð1Þ

where V is the original zonal or meridional ERS windcomponent, and t is time; (v) finally, the correctedERS wind series were blended with the weekly aver-aged QSCAT series from the period August 1999 toDecember 2008.

Zonal Ekman transport (ZET) was estimated as

ZET ¼ sy

qwfð2Þ

where sy is the meridional wind stress, qw is seawaterdensity (1020 kg m)3) and f is the Coriolis parameter.Meridional wind stress was estimated as

sy ¼ qa � Cd � Vj j � v ð3Þ

where qa is air density (1.2 kg m)3), Cd is drag coef-ficient (equal to 0.0015), |V| is the wind speed, and vis meridional wind component. Negative (positive)ZET values imply offshore (onshore) transport. Springaverages (September to November; hereafter all springaverages refer to this period) of ZET were estimated forCS (ZETCS), PC (ZETPC), and the blended satellitedata. The satellite spring ZET series (ZETSAT) cor-responds to the spatial ZET average in the 1� · 1� gridclosest to the coast, between 34� and 41�S. PC serieshas a large gap in the second semester of 1993 thatprecluded estimation of the spring average ZET thisyear.

Chlorophyll-a concentration data, with a spatialresolution of 9 km, corresponds to daily global areacoverage (GAC) SeaWiFS data for 1997–2008, ob-tained from the OceanColor NASA website (http://

oceancolor.gsfc.nasa.gov). No interpolation wasapplied to the satellite chlorophyll because we weremainly concerned with the analysis of chlorophyllvariability over rather large spatial (i.e., coastal re-gion off southern-central Chile) and temporal (i.e.,spring average) domains. Although some bias couldbe found in the association between satellite chlo-rophyll-a concentration and phytoplankton biomassdue to river runoff, a good agreement between in situand satellite chlorophyll-a has been found offsouthern central Chile (Stuart et al., 2004). As such,satellite chlorophyll was used as a proxy of phyto-plankton biomass. Monthly records of sea surfacetemperature (SST) anomalies for the Nino 3.4region (5�S–5�N, 170�–120�W) were obtained fromthe Climate Prediction Center website (http://www.cpc.noaa.gov).

For the period 1991–2004, annual estimates ofcommon sardine recruitment (R), spawning stockbiomass (i.e., adult fish biomass at August, S), andrecruitment per spawning biomass (RPS) wereobtained from a stock assessment model, in whichsingle fish cohorts recruiting each November can beidentified and followed on a monthly basis. The stockassessment model is a population dynamics model,which uses an exponential negative model of fishsurvival and the Baranov catch equation (Quinn andDeriso, 1999). This model predicts catch, catch atlength, abundance and biomass, by using observedcatch, catch at length and catch per unit effort, as wellas fish biomass derived from acoustic sampling. Themodel parameters (recruitment, fishing mortality) areestimated by using negative log-likelihood functionsfor the observed data and a procedure to minimize anobjective function. The stock assessment model wasimplemented in AD Model Builder (http://admb-project.org/). Details about the methodology of thisstock assessment model are described by Cubillos et al.(2002), and the model code, data and further resultsare available for the interested reader on request.Catch data were obtained from the following mainlanding ports: Talcahuano (36�43¢S), San Vicente(36�44¢S), Coronel (37�00¢S), Lota (37�05¢S), andCorral (39�50¢S). Acoustic fish biomass was derivedfrom research cruises carried out by the Instituto deFomento Pesquero, between 34� and 40�S (Castilloet al., 2005).

The association between time series was examinedthrough correlation analysis. All the time series werelinearly detrended before estimating correlation. Thesignificance of the correlation was evaluated with themodified Chelton method (Pyper and Peterman,1998), which takes into account the autocorrelation of

Spring upwelling and common sardine recruitment 3


the time series by estimating an effective number ofdegrees of freedom (N*):

1

N� ¼1

Nþ 2

N

XN=5

j¼1

ðN� jÞN

qXXðjÞqYYðjÞ ð4Þ

where N is sample size, and qXX(j) and qYY(j) are theautocorrelation of the series X and Y at lag j. Then, acritical value for the correlation between X and Y isderived from the Student t-distribution, for a signifi-cance level a and N* ) 2 degrees of freedom:

rcrit ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffit2a;N��2 � ðt2

a;N��2 þN� � 2Þq

ð5Þ

In addition, univariate linear regression modelswere fitted to describe the relationship betweenrecruitment indices (Y) and either environmentalfactors or S (X). Following the procedure described byIves and Zhu (2006) for time series data, the vari-ability of the recruitment indices was described withan autoregressive model of order one:

YðtÞ � YmðtÞ ¼ q Yðt� 1Þ � Ymðt� 1Þ½ � þ aðtÞ ð6Þ

which has a time-varying component X(t):

YmðtÞ ¼ b0 þ b1XðtÞ ð7Þwhere Y is the observed recruitment index, q is aparameter measuring temporal autocorrelation, and ais a normally distributed random variable, seriallyindependent, with mean zero and variance r2. Ym is amean or equilibrium recruitment, which is a functionof the independent variable X. Ives and Zhu (2006)have shown that this autoregressive model can bereformulated as

Y ¼ b0 þ Xb1 þ � ð8Þwhere � is an error term, normally distributed withmean zero and covariance matrix r2U:

r2U ¼ r2

1 q � � � qN�1

q 1 � � � qN�2

..

. ...

� � � ...

qN�1 qN�2 � � � 1

0BBB@

1CCCA ð9Þ

Thus, the correlation structure of the data wasexplicitly incorporated in the linear regression model.The parameters of the model were calculated by usingestimated generalized least squares (EGLS). With thismethod, q is estimated through an iterative process: (i)first, q is set to zero and the other parameters of themodel are calculated using generalized least squares;(ii) the autocorrelation between the model residualsgives an estimation of q; (iii) with this new q, theparameters of the model are reestimated; (iv) theprocess is repeated until the estimates of b0, b1 and q

converge. In addition, the 95% confidence interval ofthe linear model parameters was estimated withparametric bootstrapping (Efron and Tibshirani,1993). The EGLS procedure plus the parametricbootstrapping was performed with a MATLAB codewritten by Ives and Zhu (2006).

RESULTS

Spring ZET

Both coastal stations and satellite series reveal similarspring ZET interannual variability (Fig. 2a), althoughdifferences in their long-term mean and variance areevident. The minimum long-term mean (i.e., strongestoffshore transport condition) is observed in ZETSAT,while the lowest variance is linked to ZETCS(Fig. 2a). In all cases, the correlations between theseZET series are statistically significant at 99%(Table 1). Minimum offshore transports occur in thesprings of 1994, 1997 and 2002, when spring SSTanomalies in the Nino 3.4 (N34) region are positive(Fig. 2a,b). Also, maximum offshore transports aremainly associated with negative N34 anomalies(Fig. 2a,b). All correlations between ZET and N34 arestatistically significant (Table 1).

Spring chlorophyll

Strong negative correlations between spring chloro-phyll and spring ZETSAT are found on the conti-nental shelf region, which is mainly delimited by the200-m isobath, southward of approximately 35�30¢S(Fig. 3a). This means that higher (lower) chlorophyllconcentrations in the neritic zone are associated withstronger (weaker) offshore Ekman transport. In con-trast, in the oceanic region, negative correlationsbetween chlorophyll and ZET are almost absent(Fig. 3a). Similar results are obtained using eitherZETCS or ZETPC instead of ZETSAT (not shown). Inaddition, coastal chlorophyll south of approximately35�S is negatively correlated with N34 (Fig. 3b). Thatmeans that lower (higher) chlorophyll concentrationsare linked to positive (negative) N34 anomaliesassociated with El Nino (La Nina) events. We define aspring chlorophyll index as the mean chlorophyllconcentration in the region shoreward of the 200-misobath, between Constitucion (35�20¢S) and ChacaoChannel (41�45¢), which is the zone associated withthe highest correlations between chlorophyll and bothN34 and ZET (Fig. 3a,b). This spatial average index ofchlorophyll (CHL) shows a strong positive trend, aswell as an intense interannual variability (Fig. 2c).The lowest CHL values for this region occur in 1997,2002 and 2003, and the highest during 1998, 2007 and

4 F. Gomez et al.


2008 (Fig. 2c). The correlation of CHL with both ZETand N34 are statistically significant at 95% (Table 1).The positive trend observed in CHL is not associatedwith either a positive upwelling trend in ZET or acooling trend in N34 (Fig. 2a,c).

Common sardine recruitment

The logarithm of both recruitment and recruitment perspawning biomass series (logR and logRPS, respec-tively) reveal a similar interannual variability (Fig. 2),with minimum values in 1997 and 2002. Both indicesare significantly correlated with the spring average ser-ies, though logR has a stronger correlation than logRPS(Table 1). Higher (lower) logR and logRPS areobserved when offshore Ekman transport is stronger(weaker) and the SST anomalies in the Nino 3.4 regionare negative (positive) (Fig. 2a,b). Also, higher (lower)logR and logRPS values appear associated with higher(lower) CHL values (Fig. 2c). The strong fluctuationobserved in 1997–1998 indicates that low commonsardine recruitment can be quickly reversed the fol-lowing year if favorable environmental conditions occurduring spring. The correlation between the chlorophyllfield and logR (Fig. 3c) depicts a similar spatial pattern,but with opposite sign, to those found between chloro-phyll and both N34 and ZETSAT. As predictors in thelinear regression models, ZETSAT, CHL and N34explain a significant fraction of logR and logRPS(Table 2, Fig. 4). The 95% confidence intervals ob-tained with parametric bootstrapping indicate that forall these linear models, b0 and b1, but not q, are signif-icantly different than zero. In addition, in the linearregression model between S and logRPS, b1 is negativeand statistically different than zero (Table 2, Fig. 5a).

DISCUSSION

Spring upwelling and ENSO

Spring ZET from the meteorological stations of CS andPC shows similar interannual fluctuations to spring ZETbased on satellite data (Fig. 2a), which reveals thatspring upwelling variability can be well described byeither land or satellite wind data. Also, in agreementwith the findings of Montecinos and Gomez (2010), theinterannual spring ZET variability in the study region issignificantly related to ENSO. Indeed, weak upwellingoccurs in El Nino springs of 1994, 1997 and 2003,whereas strong upwelling occurs in La Nina springs of1995 and 1998. Changes in the intensity of the springupwelling season must be related to SST in the tropicalPacific through teleconnection patterns. Indeed, block-ing activity in the Amundsen–Bellingshausen Seas inthe Southern Ocean is particularly strong and frequentduring the spring of El Nino years, and can movemid-latitude storm tracks equatorward, favoring a con-tinuation of winter conditions through late spring insouthern-central Chile (Montecinos and Aceituno,2003).

(a)

(b)

(c)

(d)

Figure 2. Spring environmental indices and common sar-dine recruitment. (a) Zonal Ekman transport in Carriel Sur(ZETCS), Pichoy (ZETPC), and satellite-derived (ZET-SAT). (b) SST anomaly in the El Nino 3.4 region (N34). (c)Mean chlorophyll concentration shoreward of the 200-misobath, between 35� and 41�45¢S (CHL). (d) Logarithm (10base) of common sardine recruitment (logR) and logarithmof recruitment per spawning biomass (logRPS). Recruitmentis in 109 ish. RSP is in 106 fish · ton)1. ZET, N34 and CHLare average spring time series.



Coastal chlorophyll variability

The highest correlations of chlorophyll with bothZET and N34 were found on the continental shelf,southward of approximately 35�30¢S (Fig. 3a,b). Thissuggests that the ENSO modulation of spring wind-driven upwelling drives changes on coastal phyto-plankton biomass in the neritic region. It should benoted that the relationship between chlorophyll andboth ZET and N34 disappears outside the continentalshelf. This is probably due to the strong mesoscaleeddy variability occurring in the oceanic region. Infact, off southern-central Chile, mesoscale eddies playan important role in the distribution and production ofphytoplankton biomass in the oceanic region (Correa-Ramirez et al., 2007).

The relationship between chlorophyll and ENSOcould also be associated with coastal trapped waves(CTWs) remotely forced by equatorial winds (Shafferet al., 1997; Pizarro et al., 2002). Off Peru and northernChile, downwelling (upwelling) CTWs can modify thesource of water being upwelled to the upper layer bydeepening (shallowing) the nutricline. As a conse-quence, the concentration of nutrients available tophytoplankton in the photic layer decreases (increases)(Ulloa et al., 2001; Bertrand et al., 2008). Because theamplitude of these oceanic perturbations decreasespolewards, a weaker impact would be expected offsouthern-central Chile (Shaffer et al., 1997). None-theless, modeling studies based on summer wind con-ditions have shown that CTWs can also affectupwelling off southern-central Chile (Leth and Mid-dleton, 2006). Because strong downwelling (upwelling)CTWs are associated with warm (cold) ENSO condi-tions, we might expect these oceanic perturbations toreinforce the interannual signal observed in springchlorophyll, particularly during stronger El Nino (LaNina) events. Indeed, the stronger CHL–N34 correla-tion (r = )0.85) compared to CHL-ZETSAT (r =)0.74) might reflect chlorophyll sensitivity to bothatmospheric and oceanic perturbations. However, thefact that northward of 35�30¢S, chlorophyll variabilitydoes not appear to be related to N34 – as would beexpected if coastal chlorophyll were mainly forced byremote oceanic forcing – suggests that the ENSO-re-lated atmospheric teleconnections have a strongerimpact on spring chlorophyll variability than do CTWs.

Neither N34 nor ZETSAT evidence a linear trendas long as CHL, which is mainly determined by thechlorophyll values of 1997, 2007 and 2008. If thespring series are not detrended, the correlation of CHLwith both N34 and ZETSAT decreases to )0.77 and)0.58, respectively. These correlations are still statis-tically significant at 95%. The difference betweencorrelations obtained from series with and withoutlinear trend could be linked to decadal variability

Table 1. Correlation coefficients between the time series analyzed.

logR logRPS N34 CHL ZETSAT ZETPC ZETCS

ZETCS )0.71b )0.44 0.61b )0.67b 0.85a 0.66a –ZETPC )0.75a )0.65b 0.57b )0.67b 0.79a

ZETSAT )0.77a )0.59b 0.56b )0.74a

CHL 0.92a 0.92a )0.85a

N34 )0.83a )0.70b

logRPS 0.85a

Residuals 0.95a 0.70b )0.76b 0.93a )0.76a )0.73b )0.78a

Statistically significant at a 99%; b 95%. Time series were detrended before estimating correlation.

(a) (b) (c)

Figure 3. Correlation fields of chlorophyll with ZET (a),N34 (b), and logR (c). Only correlation coefficients with amagnitude >0.50 are plotted. Red (blue) color indicatespositive (negative) correlations. Continental shelf is de-picted by the 200-m isobaths. Chlorophyll, ZET and N34 areaverage spring time series.

6 F. Gomez et al.


affecting chlorophyll, not directly related to ENSO. Inany case, linear trends in all the time series analyzedwere not significant at 5% according to the nonpara-metric Mann–Kendall test (Yue et al., 2002).

Bottom-up control of common sardine recruitment

The high correlation between spring chlorophyll andboth logR and logRPS suggests that common sardinerecruitment is modulated by phytoplankton food sup-

(a)

(b)

(c)

(d)

(e)

(f)

Figure 4. Linear regression models. LogR and logRPS as a function of ZETSAT (a, d), CHL (b, e), and N34 (c, f).

Table 2. Estimated general least square parameters.

Relationship b0 b1 q r n R2

logR–ZETSAT 1.24 [0.94, 1.55] )1.185[)1.68, )0.70] 0.38 [)0.45, 0.71]] 0.19 [0.11, 0.27] 14 0.55logR–CHL 0.97 [0.56, 1.32] 0.215 [0.13, 0.31] )0.21 [)0.88, 0.48] 0.15 [0.06, 0.24] 8 0.84logR–N34 1.91 [1.84, 1.98] )0.214 [)0.30, )0.13] )0.28 [)0.76, 0.27] 0.17 [0.09, 0.23] 14 0.68logRPS–ZETSAT )1.50 [)2.04, )0.98] )1.366 [)2.21, )0.49] 0.44 [)0.41, 0.72] 0.33 [0.20, 0.47] 14 0.30logRPS–CHL )2.09 [)2.71, )1.46] 0.318 [0.17, 0.46] 0.22 [)0.83, 0.71] 0.24 [0.10, 0.37] 8 0.82logRPS–N34 )0.72 [)0.91, )0.53] )0.268 [)0.44, )0.10] 0.10 [)0.56, 0.54] 0.32 [0.19, 0.45] 14 0.48logRPS–S )0.19 [)0.52, 0.16] )0.0012 [)0.0018, )0.0006] )0.21 [)0.72, 0.28] 0.30 [0.18, 0.42] 14 0.52

Numbers in brackets are the 95% confidence intervals, n is number of observations.



ply during the pre-recruitment period (Figs 2c,d and 3c).Interestingly, in Figure 3c, the region with the highestcorrelations between chlorophyll and logR is similar tothe main spawning area of common sardine, althoughthe austral limit for the latter seems closer to 41�S(Castillo-Jordan et al., 2007; Cubillos et al., 2007).We can expect that upwelling variability there exerts astrong impact in the survival of early life stages. Nodetailed studies about common sardine diet in the pre-recruitment period exist but it is known that commonsardine is a planktivorous fish, feeding on both phyto-and zooplankton (van der Lingen et al., 2009). Zoo-plankton series were not available for the periodanalyzed, but increased zooplankton abundance can beexpected when phytoplankton increases. In a plausiblefirst approximation, coastal chlorophyll can be con-sidered an index of food abundance for common sar-dine. Thus, weak spring upwelling conditions limitfood abundance for common sardine, decreasing pre-recruit survival, whereas the opposite occurs withstrong upwelling conditions. In addition, changes inplankton community structure driven by upwellingintensity can probably change food availability forsmall pelagic fish (Rykaczewski and Checkley, 2008).In this context, further studies on common sardinediet and seasonal plankton dynamics are required toidentify the underlying mechanism affecting pre-recruit survival.

The relationship between spring ZET and both logRand logRPS (Fig. 4a) is consistent with an earlier studythat shows that high common sardine recruitment islinked to both strong upwelling and negative SSTanomalies during spring (Cubillos and Arcos, 2002).We also evaluated the impact of the environmentalconditions on the spawning period (July–August) butno significant association was found (not shown),which could indicate that, in the period 1991–2004,the recruitment of common sardine was mainly mod-ulated by the spring environmental conditions.

Although the strong association between recruit-ment and spring environmental conditions suggeststhat recruitment is strongly affected by density-inde-pendent factors, the importance of density-dependentfactors on the recruitment dynamic cannot beneglected. In fact, the significant relationship betweenS and logRPS suggests the existence of a compensatoryeffect in the stock–recruitment relationship. Therelationship between S and logRPS is in agreementwith previous findings that reveal a density-depen-dence in the per capita population growth rate vari-ability of common sardine (Pedraza-Garcia andCubillos, 2007). Interestingly, as a time series, theresiduals of the regression model between logRPS andS have a stronger correlation with spring environ-mental conditions than logRPS, and closely resemblelogR (Table 1, Fig. 5b). Because this residual timeseries is associated with the variability of logRPS oncethe influence of density-dependent factors has beenremoved, it could be suggested that logR reflects thevariability of logRPS that depends on density-inde-pendent factors.

Statistical models

The estimation of a corrected number of degrees offreedom to estimate the significance of the correlationis a common procedure to deal with autocorrelateddata. The adjustment of degrees of freedom in (Eqn 4)depends on the autocorrelation functions qXX and qYY,which are not well defined for short time series.However, the analysis performed by Pyper andPeterman (1998) showed that their modified Cheltonmethod is an effective procedure to maintain theprescribed significance level when the time series areshorter. Thus, we can be confident about the signifi-cance of the correlations obtained between environ-mental and recruitment series. The autocorrelation ofthe time series also could induce significance in linearregression analysis. For this reason, a method thatincorporates the correlation structure of the data wasused so that the parameters b0 and b1 are not biased bythe time-series nature of the data (Ives and Zhu,

(a)

(b)

Figure 5. Density-dependence. Linear regression modelsbetween S and logRPS (a); time series of logR and theresiduals of the S-logRPS regression (b).

8 F. Gomez et al.


2006). The fact that q, the parameter linked to thecorrelation of the error term, is not significantly dif-ferent than zero implies that there is not a strongimpact of the autocorrelation, of either logR or log-RPS, on the estimation of b0 and b1.

The relationship between recruitment and springupwelling-related conditions (ZET, CHL) for the period1991–2004 may not necessarily hold for other periods, asmany factors could interact to determine recruitmentvariability. In fact, most published relationships be-tween recruitment and environmental factors havebeen shown to fail after retesting (Myers, 1998). How-ever, the estimated relationships can be useful toestablish general patterns associated with recruitmentvariability, giving insights about the factors controllingcommon sardine abundance. In this study, linear cor-relations and linear regression models were used to testthe recruitment–environment relationships, eventhough a non-linear behavior could be suggested insome cases. For example, logR seems to converge at highCHL level and strong offshore transport (although thelatter is mainly supported by the extreme ZET conditionof 1998) (Fig. 4). This non-linear behavior may be ex-pected, as strong upwelling increases pre-recruit lossesdue to offshore advection, reduces food concentrationby increasing turbulence, and eventually exposes pre-recruits to temperatures below their optimal range.Another aspect to consider is that extreme ENSOconditions are the main determinants of the relation-ship between recruitment and N34, but under neutral orslightly warm ENSO conditions (N34 betweenapproximately 0� and +1�C) the recruitment seemsmore variable (Fig. 4). Longer time series will be nec-essary to obtain statistically robust relationships thatenable the uncertainty about common sardine recruit-ment variability to be reduced.

Concluding remarks

The intensity of coastal upwelling off southern-centralChile experiences high interannual variability which ismodulated by ENSO. The impact of ENSO in theupwelling system is mainly related to atmosphericteleconnections (Montecinos and Gomez, 2010),though oceanic perturbations linked to CTWs couldreinforce the atmospheric signal. Wind-drivenupwelling and N34 are significantly correlated withchlorophyll, logR and logRPS: weak (strong) upwell-ing results in low (high) coastal chlorophyll, low(high) recruitment, and low (high) recruit survivalrate. We suggest that spring coastal chlorophyll is agood proxy for the abundance of common sardine food,and changes in this quantity significantly affect thesurvival of common sardine pre-recruits, determining

recruitment strength in late spring. Thus, the recruit-ment strength of common sardine is seen as aresult of integrated environmental conditions over a3-month period. We do not evaluate other sources ofenvironmental variability occurring at shorter timescales, such as intraseasonal and synoptic scales, whichcould potentially affect common sardine recruitment.However, the strength of the relationship betweenZET, CHL and recruitment is strong enough to pro-pose a spring modulation of common sardine recruit-ment in the period 1991–2004. Because commonsardine abundance is mostly determined by its annualrecruitment pulse (Cubillos et al., 2002), the rela-tionship between chlorophyll and recruitment suggestsa bottom-up control of common sardine abundance.Nonetheless, the influence of density-dependent fac-tors on the recruitment dynamic must be recognized, aslogRPS appears negatively correlated with thespawning biomass. Common sardine sustains one ofthe most important fisheries in the southern PCCS,with annual landings of nearly 300 000 tons over thelast 15 yr. Knowledge of the factors driving fluctua-tions in common sardine abundance is important forfishery management. The relationships observedbetween common sardine recruitment and springenvironmental indices could help to anticipaterecruitment scenarios associated with intense upwell-ing anomalies or strong El Nino ⁄ La Nina events.However, further studies will be necessary to betterunderstand the underlying mechanisms behind com-mon sardine recruitment variability.

ACKNOWLEDGEMENTS

We thank Vincent Combes, Brie Lindsey, CarolinaParada and two anonymous reviewers of this manu-script for their helpful comments and suggestions. F.G.is very grateful for the support of CONICYT-ChileScholarship and the Direccion de Postgrado, Uni-versidad de Concepcion. This work was supported byCONICYT-Chile through grant PBCT-Anillo ACT-19, FONDECYT 1070504, and by the University ofConcepcion through grant DIUC-205.011.041-1.0.F.P.C was supported by NASA and the David andLucile Packard Foundation. Wind data from CarrielSur and Pichoy was kindly provided by the DireccionMeteorologica de Chile. Landing data was kindlyprovided by the Servicio Nacional de Pesca.

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