Relating sardine Sardinops sagax abundance to environmental indices in northern Benguela

Progress in Oceanography 59 (2003) 257–274 www.elsevier.com/locate/pocean

Relating sardine Sardinops sagax abundance to environmental indices in northern Benguela G.M. Daskalov a,∗, D.C. Boyer a, J.P. Roux b a

National Marine Information and Research Centre, P.O. Box 912, Swakopmund, Namibia b Lu¨deritz Marine Research, P.O. Box 394, Lu¨deritz, Namibia Revised 9 September 2003; accepted 29 September 2003

Abstract Most attempts to define relationships between the environment and Sardinops sagax recruitment success in the northern Benguela have focused on regional indices. This study used a large-scale perspective and proposed two new indices: one based on the sea surface temperature in the tropical Atlantic, and the other on coastal wind stress. The proposed indices were not only consistent with empirical evidence but were also based on current understanding of the processes underlying environmental variability and pelagic fish recruitment dynamics. Prior to the mid-1980s, sardine recruitment strength was positively correlated with SST and negatively with wind. Subsequently, these relationships reversed, recruitment being negatively correlated with SST and positively correlated with wind. GAM models incorporating recruitment, spawning stock biomass and environmental indices were built and compared, and two hypotheses were formulated to explain the reversal of the environment–fish relationships. The first addressed a switch between two environmental regimes: the first (prior to the mid-1980s) characterised by weak stratification and strong enrichment, and the second (late 1980s and the 1990s) characterised by frequent warm events, stronger stratification, and reduced enrichment and productivity. The second hypothesis suggested that the reversal can be attributed to changed population structure, distribution and migration behaviour of the severely overfished sardine stock resulting in a shift from the optimal spawning area in the vicinity of Walvis Bay to the a less favourable spawning habitat in the north. The present depressed state of the stock does not allow firm conclusions to be drawn about the effect of the environment on its population dynamics, nor to consider the implications for management. However, it is argued that an appreciation of how such relationships may change through time is important in further understanding and modelling oceanic and fisheries systems in the region. Crown copyright  2003 Published by Elsevier Ltd. All rights reserved.

Contents 1.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258

∗

Corresponding author. Present address: CEFAS Lowestoft Laboratory, Pakefield Rd, Lowestoft, Suffolk, NR33 0HT, UK. Tel.: +44-1502-524584; fax: +44-1502-524511. E-mail address: [email protected] (G.M. Daskalov). 0079-6611/$ - see front matter. Crown copyright  2003 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.pocean.2003.09.002

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Material and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262 3.1. Identifying environmental indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262 3.2. Models of sardine abundance and environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 4.

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267

1. Introduction The northern Benguela sardine Sardinops sagax (also called pilchard) stock supported annual catches of more than a million tons in the late 1960s, but several years of poor recruitment concurrent with these high catches resulted in the first of a series of stock collapses in the early 1970s (Boyer & Hampton, 2001). A similar sequence of events was repeated in the mid-1970s and again in the mid-1990s, leading to further collapses. By the end of the 1990s, the stock had reached such low levels that finally the fishery was closed in 2002. Owing to its historical importance, and the potential to become again a fishery of major economic and social importance, the rebuilding of the sardine stock is considered one of the top priorities for Namibian fisheries authorities. Boyer, Boyer, Fossen and Kreiner (2001) proposed that even the limited catches permitted during the mid- and late-1990s were too high for sustaining the stock given the poor recruitment of those years, while catches could potentially have been higher during periods of greater recruitment (Jacobson & MacCall, 1995). Understanding the processes controlling recruitment would enable the authorities to develop a management strategy based on future stock levels, rather than recent past ones, so avoiding overfishing during periods of poor recruitment and consequently enhancing the chances of a recovery of the stock. The oceanographic conditions in the Benguela system are influenced by basin-scale ocean–atmosphere processes (Jury & Courtney, 1995; Shannon & Agenbag, 1990; Shannon, Lutjeharms, & Nelson, 1990). The South Atlantic anticyclone high-pressure cell, together with the equatorial low-pressure belt and the continental low-pressure cell over southern Africa control the south-east trade wind regime along the west coast of southern Africa. This trade wind is the driving force for the Benguela Current, which feeds into the South Equatorial Current as part of the basin-scale wind-driven circulation. The meridional component of the trade wind is responsible for offshore Ekman transport in near-surface shelf water near the eastern ocean boundary, and causes coastal upwelling along the South African and Namibian coasts (Lutjeharms & Meeuwis, 1987). Warm tropical surface waters are carried southwards in the Angola Current where they meet cold upwelled water in the Angola-Benguela frontal system, located around 15–17°S (Meeuwis & Lutjeharms, 1990). On an interannual time-scale, this frontal system migrates southwards to reach relatively high latitudes well into Namibia. Southward “outbreaks” of equatorial water can block coastal upwelling within affected regions; such warm events have been referred to as “Benguela Nin˜ os” (Shannon, Boyd, Brundrit, & Taunton-Clark, 1986). The well-known Benguela Nin˜ o years of 1963, 1974, 1984 (Shannon et al., 1986) and 1995 (Gammelsrød, Bartholomae, Boyer, Filipe, & O’Toole, 1998) are characterised by a reduction of upwelling in the northern Benguela and by positive anomalies in rainfall over the Namibian hinterland and St Helena Island. Comparative studies of small pelagic clupeoid fish have frequently focused on the three main factors thought to be responsible for their high productivity. These factors, known as the ocean triad (Bakun, 1996) are enrichment with nutrients resulting in high primary and secondary productivity, retention of larvae in areas favourable for fish growth and survival, and concentration of fish larvae and their food in a stable environment. The components of the “triad” are strongly dependent upon physical processes in the sea

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associated with oceanographic structures such as gyres, fronts, eddies and coastal upwelling cells; enrichment is mainly caused by divergence and upwelling, while concentration and retention are a result of convergence or stratification. The intense upwelling in the Lu¨ deritz area constitutes the major source of nutrients to the central region of the northern Benguela (Bakun, 1993; Lutjeharms & Meeuwis, 1987; Shannon, 1985) and thus satisfies the first component of the “triad” (Fig. 1). Downstream of this region of upwelling is a declining gradient in upwelling intensity and wind-induced mixing at about 25°S (Agenbag & Shannon, 1988; Shannon, 1985), north of which lies a partially sheltered extended bight centred on Walvis Bay. This area provides quiescent conditions which permit stratification and hence concentration of food, and is a preferred habitat for sardine spawning (Bakun, 1993). The third component of the “triad”, retention, is at least partially fulfilled by the regulation by sardine larvae of their depth in the water column such that they remain below the offshore-flowing Ekman layer, hence ensuring that they are retained nearshore (Stenevik, Sundby, & Cloete, 2001). Other upwelling centres to the north (Lutjeharms & Meeuwis, 1987; Shannon, 1985) are possibly less important for sardine reproduction, although another spawning area is located between 19°S and 21°S (King, 1977; Le Clus, 1990; O’Toole, 1977; Fig. 1). Previous studies explored the relationship between sardine abundance and environmental conditions in the Benguela system, but these have resulted in somewhat contradictory findings. Shannon, Crawford, Brundrit, and Underhill (1988) reported a negative correlation between sardine abundance and sea surface

Fig. 1.

Map of the northern Benguela showing sardine spawning areas and the Lu¨ deritz upwelling cell.

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temperature (SST) in the northern Benguela and a positive correlation in the southern Benguela. In contrast, Cole (1997) detected a positive correlation between sardine abundance and SST in the northern Benguela, and Le Clus (1991) reported a dome-shaped response of sardine spawning (number of eggs) to thermal stratification. In other areas, results on sardine–environment linkages have also been equivocal. For Californian sardine, Bakun (1993) reported a positive correlation between sardine and SST during the population decline and a negative correlation during the earlier period of population growth. When both periods were aggregated, Jacobsen and MacCall (1995) found a positive relation between sardine recruitment and SST. Sardine stocks of the Far East seem to be negatively related to regional SST (Kawasaki, 1991), but positively related to global long-term temperature trends (Kawasaki, 1991; Klyashtorin, 1998). Many other stocks in different locations have been investigated for relationships between abundance and environmental factors (e.g. Klyashtorin, 1998; Myers, 1998), but more questions than answers on the causes and mechanisms of the fish stock response to environmental variability have arisen (Bakun, 2001; Roy et al., 2001). Early claims about insufficient data and that long series would lead to sounder statistical models and validation of linkages failed in many cases because relationships tended to “breakdown” over time (Bakun, 2001). Knowing that the major features of sardine spawning habitat in the northern Benguela are driven by largescale ocean processes and that sardine population dynamics are dominated by low frequency variability, this study aimed to explore the large-scale environmental effects on recruitment in order to develop environmental indices for stock assessment, recruitment prediction and, eventually, for environmentally based fisheries management.

2. Material and methods The analyses focused on the relationships between sardine abundance and environmental variables in the Southeast Atlantic and northern Benguela. We used correlation and principal components (PCA) analyses to explore spatial patterns in SST, and generalised additive modelling (GAM) to build models of sardine recruitment in relation to spawning stock biomass, wind stress and SST. Estimates of sardine recruitment and spawning stock biomass (SSB) during the period 1952–1987 were based on VPA (Le Clus, Melo, & Cooper, 1988). Recruitment is the number of fish aged 0 and SSB includes fish 2 years of age and older (Thomas, 1986). VPA estimates of abundance before 1960 were considered imprecise owing to the difficulty of age determination. VPA estimates after 1960 were more reliable because of the inclusion of more catch and age data (Thomas, 1986), but after 1975 the stock abundance was very low, comprising few age classes, and the accuracy of estimates decreased. The general trends reflected by VPA were believed to track the stock dynamics, and the 1961–1983 dataset was considered reliable for this analysis. After Namibian Independence sardine abundance was assessed by hydroacoustic surveys (Boyer et al., 2001). During the period 1991–1999 the number of age 0 years fish, estimated from surveys carried out in November each year, was used as an index of recruitment in that year. An index of SSB was derived by averaging the biomass estimate from the current year March survey and the previous year November survey. Because of the different methodologies of estimating abundance (VPA vs. hydroacoustics), we analysed the periods 1961–1983 and 1991–1999 separately. Sea surface temperature (SST) data for the Atlantic Ocean from 30°N to 45°S were downloaded from the Lamont-Doherty Earth Observatory website (http://www.ingrid.ldeo.columbia.edu). The SST data came from a Reduced Space Optimal Analysis of the EXTENDED dataset (Kaplan et al., 1998). This analysis has used recent temperature patterns to enhance the meagre data available in the past. The SST data analyses and plots were performed using the CLIMLAB software (Tanco & Berri, 2000; http://www.iri.ldeo.columbia.edu).

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The meridional wind speed index was compiled from measurements collected at the Diaz Point lighthouse in Lu¨ deritz, Namibia, starting in 1960. The daily averages of the southerly components of those measurements were squared to produce a meridional wind pseudo-stress index (m2/s2). An annual index was then compiled from Julyyear n-1 to Juneyear n in order to account for the main upwelling season that peaks in December/January. The annual intensity of the wind pseudo-stress (referred to here as wind stress) was taken as an index of coastal upwelling, surface layer turbulence and offshore transport. Correlation and principal component analyses were used to explore links between annual sardine recruitment and SST over the years 1961–1999. Principal component analysis (PCA, called also empirical orthogonal functions or EOF; Lebart, Morineau, & Piron, 1995) was applied in order to reveal the dominant patterns in gridded SST data and to reduce the number of variables (SST time-series at each grid point) to a few principal components (PCs) that are independent (orthogonal) and correlated with the original variables. Derived PCs can be interpreted as empirical indices of the main properties of the investigated system. A generalised additive model (GAM; Hastie & Tibshirani, 1990) was used to explore relationships between fish recruitment, spawner biomass and environmental variables. GAMs have been previously used to fit fish abundance to environmental variables by Cury et al. (1995), Daskalov (1999), and Jacobson and MacCall (1995). The GAM general equation is given by:

冘 p

g(m) ⫽ a ⫹

fj(Xj)

j⫽1

In this equation g(.) is the link function, m = E(Y) is the expectation of the response, and a + Σfj(Xj) is a function called an additive predictor. As in generalised linear models (McGullagh & Nelder, 1989), different families of models are allowed for defining of the response distribution and link function. We used a lognormal distribution, which has also been recommended in other studies (Hilborn & Walters, 1992; Myers, Bridson, & Barrowman, 1995), and regressed log recruitment to the predictor variables. The originality of a GAM is that it provides a flexible way to define the predictor function fj(.), which is analogous to regression coefficients in a linear model. The terms fj(.) can be modelled non-parametrically or parametrically, or both forms can be combined into a semi-parametric model. Non-parametric terms are fitted using scatterplot smoothers. We used the weighted local regression smoother called loess (Cleveland, Grosse, & Shyu, 1992). The degree of smoothness of the loess term depends on two parameters: the neighbourhood span (e.g. 0.5 or 0.75 of the total number of observations) and the degree (linear or quadratic loess) of the weighted regression fitted locally in the neighbourhood of each data point. Smaller neighbourhoods and quadratic loess give more flexible regression lines but they are sensitive to non-systematic data variation, whereas larger neighbourhoods and linear loess give smoother regressions but more bias. Linear loess and span 0.75 was used in this study. Parametric terms can be defined as linear coefficients, polynomials of n-degree or piecewise polynomials. Linear coefficients are not shown and loess is referred to as lo(.) in GAM formulas and in Figures, following the S-plus notation. In order to reduce the possible influence of outliers, a robust estimation was applied (Venables & Ripley, 1996). GAM fits are illustrated using partial regression graphs showing the shape of the estimated relationship between the response and each predictor together with its approximate 95% pointwise confidence intervals and the partial residuals around the prediction line. The partial regression representation depicts the incremental predictive effect of each single predictor in the multiple regression model, with the effects of other predictors held constant. The figures are adjusted to the scale of the partial residuals (centred around zero). Analysis of deviance was applied to compare models (Chambers & Hastie, 1992). Adding terms can improve the fit, but also “consumes” degrees of freedom. The Akaike information criterion (AIC; Akaike,

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1973) was used to compare non-nested models. The form of AIC implemented in S-plus (Chambers & Hastie, 1992) is given by: AIC ⫽ Deviance ⫹ 2dff where df are the effective degrees of freedom used in the fit (analogue to number of parameters in linear models) and f is the dispersion parameter. AIC penalises the use of additional degrees of freedom for increasing the goodness of fit. Models are compared according to their AIC values: a better model has a lower AIC. For the Gaussian family, f is the variance and its estimate is given by dividing the deviance by the number of effective degrees of freedom of the fit. When comparing non-nested models, it is important to hold f constant (Venables & Ripley, 1996). The estimate of f used is that of the largest model (the one with the most effective degrees of freedom). There are many methodological problems related to models of fish abundance and environmental predictors such as serial autocorrelation, dependence of adult biomass on recruitment, difficulties in validation of models on different datasets and over time (Myers, 1998; Pyper & Peterman, 1998; Walters, 1985). These problems tend to violate the basic assumptions behind regression models, inflate statistical inference and introduce bias into the models. They therefore need to be carefully accounted for in cases of building predictive models or testing statistical hypotheses. However, in this study we try to explore possible relationships between abundance and environment in an informal and flexible way.

3. Results 3.1. Identifying environmental indices The spatial distribution of the correlations between log-transformed sardine recruitment and gridded SST in the Atlantic for the periods 1961–1983 and 1991–1999 is displayed in Fig. 2. In order to downweight the effect of fishing the linear trend was removed from the log-transformed recruitment series and the detrended series was further explored for correlation with SST. During the period 1961–1983, the highest positive correlations were in the equatorial and tropical Atlantic, with r-values from 0.15–0.3 to 0.6 in the Gulf of Guinea and down to Angola. Spatial patterns of correlation in 1991–1999 were similar, but with an inverse sign: negative correlations were found in the equatorial and tropical Atlantic (Fig. 2b). In this period, the area of high negative correlation spread down to the sardine reproduction area off Namibia, but most of the correlated area was in the western tropical Atlantic that indicates the likely origin of the thermal influence. PCA was applied over a box 2.5°W–12.5°E, 2.5°S–12.5°S in order to extract a surrogate SST based on the gridded data. The first eigenvector explained 77% of the variance and the first principal component (PC1, Fig. 3d) captured the main aspects of the SST data. A cyclic ~10 year pattern can be observed in sardine recruitment and biomass (Fig. 3a,b). The timeseries was dominated by a pronounced downward trend describing the more than 10 times reduction in recruitment from the 1960s to the 1990s. The wind stress and SST-PC1 (Fig. 3c,d) series were also marked by a quasi 10 year periodic pattern. Cycles dominating wind stress and PC1 series were inversely correlated, as was the longer-term (inter-decadal) pattern which was characterised by an increase to about 1980 for wind speed and a decrease in PC1. An increase in SST after 1980–1983 can be recognised in PC1. Cooling during 1990–1994 was dominated by the very low anomaly in 1992 (~2 °C below the average for 1984–1999).

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Fig. 2. Spatial correlations between log-transformed sardine recruitment and gridded SST for (a) 1961–1983, (b) 1991–1999. Solid contours/dashed contours represent positive/negative correlations and areas with absolute correlation values higher than 0.4 are shaded.

3.2. Models of sardine abundance and environment The bivariate GAM models developed to explore relationships between sardine recruitment, SSB and environmental variables are shown in Fig. 4. The low number of observations in the period 1991–1999 caused an unstable behaviour of the smooth line that in multi-variate models would be constrained using parametric terms (Fig. 4b). The series used further as predictor variables (SSB, wind stress, PC1) were in some cases correlated between them (Fig. 4).

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Fig. 3. Time-series data used in the GAM analysis (line) smoothed by loess (bold line): (a) Recruitment (numbers × 10⫺9), (b) Spawner biomass (tons × 10⫺6), (c) Wind stress (m2 s⫺2), (d) First principal component (PC1) from gridded SST in central-southeastern Atlantic.

Fitted models of log recruitment as a function of SSB and environmental variables are presented in Table 1 and Fig. 5. SSB was included in all models in order to account for the stock-recruit effect. In Fig. 3, pronounced downward trends in both recruitment and spawner stock are evident. One of the main causes of the decrease of the biomass was heavy fishing (Boyer et al., 2001). Therefore, the trend in recruitment can be interpreted as a result of parent stock overfishing (so-called recruitment-overfishing). Removing the effect of fishing is crucial when analysing fish–environment linkages because the sharp trend in overfished stocks can generate spurious correlations with environmental variables. The best relationship between recruitment and SSB between 1961 and 1983 was fitted by the log–log Cushing model (Cushing, 1973; O’Brien, 1999; Figs. 4a and 5a,b). Other parametric relationships such as those of Ricker and Beverton & Holt (O’Brien, 1999) were tried, but not retained, because the fit was

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Fig. 4. Bivariate GAMs. Plots are scaled to residuals: solid line is the fitted model, dashed lines are 95% confidence intervals, scatters are residuals. (a) 1961–1983, (b) 1991–1999.

worse and they consumed more degrees of freedom as a result of the additional parameters. Adding environmental variables clearly improved the model fit (Table 1). The relationship with wind stress was negative and that with PC1 was positive (Figs. 4a and 5a,b). In models with both wind stress and PC1, the relationship with PC1 was inflated because of the co-linearity between the two environmental variables (Fig. 5a,b), but bivariate correlation between log recruitment and PC1 was quite strong, as seen from Fig. 4a. In order to account for the interactions between predictor variables they were fitted simultaneously using surface loess (Table 1, Fig. 6b,d). In Table 1, the goodness of the fit of models is evaluated accounting for degrees of freedom used.

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Table 1 Analysis of deviance of the fitted models. Intercepts and linear coefficients are omitted in accordance to the S-plus notation Models

Residual df

Deviance

AIC

r2

a. 1961–1983, n = 23, null deviance = 57.26 Response: log (recruits) Terms 1. log(SSB) 2. Wind stress 3. PC1 4. log(SSB) + wind stress 5. log(SSB) + PC1 6. Wind stress + PC1 7. log(SSB) + wind stress + PC1 8. lo(SSB, wind stress, span = 0.75, degree = 1) 9. lo(SSB, PC1, span = 0.75, degree = 1) 10. lo(wind stress, PC1, span = 0.75, degree = 1) 11. log(SSB) + lo(wind stress, PC1, span = 0.75, degree = 1)

21.00 21.00 21.00 20.00 20.00 20.00 19.00 17.02 16.20 16.66 15.66

14.19 16.85 32.34 9.56 11.84 16.70 9.34 7.08 10.99 11.74 8.03

16.24 19.93 35.42 13.66 15.94 20.80 14.47 14.24 18.99 19.27 16.58

0.75 0.71 0.44 0.83 0.79 0.71 0.84 0.88 0.81 0.79 0.86

b. 1991–1999, n = 9, null deviance = 20.31 Response: log (recruits) Terms 1. log(SSB) 2. Wind stress 3. bs(PC1, knots = 0, degree = 1) 4. log(SSB) + wind stress 5. log(SSB) + bs(PC1, knots = 0, degree = 1) 6. Wind stress + bs(PC1, knots = 0, degree = 1) 7. log(SSB) + wind stress + bs(PC1, knots = 0, degree = 1) 8. lo(SSB, wind stress, span = 0.75, degree = 1) 9. lo(SSB, PC1, span = 0.75, degree = 1) 10. lo(wind stress, PC1, span = 0.75, degree = 1) 11. log(SSB) + lo(wind stress, PC1, span = 0.75, degree = 1)

7.00 7.00 6.00 6.00 5.00 5.00 4.00 2.33 1.74 2.54 1.54

16.70 12.59 9.23 9.61 8.52 5.00 1.55 0.46 1.26 1.10 1.25

19.95 15.84 14.10 14.48 15.01 11.49 9.67 11.29 13.05 11.59 13.36

0.18 0.38 0.55 0.53 0.58 0.75 0.92 0.98 0.94 0.95 0.94

Regressions of log recruitment against SSB and environmental variables have better fit and lower AIC than those to either SSB or environmental variables alone. The best model in terms of AIC is model 4 (AIC = 13.66), in which log recruitment is regressed against SSB and wind stress (Table 1a, Fig. 5a). Lowest deviance (7.08) and highest r2 (0.88) was attained with model 8, which fits SSB and wind stress as a loess surface (Fig. 5b). The good fit of these models asserts the high explanatory power of wind stress. Only 9 years of data were available for the second period considered. The environmental effects were generally opposite to those observed for the period 1961–1983; such a change regarding SST was also observed for spatial correlations (Fig. 2). The effect of the wind consistently changes from negative in the period 1961–1983 to positive in the period 1991–1999. The best fit of PC1 is curvilinear using b-splines (Table 1b). The left-hand side of the curve is plateau-like and the right-hand side is decreasing (Figs. 4b and 5c,d). However, only two data points influence the left-hand side, those for the years 1992 and 1997, when SST was anomalously low and PC1 was negative (Fig. 3). For the period 1991–1999, the best model in terms of AIC is model 7 (AIC = 13.66, Fig. 5c) and the best fit is obtained using model 8 (deviance = 0.46, r 2 = 0.98, Fig. 5d). However, in the latter model as well as in the others fitted by loess surface, model terms consume most of the degrees of freedom. Such models are clearly inappropriate in data-poor cases and are only presented here to illustrate the exploratory analysis. The residuals of the best models (with the lowest AIC) are explored for violations from the model

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Fig. 5. Partial regression plots of selected best-fitting models (the lowest AIC) from Table 1, notations are as in Fig. 4. (a) 1961– 1983 model 4, (b) 1961–1983 model 8, (c) 1991–1999 model 7, (d) 1991–1999 model 8.

assumptions in Fig. 6. There is no significant linear trend in residuals plotted against the predicted response, and residuals appear to be normally distributed. Residuals from the period 1961–1983 are still auto-correlated, with a lag ~10 that indicates that the strong ~10-year periodicity present in all variables is not correctly modelled and that some variant of an auto-regressive model should probably be used in future. No autocorrelation is evident for the 1991–1999 data. Although the wind-stress appeared to be a better environmental predictor than the PC1 (Table 1) and the evidence that wind-stress and PC1 were correlated to some extent (Fig. 4a) it was important to include both physical variables in the exploratory models because they were believed to indicate different environmental processes with respect to the “ocean triad” (see Discussion). The correlation with the SST in the tropical Atlantic (PC1) was also one of the important results from this study that needed further interpretation.

4. Discussion The inverse effects of the wind and SST on sardine recruitment can be interpreted in terms of the triad hypothesis. The meridional wind stress at Lu¨ deritz was associated with upwelling intensity, surface layer turbulence and transport. Intense upwelling decreases surface water temperature. In the north, the higher SST related to southward intrusions of tropical water strengthens and deepens the thermocline and creates

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Fig. 6. Residuals from the models with the lowest AIC from Table 1 plotted against the predicted log recruitment (left), quantiles of the standard normal distribution (right) and the first-order autocorrelation function (acf) of the residuals plotted against the time lag in years (in the middle).

more stable conditions in the upper layer (Boyd, Salat, & Maso´ , 1987; Le Clus, 1990). Thus, the two envisaged environmental factors have opposite effects on stability of the surface water layers: the wind/upwelling intensifies the transport/turbulence and the incoming warm water strengthens the stratification. The warm and stratified surface layer can be beneficial for sardine reproduction by creating conditions suitable for rapid growth of both planktonic food and fish larvae and preventing advection, i.e. resulting in retention (Bakun, 1996; Le Clus, 1991). Warm water masses can also be responsible for more intensive frontal and convergence structures and in this manner concentrate food and fish larvae. However, the positive effects of the high temperature are conditional on the maintenance of a productive plankton environment, which is ensured by nutrient-rich upwelled waters (enrichment). If enrichment is lacking, then the effect of the warm surface water would be reversed, causing low productivity, increased cycling, competition and starvation of larvae, and thus lead to recruitment failure. In such conditions one may expect a negative relationship between recruitment and SST. Two main findings resulted from this study: the correlation between sardine recruitment and SST in the tropical Atlantic, implying a remote large-scale influence on fish productivity in the northern Benguela, and the shift in the environment–recruitment relationships between the period 1961–1983 and 1991–1999. An important implication of the results presented in Fig. 2 from a fisheries perspective is that the area of the highest correlation between sardine recruitment and SST is spread mainly in the tropical eastern Atlantic. Decadal variability in SST and wind fields in the tropical Atlantic has been analysed by Carton, Xianhe, Giese, and da Silva (1996) and Chang, Ji, and Li (1997). The correlations between northern Benguela sardine recruitment and gridded SST in the central Atlantic (Fig. 2) captured some general features of the SST spatial patterns—such as Tropical Atlantic dipole (Carton et al., 1996; Chang et al., 1997). We propose that an SST surrogate derived from the region with maximum correlation with sardine recruitment (PC1, Figs. 3 and 4) be used as an environmental index in recruitment models. The reasons are given below. Surface thermal conditions in the northern Benguela are dominated by intensive coastal upwelling. The SST conditions in the region are influenced by both large-scale heat fluxes from the ocean and atmosphere and upwelling intensity, while regular southward fluxes of Angola Current water lead to increased stratification and upwelling relaxation (Boyd et al., 1987; Le Clus, 1990). As these intrusions occur during the

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peak spawning period for sardine, namely January–March (Kreiner, Van der Lingen, & Fre´ on, 2001; Le Clus, 1990), such warmer years should extend spawning in space and time and could lead to greater spawning success (Le Clus, 1990, 1991). In contrast, episodic massive intrusions of warm nutrient-poor tropical waters (Benguela Nin˜ os) may cause recruitment failures (Boyer et al., 2001; Gammelsrød et al., 1998; Le Clus, 1991). We hypothesise that the origin of these effects can be related to large-scale processes originating in the tropical Atlantic and the Gulf of Guinea and affecting sardine habitat in the northern Benguela. The tropical Atlantic dipole and the underlying ~10 year oscillatory regime provide a hypothetical explanation of those processes, and modelling seems capable of successfully predicting those changes (Chang et al., 1997). Sardine recruitment is more strongly correlated with SST in the Gulf of Guinea than in the northern Benguela, probably because the former more closely indexes the underlying large-scale processes affecting sardine recruitment (especially stratification and retention). Upwelling activity and the flow of the cold Benguela Current are presumably related to other processes, such as enrichment and advection of offspring. Thus, the local SST reflects a mixture of different and possibly opposing influences on fish recruitment that makes the relationship rather complex and difficult to interpret. During 1961–1983, the wind stress at Lu¨ deritz was inversely correlated with SST in the tropical Atlantic and followed a spatial pattern similar to the recruitment–SST correlation displayed in Fig. 2a (figure of SST–wind stress spatial correlations is not shown because the pattern (with inverse sign) is very similar to Fig. 2a). In the late 1980s and 1990s, this pattern broke down and the wind stress tended to be positively correlated with the tropical Atlantic SST. The inverse correlation between wind (upwelling) and SST (stratification) during the 1960s-to-early-1980s complicated even more the interpretation of the environmental influences on recruitment success. It is possible that far-field thermal conditions have influenced the strength and frequency of the upwelling winds and upwelling intensity. It is also possible that the correlation between recruitment and remote SST—PC1 only reflects the influence of the far-field conditions (indexed by SST in the tropical Atlantic) on the local upwelling (indexed by the wind stress at Lu¨ deritz), which affects directly the recruitment success. If this is the case, then the wind stress at Lu¨ deritz may become a useful index for recruitment forecasting (as suggested in this study). If the link between processes in the tropical Atlantic and northern Benguela is confirmed in the future, the SST—PC1 could be regarded as an index of far-field influence. The question of the remote influences on local oceanographic processes is beyond the scope of this study, but may provide clues for understanding the relationships between the environment and fish stocks and needs to be further investigated. A second important result was the shift of the relationship between sardine recruitment and SST from positive over the period 1961–1983 to negative from 1991 to 1999. A consistent change was also observed in the relationship between sardine recruitment and wind speed, which switched from negative in the period 1961–1983 to positive in the 1991–1999. Most of the studies in the Benguela and worldwide suggested that stratification and increased SST seem ˜ iquen, 2003; Cole, to favour sardine recruitment success (e.g. Bakun, 1993; Chavez, Ryan, Lluch-Cota, & N 1997; Jacobson & MacCall, 1995; Le Clus, 1991). Le Clus (1990) described the spatial range of sardine spawning to be restrained in the north part of the Namibian shelf during the colder years, and to extend to the south during warmer years. Bakun (1993, 1996) described the preferred sardine spawning habitat as the coastal area near Walvis Bay, which is especially suitable because of the local minimum of the offshore Ekman transport and weak cyclonic gyre contributing for the retention of larvae. There are several possible interpretations of the shift in the relationships between sardine recruitment and environment after 1990. The change in fish–environmental relationships may be spurious and due to change in the assessment methodology from VPA to hydroacoustic surveys during the 1990s. However, assuming the biomass estimates are reliable for both 1961–1983 and 1991–1999, then the reverse in fish– environment relationships can be related to some large-scale environmental regime shift. The possible signature of such an alteration was the stepwise increase of the SST in the northern Benguela after 1983

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(Fig. 7). The wind stress also displayed a pronounced decrease after 1988 (Fig. 3c). If these trends were related to a relaxation of the upwelling and deepening of the thermocline, leading to reduced enrichment and productivity, then the relationship between SST and sardine recruitment could be reversed, and the higher SST (related to stronger stratification) would be responsible for the lesser abundance of recruits. Respectively, the stronger wind would have a favourable effect on enrichment that is in agreement with the positive relationship between recruitment and wind during 1991–1999. Le Clus (1991) reported three types of large-scale environmental effects on the distribution and abundance of the sardine spawning: cold, warm and Benguela Nin˜ o regimes, between which only the warm regime was beneficial for extended spawning. It is possible that during the 1990s the system has switched permanently to a state similar to the Benguela Nin˜ o, characterised by weak enrichment. The warm events were thought to have large-scale effects on the ocean–atmosphere systems in both north and south tropical Atlantic (Carton & Huang, 1994). Their influences affected surface pressure and wind fields and were related to the dipole mode and decadal temporal variability in SST (Carton et al., 1996; Chang et al., 1997). The warm events had pronounced influence on the southern African climate and the Benguela system in particular, where the strongest events were referred as Benguela Nin˜ os (Jury, 1996; Jury & Courtney, 1995; Shannon et al., 1986). The 1984 event has been described as particularly strong and having negative effects on the marine biota (Crawford, Siegfried, Shannon, Villacastin-Herrero, & Underhill, 1990; Le Clus, 1991). The Benguela Nin˜ o of 1984 was followed by a decade of relatively higher SST and more frequent warm events (1987–1988, 1994– 1995) characterised by various physical and biological effects (Boyer & Hampton, 2001; Boyer et al., 2001; Gammelsrød et al., 1998). The increase in SST after 1983 displayed in Fig. 7 is confirmed by other data series (e.g. Carton et al., 1996; Cole, 1997; Jury, 1996). The breakdown of the spatial pattern of correlation between SST and wind stress at Lu¨ deritz, mentioned earlier, may also indicate some major rearrangement of the ocean–atmosphere system, taking place during the 1980–1990s. Thus, the inverse relationships between recruitment and environment can be explained as responses of two different environmental regimes: the first (prior to the mid-1980s) characterised by weak stratification and strong enrichment, and the second (late 1980s and the 1990s) characterised by frequent warm events, stronger stratification and reduced enrichment and productivity. Finally, we propose that more studies be conducted on large-scale ocean–atmosphere influences in the northern Benguela system and their linkages with ecology, which may further elucidate the causality behind long-term changes in fish stocks. Another hypothesis suggests that the change in relationships between recruitment and environmental indices can be attributed to population and distribution changes in the sardine stock. Historically, the main areas of sardine spawning were in the vicinity of Walvis Bay (23°S) in spring

Fig. 7. Time-series of northern Benguela SST anomalies averaged over 5°E–15°E, 20°S–30°S (solid line) and air temperature at Lu¨ deritz (dashed line) smoothed by loess (bold lines).

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for older fish, and farther north in the Dune Point region (19–21°S) in summer for younger fish (Crawford, Shannon, & Pollock, 1987; King, 1977; Le Clus, 1990; O’Toole, 1977; Fig. 1). The exact cause of such a distribution is not known, but Crawford (1981) suggested for the South African sardine that older fish would be more likely to spawn near centres of strong upwelling than younger ones. Spawning around Walvis Bay apparently declined after the first collapse of the stock and the resulting reduced number of age classes in the population in the early 1970s (Crawford et al., 1987). Recent ichthyoplankton surveys along the Namibian coast between 24°S and 17.25°S in 2000 and 2001 found few sardine eggs or larvae, but these were concentrated around Palgrave Point (20.5°S); none were recorded in the central area off Walvis Bay (Stenevik et al., 2001). This is consistent with the hypothesis that the southern spawning area has declined in importance or possibly even ceased to exist. Consequently, in recent years, the stock has consisted of few age classes of young fish, usually dominated by a single cohort, whose natal area is believed to be in the northern part of the system. The main biological characteristics of the population have changed since the collapse in the late 1970s, particularly in terms of decreased longevity and the number of age classes (Fossen, Boyer, & Plarre, 2001; Thomas, 1986). That led to reduced reproductive output of the population, which is related to the biomass of the mature stock, and the fact that large female sardine make disproportionally greater contributions to reproductive success by spawning larger batches of eggs more frequently than small females (Beckley & Van der Lingen, 1999). Another consequence of the reduced age in the stock was probably the changed sex ratio of females: males from 1.8 in the 1950s and 1960s to about 1 in the 1970s and 1980s (Thomas, 1986) owing to growth-overfishing and the compensatory increase of the portion of the fast-growing males that would have further reduced the reproductive capacity of the stock. Additionally, the spawning stock biomass declined by several orders of magnitude then, which must have had consequences on the reproductive output and resilience of the stock. The most trivial explanation of the distribution change is based on evidence of disturbed size/age structure of the stock attributable to growth-overfishing: the elimination of the adult fishable part of the stock and compensatory increase of the portion of the younger fish led to a decrease in the number of age groups from 10–12 in the 1950s and 1960s to 5 or fewer in the 1980s and 1990s (Fossen et al., 2001; Le Clus et al., 1988; Thomas, 1986). Thus, during the 1990s the stock consisted of few age groups of young fish, usually dominated by a single cohort, whose natal area is believed to be in the northern part of the system (Boyer et al., 2001; Fossen et al., 2001). Fishing effort was concentrated close to Walvis Bay, the main fishing harbour in Namibia. Therefore, the older fish that traditionally spawned in this area, were believed to have been subjected to a higher fishing mortality than younger fish, not only due to the longer period that they were subjected to fishing, but also because they occurred in the area of the highest fishing pressure. Bakun (2001) proposed a more complex evolutionary mechanism in order to explain the shift in the sardine spawning distribution. He suggested a selective change in migratory behaviour in response to the heavy fishing pressure, as a result of which sardine is believed to have lost its southward-migratory tendency towards the preferred spawning ground in the vicinity of Walvis Bay. Finally, the Walvis Bay area might be avoided by spawning sardine because of the increased risk of outbreaks of hypoxic water caused by consecutive development and breakdown of strong stratification (Boyer et al., 2001; Le Clus, 1991). This risk would be even higher during warm and Benguela Nin˜ o regimes (Boyer et al., 2001; Le Clus, 1991). If we accept the premise of a northward shift in spawning location, then it could be hypothesised that enrichment was less favourable in the northern than in the southern part of the spawning habitat. If there were some general relaxation in upwelling, increase in stratification and deepening of the thermocline over the past 10 years, then the decrease in enrichment in the north would be manifested even more. In addition, the northern part of the habitat is more strongly influenced by the intrusion of warm Angolan waters and their stratification effect. A negative/positive correlation between recruitment and SST/wind could also be

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expected with regard to increased hypoxia, which developed in strongly stratified water, favoured by higher SST and low wind. In conclusion, a combination of environmentally (climate) driven changes to the sardine spawning habitat and population and distribution changes related to the overfishing and collapse of the stock, may have induced the reversal in the relationship between sardine recruitment and environmental factors, as indexed by SST and wind-stress. Studies exploring empirical relationships between fish and environmental variables were criticised as being inaccurate and often revealing “spurious” correlations that tended to fail when new data were analysed (Myers, 1998). However, in both cases of detection and failure of a given correlative relationship, the underlying causality remained obscured in uncertainty and the subject of very “loose” hypotheses. The failure of a given correlation does not necessarily mean that the established relationship between environment and fish has been “spurious”; rather, our knowledge of the underlying causality is insufficient to understand the change in a complex system. Although results from modelling the 1991–1999 data should be interpreted with caution owing to the scarce and highly variable data and the generally depressed state of the stock, they clearly exhibit a consistent change in the relationship between sardine recruitment and the environment. It is not recommended that these models be used for prediction or defining reference points, but the results are likely to be important in improving understanding of the joint environment–fish dynamics in the region.

Acknowledgements We thank Leo Nykjaer for support in running the ENVIFISH project and Helen Boyer for providing unpublished data and preparing a map of the sardine spawning area. Anja Kreiner and Chris Bartholomae (NatMIRC) and all the ENVIFISH team are acknowledged for valuable comments and discussions. The paper benefiteted from discussions in the GLOBEC SPACC/IOC working group. A. Payne, C. O’Brien, B. Roel and J. de Oliveira (all CEFAS) and three anonymous reviewers provided helpful comments. The first author was supported by a post-doctoral grant under the ENVIFISH project (EU DG XII contract ERB IC18-CT98-0329) and the UK Department of Environment, Food & Rural Affairs contract MF0316.

References Agenbag, J. J., & Shannon, L. V. (1988). A suggested physical explanation for the existence of a biological boundary at 24°30⬘S in the Benguela system. South African Journal of Marine Science, 6, 119–132. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov, & F. Csa`ki (Eds.), Second international symposium on information theory (pp. 267–281). Budapest: Akademia Kiado. Bakun, A. (1993). The California Current, Benguela Current, and Southwestern Atlantic shelf ecosystems: A comparative approach to identifying factors regulating biomass yields. In K. Sherman, L. M. Alexander, & B. D. Gold (Eds.), Large marine ecosystems. Stress, mitigation, and sustainability (pp. 199–221). Washington, DC: AAAS. Bakun, A. (1996). Patterns in the ocean: Ocean processes and marine population dynamics. University of California Sea Grant, San Diego, California, USA, in cooperation with Centro de Investigaciones Biolo´ gicas de Noroeste, La Paz, Baja California Sur, Mexico, 323 pp. Bakun, A. (2001). School-mix feedback: A different way to think about low frequency variability in large mobile fish populations. Progress in Oceanography, 49, 485–511. Beckley, L. E., & Van der Lingen, C. D. (1999). Biology, fishery and management of sardines (Sardinops sagax) in southern African waters. Marine & Freshwater Research, 50, 955–978. Boyd, A. J., Salat, J., & Maso´ , M. (1987). The seasonal intrusion of relatively saline water on the shelf of northern and central Namibia. South African Journal of Marine Science, 5, 107–121. Boyer, D. C., Boyer, H. J., Fossen, I., & Kreiner, A. (2001). Changes in abundance of the northern Benguela sardine stock during

G.M. Daskalov et al. / Progress in Oceanography 59 (2003) 257–274

273

the decade 1990 to 2000, including a discussion of the relative importance of fishing and the environment. South African Journal of Marine Science, 23, 76–84. Boyer, D. C., & Hampton, I. (2001). An overview of the living marine resources of Namibia. South African Journal of Marine Science, 23, 5–35. Carton, J. A., & Huang, B. (1994). Warm events in the tropical Atlantic. Journal of Physical Oceanography, 24(5), 888–903. Carton, J. A., Xianhe, C., Giese, B. S., & da Silva, A. M. (1996). Decadal and interannual SST variability in the tropical Atlantic ocean. Journal of Physical Oceanography, 26(7), 1165–1175. Chambers, J.M., & Hastie, T.J. (Eds.). (1992). Statistical models. Pacific Grove, CA: S. Wadsworth & Brooks/Cole Advanced Books & Software (608 pp.). Chang, P., Ji, L., & Li, H. (1997). A decadal climate variation in the tropical Atlantic ocean from thermodynamic air–sea interactions. Nature, 385, 516–518. ˜ iquen, M. C. (2003). From anchovies to sardines and back: Multidecadal change in Chavez, F. P., Ryan, J., Lluch-Cota, S. E., & N the Pacific Ocean. Science, 299, 217–221. Cleveland, W. S., Grosse, E., & Shyu, W. M. (1992). Local regression models. In J. M. Chambers, & T. J. Hastie (Eds.), Statistical models (pp. 309–376). Pacific Grove, CA: S. Wadsworth & Brooks/Cole Advanced Books & Software. Cole, J. F. T. (1997). The dynamics of the northern Benguela upwelling system and its relationship to patterns of clupeoid production. Ph.D. Thesis, University of Warwick, UK: 208 pp. (unpublished). Crawford, R. J. M. (1981). Distribution, availability and movement of pilchard Sardinops ocellata of South Africa, 1964–1976. Fisheries Bulletin South Africa, 14, 1–46. Crawford, R. J. M., Shannon, L. V., & Pollock, D. E. (1987). The Benguela ecosystem. Part IV. The major fish and invertebrate resources. Oceanography and Marine Biology. An Annual Review, 25, 353–505. Crawford, R. J. M., Siegfried, W. R., Shannon, L. V., Villacastin-Herrero, C. A., & Underhill, L. G. (1990). Environmental influences on marine biota off Southern Africa. South African Journal of Science, 86, 330–339. Cury, P., Roy, C., Mendelssohn, R., Bakun, A., Husby, D. M., & Parrish, R. H. (1995). “Moderate” is “better”: Exploring nonlinear climatic effects on the Californian northern anchovy (Engraulis mordax). In R. J. Beamish (Ed.), Climate change and northern fish populations (pp. 417–424). Canadian Special Publication of Fisheries and Aquatic Science, 121, Cushing, D. H. (1973). The dependence of recruitment on parental stock. Journal of the Fisheries Research Board of Canada, 30, 1965–1976. Daskalov, G. (1999). Relating fish recruitment to stock and physical environment in the Black Sea using generalized additive models. Fisheries Research, 41, 1–23. Fossen, I., Boyer, D. C., & Plarre, H. (2001). Changes in some key biological parameters of the northern Benguela sardine stock. South African Journal of Marine Science, 23, 111–123. Gammelsrød, T., Bartholomae, C. H., Boyer, D. C., Filipe, V. I. L., & O’Toole, M. J. (1998). Intrusion of warm surface water along the Angolan-Namibian coast in February–March 1995: The 1995 Benguela Nin˜o. South African Journal of Marine Science, 19, 41–56. Hastie, T., & Tibshirani, R. (1990). Generalised additive models. London: Chapman and Hall (325 pp.). Hilborn, R., & Walters, C. J. (1992). Quantitative fisheries stock assessment. Change, dynamics and uncertainty. New York: Chapman and Hall (570 pp.). Jacobson, L. D., & MacCall, A. D. (1995). Stock-recruitment models for Pacific sardine (Sardinops sagax). Canadian Journal of Fisheries and Aquatic Sciences, 52, 566–577. Jury, M. R. (1996). South-east Atlantic warm events: Composite evolution and consequences for Southern African climate. South African Journal of Marine Science, 17, 21–28. Jury, M. R., & Courtney, S. (1995). Climatic determinants of Benguela SST variability. Continental Shelf Research, 15, 1339–1354. Kaplan, A., Cane, M., Kushnir, Y., Clement, A., Blumenthal, M., & Rajagopalan, B. (1998). Analyses of global sea surface temperature 1856–1991. Journal of Geophysical Research, 103, 18567–18589. Kawasaki, T. (1991). Long-term variability in the pelagic fish populations. In T. Kawasaki, S. Tanaka, Y. Toba, & A. Taniguchi (Eds.), Long-term variability of pelagic fish populations and their environment (pp. 47–60). Oxford: Pergamon press. King, D. P. F. (1977). Influence of temperature, dissolved oxygen and salinity on incubation and early larval development of the South West African pilchard Sardinops ocellata. Investigational Report Sea Fisheries Branch South Africa, 114, 35. Klyashtorin, L. B. (1998). Long-term climate change and main commercial fish production in the Atlantic and Pacific. Fisheries Research, 37, 115–125. Kreiner, A., Van der Lingen, C., & Fre´ on, P. (2001). A comparison of condition factor and gonadosomatic index of sardine Sardinops sagax stocks in the northern and southern Benguela upwelling ecosystem 1984–1999. South African Journal of Marine Science, 23, 123–135. Le Clus, F. (1990). Impact and implications of large-scale environmental anomalies on the special distribution of spawning of the Namibian pilchard and anchovy populations. South African Journal of Marine Science, 9, 141–159.

274

G.M. Daskalov et al. / Progress in Oceanography 59 (2003) 257–274

Le Clus, F. (1991). Hydrograpic features related to pilchard and anchovy spawning in the northern Benguela system, comparing three environmental regimes. South African Journal of Marine Science, 10, 103–125. Le Clus, F., Melo, Y. C., & Cooper, R. M. (1988). Impact of environmental perturbation during 1986 on the availability and abundance of pilchard and anchovy in the northern Benguela system. Collection of Scientific Papers of the International Commission for the South East Atlantic Fisheries, 15(2), 49–70. Lebart, L., Morineau, A., & Piron, M. (1995). Statistique exploratoire multidimensionnelle. Paris: Dunod (437 pp.). Lutjeharms, J. R. E., & Meeuwis, J. M. (1987). The extent and variability of South-East Atlantic upwelling. South African Journal of Marine Science, 5, 51–62. McGullagh, P., & Nelder, J. A. (1989). Generalised linear models. London: Chapman & Hall (511 pp.). Meeuwis, J. M., & Lutjeharms, J. R. E. (1990). Surface thermal characteristics of the Angola-Benguela front. South African Journal of Marine Science, 9, 261–281. Myers, R. A. (1998). When do environment–recruitment correlations work? Reviews in Fish Biology & Fisheries, 8, 285–305. Myers, R. A., Bridson, J., & Barrowman, N. J. (1995). Summary of world-wide spawner and recruitment data. In (p. 327). Canadian Special Publication of Fisheries and Aquatic Sciences, 2024, O’Brien, C. M. (1999). An approach to stock-recruitment modelling based upon GLMs, HGLMs, and DLMs. ICES CM 1999/T:01. O’Toole, M. J. (1977). Investigation into some important fish larvae in the South-East Atlantic. Ph.D. Thesis, University of Cape Town: 299 pp. (unpublished). Pyper, B. J., & Peterman, R. M. (1998). Comparison of methods to account for autocorrelation in correlation analyses of data. Canadian Journal of Fisheries and Aquatic Sciences, 55, 2127–2140. Roy, C., Van der Lingen, C., Weeks, S., Rouault, M., Coetzee, J., Nelson, G., & Barlow, R. (2001). The southern Benguela anchovy population reached an unpredicted record level of abundance in 2000: Another failure for fisheries oceanography? GLOBEC International Newsletter, 7(1), 9–11. Shannon, L. V. (1985). The Benguela ecosystem Part 1. Evolution of the Benguela, physical features and processes. Oceanography and Marine Biology. An Annual Review, 23, 105–182. Shannon, L. V., & Agenbag, J. J. (1990). A large-scale perspective on interannual variability in the environment in the South-East Atlantic. South African Journal of Marine Science, 9, 161–168. Shannon, L. V., Boyd, A. J., Brundrit, G. B., & Taunton-Clark, J. (1986). On the existence of an El Nin˜ o-type phenomenon in the Benguela system. Journal of Marine Research, 44(3), 495–520. Shannon, L. V., Crawford, R. J. M., Brundrit, G. B., & Underhill, L. G. (1988). Responses of fish populations in the Benguela system to environmental change. Journal du Conseil, 45, 5–12. Shannon, L. V., Lutjeharms, J. R. E., & Nelson, G. (1990). Causative mechanisms for intra-annual and interannual variability in the marine environment around Southern Africa. South African Journal of Science, 86, 356–373. Stenevik, E. K., Sundby, S., & Cloete, R. (2001). Influence of buoyancy and vertical distribution of sardine (Sardinops sagax) eggs and larvae on their transport in the northern Benguela upwelling ecosystem. South African Journal of Marine Science, 23, 85–97. Tanco, R. A., Berri, G. J. (2000). CLIMLAB 2000 version 1.1, manual. IRI, LDEO Palisades, NY, 55 pp. Thomas, R. (1986). The Namibian pilchard: The 1985 season, assessment for 1952–1985 and recommendations for 1986. Collection of Scientific Papers of the International Commission for the South East Atlantic Fisheries, 13(2), 243–269. Venables, W. N., & Ripley, B. D. (1996). Modern applied statistics with S-plus. New York: Springer-Verlag (462 pp.). Walters, C. J. (1985). Bias in estimation of function relationships from time-series data. Canadian Journal of Fisheries and Aquatic Sciences, 42, 147–149.