Fish and zooplankton interaction in the Central Baltic Sea

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1 ICES Journal of Marine Science, 58: doi: /jmsc , available online at on Fish and zooplankton interaction in the Central Baltic Sea G. Kornilovs, L. Sidrevics, and J. W. Dippner Kornilovs, G., Sidrevics, L., and Dippner, J. W Fish and zooplankton interaction in the Central Baltic Sea. ICES Journal of Marine Science, 58: The pelagic ecosystem of the Baltic Sea has considerably changed since the beginning of the 1980s. The stock size of cod as the top predator in the Central Baltic Sea has significantly decreased and is at a very low level in the 1990s, with the result that a cod-dominated system has changed to a clupeid(sprat)-dominated system. It has been stated that the zooplankton community has also changed in the Baltic Sea and that this phenomenon is mainly explained by changes in the hydrological environment. The long-term zooplankton data collected since 1960 by the Latvian Fisheries Research Institute allowed analyses of the influence of pelagic fishes, the main predators of the zooplankton, on interannual variability of biomass, stock structure and vertical migration of zooplankton. Using a multivariate regression model, two statistically significant and biologically plausible relationships between total stock biomass of sprat and biomass of Pseudocalanus elongatus in spring and biomass of Cladocera in summer were found. Linear correlation analyses indicate that increasing sprat stock size and strong year classes significantly influence the stock structure and vertical migration of Acartia spp. and Temora longicornis, the main food items of sprat. The fast disappearance of Cladocera in autumn could be caused by predation International Council for the Exploration of the Sea Key words: Baltic Sea, zooplankton, sprat, herring, predator-prey interactions. Received 5 August 1999; accepted 6 February G. Kornilovs and L. Sidrevics, Latvian Fisheries Research Institute, 8 Daugavgrivas Street, LV-1007 Riga, Latvia, J. W. Dippner, Institut für Ostseeforschung Warnemünde, Seestrasse 15, D Rostock, FR Germany. Correspondence to J. W. Dippner: tel: ; fax: ; dippner@io-warnemuende.de Introduction The pelagic ecosystem of the Central Baltic Sea has considerably changed since the beginning of the 1980s. Due to a reduction in the major inflow of saline water from the North Sea the salinity of the Baltic Sea has gradually decreased (Matthäus and Schinke, 1994). The decrease of salinity has unfavourably influenced the reproduction of cod which, together with high fishing pressure, has resulted in a decrease of cod stocks to low levels in the 1990s (Plikshs et al., 1993). The predation mortality of pelagic fishes has considerably decreased (ICES, 1998). However, the reactions of the two main pelagic species, sprat Sprattus sprattus balticus and herring Clupea harengus membras were different. Herring stock size is gradually diminishing, whilst the size of the sprat stock increased to a record level in 1994 (ICES, 1999). The main food item of pelagic fishes is zooplankton which has also been influenced by the changes of salinity that are thought to have caused a decrease of large zooplankton in the Gulf of Finland (Lumberg and Ojaveer, 1991) and in the Baltic proper (Flinkman et al., 1998), and a decrease of the biomass ratio between copepods and cladocerans in the northern Baltic Sea (Vuorinen et al., 1998). Since the mid-1980s the mean weight-at-age of Baltic herring has considerably decreased (Naglis and Sidrevics, 1993). It was considered that the main causal factor was the decrease of predation by cod which resulted in a higher survival of smaller herring (Sparholt, 1994). In the Gulf of Riga, however, where cod is a rare visitor, the decrease in mean weight-at-age was connected with changes in the zooplankton community and with the increase of the stock size of the local Gulf herring (Kornilovs et al., 1992). Although the cod stock biomass has been small since the end of the 1980s, the decrease in the mean weight-at-age of herring continued and, since 1994, a similar phenomenon has been observed for sprat. The decrease in mean weight-at-age of herring and sprat indicates that there is a limitation of food for pelagic fishes in the Baltic Sea. Investigations into the feeding of herring and sprat in the Central Baltic Sea revealed that /01/ $35.00/ International Council for the Exploration of the Sea

2 580 G. Kornilovs et al N Gotland Liepaja E Figure 1. Location of the zooplankton sampling stations in the central part of the Baltic Sea. Table 1. Number of stations covered per season for mesozooplankton in the Central Baltic Sea and the years with missing seasonal surveys in Spring Summer Autumn Number of samples Missing years 1968, 1971, 1979, , 1969, 1973, 1974, 1992, , 1974, 1991, 1992 the food spectrum of herring has significantly changed since the mid-1980s. The share of nectobenthos (mainly mysids Mysidacea) and Pseudocalanus elongatus has considerably diminished in the herring diet and the food of herring has become similar to that of sprat (Davidyuka, 1996). This means that food competition between species has increased, as well as the total predation pressure on certain zooplankton species which are the main food items for herring and sprat. In the 1990s the most important food items of herring and sprat were the copopods Temora longicornis, Acartia spp. and the cladocera Bosmina longispina maritima. Although there are several publications examining the impact of pelagic fish predation on zooplankton communities (Hansson et al., 1990; Flinkman et al., 1992), no evidence for an interannual relationship between

3 Fish and zooplankton interaction in the Central Baltic Sea 581 Table 2. CCA correlation coefficients between zooplankton (predictand) in different seasons (SE, spring and summer) and total stock biomass (TSB) of pelagic fishes (predictor), 99% and 95% confidence level (CV), correlation of cross validation (CC), model skill and the time lag in years. In general, the CCA correlation is a positive quantity by definition. A negative sign in this case marks a negative CCA pattern in the predictand. Bold numbers indicate a significance with respect to the 99% confidence level. Predictor TSB of Predictand zooplankton biomass SE CCA CV 99% (95%) CC Skill Lag 1 Sprat Acartia spp. Sp (0.66) Herring Pseudocalanus elongatus Sp 7 8(3) Sprat Pseudocalanus elongatus Sp (1) Sprat Cladocera spp. Su (2) Sprat Centropages hamatus Su (0.66) Sprat Centropages hamatus Su (0.61) Zooplankton anomaly [log 10 (mg m 3 + 1)] Time (years) 2000 Figure 2. Acartia spp. anomaly in spring predicted from the total stock biomass of sprat., model;, observation. zooplankton abundance and clupeid biomass has been identified so far in the Central Baltic Sea (Rudstam et al., 1994). In the Gulf of Riga, however, the decrease in abundance of Limnocalanus macrurus was mainly caused by an increase of stock size of the local Gulf herring (Kornilovs et al., 1992). We wanted to examine possible relationships between zooplankton abundance and the stock size of pelagic fishes in the Central Baltic Sea. Since it has been proven that herring feeds selectively on larger zooplankton (Flinkman et al., 1992), we hypothesized that sprat could have a similar feeding behaviour. Due to selective feeding of pelagic fishes, increased stock size of sprat and the changes in herring diet (increased proportion of Acartia spp. and Temora longicornis), there could be signs of changing stock structure of these copepods. It is also known that one response of zooplankton, especially adult copepods, to increasing predation is to intensify Zooplankton anomaly [log 10 (mg m 3 + 1)] Time (years) their vertical migration behaviour (Rudstam et al., 1994). Therefore, we wished to determine whether changes in vertical migration of Acartia spp. and Temora longicornis are connected to the stock size of pelagic fishes. Materials and methods 2000 Figure 3. Pseudocalanus elongatus anomaly in spring predicted from the total stock biomass of herring., model;, observation. Since 1960, the Latvian Fisheries Research Institute has performed regular seasonal investigations of zooplankton in the Central Baltic. The surveys are carried out in February, May, August and October to determine the abundance and distribution of zooplankton in winter, spring, summer, and autumn. There are nine sampling stations extending from the Latvian coast in the east to the eastern slope of the Gotland Deep in the west (Figure 1). The samples are taken with a Juday net

4 582 G. Kornilovs et al. Zooplankton anomaly [log 10 (mg m 3 + 1)] Time (years) 2000 Figure 4. Pseudocalanus elongatus anomaly in spring predicted from the total stock biomass of sprat., model;, observation. Zooplankton anomaly [log 10 (mg m 3 + 1)] Time (years) 2000 Figure 5. Cladocerans anomaly in summer predicted from the total stock biomass of sprat, with a time lag of one year., model;, observation. [diameter of the upper aperture: 37 cm; diameter of the middle section: cm (UNESCO, 1968)]. The mesh size of the filtering cone is 0.16 mm. On every station samples are taken from different water layers, mainly 0 25, 25, 100 m. The zooplankton is preserved in a 4% unbuffered solution of formaldehyde in sea water and later examined in the laboratory. The samples are diluted with water to a known volume, thoroughly mixed and a sub-sample is taken in which all specimens of zooplankton are identified and counted. The size of the sub-sample depends on the abundance of zooplankton in the sample because it is necessary to get 0 specimens in the sub-sample. The specimens are identified to species or, in some cases, to groups of species. For copepods, seven stages of development are resolved (nauplii, five copepodite stages and adults). Biomass was estimated from values on individual wet weight (Hernroth, 1985). The numbers of stations per season included in the analysis and the years when some seasonal surveys were not carried out are shown in Table 1. The analyses were not performed for samples collected in winter, when feeding of pelagic fishes is reduced (Arrhenius and Hansson, 1993). For the stock structure analysis of Acartia spp. and Temora longicornis the percentual proportions of adults, copepodites and nauplii in the whole water column in summer and autumn were calculated. Both species start spawning in spring and have five to seven reproductive cycles in the period from April to October. Due to unsynchronized maturing of individuals it could be assumed that in this period spawning takes place continuously and it is not possible to mark out a definite spawning time. At this period all stages of development are usually present in the samples. The vertical migration was expressed as the proportion of the total number of adults in the water column in the daytime samples that was taken in the upper m. The latter analysis was performed only for summer because the number of samples collected during daytime in autumn was small. The summer and autumn seasons were chosen because they correspond to the time of most intensive feeding by pelagic fishes (Hansson et al., 1990). Sidrevics (1979) has shown that vertical migration of Table 3. Correlation coefficients between sprat 0-group abundance and total stock biomass (TSB) and biomass of cladocera species in summer and autumn. All correlations are significant with respect to the 95% confidence level, bold numbers indicate a significance with respect to the 99% confidence level, and the asterisk a significance with respect to the 90% confidence level. Species Summer Autumn Sprat 0-group Sprat TSB Sprat 0-group Sprat TSB Bosmina longispina * Evadne nordmanni Podon spp Total cladocerans

5 Fish and zooplankton interaction in the Central Baltic Sea 583 Table 4. The correlation coefficients between sprat 0-group abundance and total stock biomass and stock structure and vertical distribution of adult Temora longicornis and Acartia spp. Zooplankton Sprat 0-group abundance Sprat total biomass 1 % of adult Temora in summer 0.48 (p<5) 0.44 (p<5) 2 % of adult Acartia in summer 5 (p<2) 0.72 (p<1) 3 % of adult Acartia in autumn 0.46 (p<5) 0.47 (p<5) 4 % of adult Temora at depth < m in summer 0. (p<5) 0.66 (p<1) 5 % of adult Acartia at depth < m in summer 0.41 (p<0.1) 0.41 (p<0.1) Adult Temora in the stock (%) Relative values of sprat recruitment and total biomass Figure 6. Percentage of adult Temora longicornis in the stock in summer and relative values of sprat 0-group abundance and total stock biomass., Temora;, sprat recruitment;, sprat total biomass. zooplankton in spring is not important and this may be related to low predation pressure. Relationships between stock structure and vertical migration of zooplankton and pelagic fishes were analysed using linear correlation analyses. While the zooplankton time-series starts in 1960, the fish time-series starts in The total stock biomass of herring and sprat was taken from the assessments of sprat stock in the Baltic Sea (Subdivisions 22 32) and herring stock in the Central Baltic Sea (Subdivisions 25 29, 32) (ICES, 1998). Because young-of-the-year (0-group) could play a substantial role in the predation on zooplankton (Arrhenius, 1996) we used also relative values of the abundance of 1-group herring and sprat at the beginning of the next year, derived from the same assessments (ICES, 1998). To identify interannual variability in the relationship between fish and zooplankton, a multivariate regression model was applied. A detailed description of the technique can be found in von Storch et al. (1993), von Storch (1995), and Dippner et al. (2001). The data were treated in the following way. Firstly, the data were log-transformed to avoid statistical problems in Canonical Correlation Analysis (CCA) with extreme values. Secondly, anomalies were constructed by subtracting the long-term mean value. These interannual anomalies were decomposed into Empirical Orthogonal Functions (EOFs), also known as Principal Components, to separate the signal from the noise in the observations. Thus, the major part of the interannual variability of the data sets is concentrated in a few new dimensions, the leading eigenmodes. In a further step, a CCA was performed between the leading eigenmodes of the data sets. Assuming that the fish F data are predictors and the zooplankton Z data are predictands, the CCA identifies fixed patterns in space F and Z and time

6 584 G. Kornilovs et al. Adult Acartia in the stock (%) Relative values of sprat recruitment and total biomass Figure 7. Percentage of adult Acartia in the stock in summer and relative values of sprat 0-group abundance and total stock biomass., Acartia;, sprat recruitment;, sprat total biomass. coefficients f (t) and z (t). The fixed pattern F and Z are weighted linear combinations of the dominant eigenmodes under the condition that the time coefficients are optimally correlated to each other, i.e. Kf (t),z(t)l=ρ max, with ρ being the correlation coefficient of the considered eigenmodes. F =F *f(t) represents a significant part of the variance of the fish time-series which serves as predictor and Z =Z *z(t) is the investigated time-series of zooplankton which is used as predictand. Finally, the zooplankton time-series Z =Z *z(t) were regressed from the fish data as Z reg=z *ρ*f(t). The appearance of possible time lags was also considered. The same technique, which has been called statistical downscaling in climate research (von Storch et al., 1993; von Storch, 1995) has been applied to relate interannual variability of zooplankton in the Central Baltic Sea to climate variability (Dippner et al., 2000). Since CCA correlations are optimized with respect to the used data sample, it is necessary to validate the correlation with independent data. In contrast to von Storch et al. (1993), who split the records into a fit and a validation period, we use cross-validation technique (Michaelsen, 1987) and Monte-Carlo simulations. In the cross-validation, if n time steps of data are available, n models are fitted by using (n 1) different time steps each. For each model, the nth step of the predictand is regressed from the predictor. Finally, the n estimations are compared with the observations of the predictand. From all tested combinations, the results with the relatively highest skills were selected. As skill factors, the correlation coefficient r (between the regional observations and the cross-validated estimations) and the Brier-based score β are used. The Brier-based score is defined as: β=1 σ 2 e/σ 2 o, where σ 2 e and σ 2 o are the variances of the error (i.e. observation minus model) and observations. β=1 means that model and observation are identical, β=0 that the error of the model has the same size as the variance of the observations (Livezey, 1995). The significance level of selected results was calculated with the Monte-Carlo technique. One thousand series of random numbers with the same statistical properties (mean, standard deviation, autocorrelation) as the EOF coefficients of the predictands were generated. Additionally, linear correlation analyses for relationships between fish and zooplankton were performed using log-transformed values of zooplankton and pelagic fish biomass. Results Various combinations with different lags between biomass and year-class strength of herring and sprat and the zooplankton data for spring, summer and autumn were carried out. Several statistically significant

7 Fish and zooplankton interaction in the Central Baltic Sea 585 Percentage (%) of adult Temora under m Relative values of sprat recruitment and total biomass Figure 8. Percentage of adult Temora longicornis at depth < m in daytime in summer and relative values of sprat 0-group abundance and total stock biomass., Temora;, sprat recruitment;, sprat total biomass. correlations were found. Table 2 displays the CCA correlation coefficients between zooplankton in spring as predictand and total stock biomass of pelagic fishes which is used as predictor. In addition, the 99% and 95% confidence levels, the correlation coefficient of cross validation, the model skill and the time lag in years are given. In general, the CCA correlation is a positive quantity by definition. A negative sign in this case marks a negative CCA pattern in the predictand. The majority of the correlations do not seem to be statistical significant or biologically reasonable. A few apparently reasonable links have, however, been found (Table 2, Figures 2 5). Figure 2 shows the Acartia spp. anomaly in spring predicted from the total stock biomass (TSB) of sprat, Figures 3 and 4 the Pseudocalanus elongatus anomaly in spring predicted from the TSB of herring and sprat, respectively, and Figure 5 the Cladocera anomaly in summer predicted from the TSB of sprat. In the last prediction a time lag of one year exists. Although the CCA correlation between the Acartia spp. anomaly in spring and the TSB of sprat is significant with respect to the 95% confidence level only, the correlation of the cross validation and the skill are relatively high. The peaks and the trend are well predicted (Figure 2). Both CCA patterns are positive which means that a positive anomaly in Acartia spp. is related to a positive anomaly in TSB of sprat. The same is valid for the prediction of Pseudocalanus elongatus anomaly from TSB anomaly of herring. The correlations of the cross validation and the skill are relatively low. The result is that only the trend is predicted well but not the amplitudes of anomaly (Figure 3). In contrast, the prediction of Pseudocalanus elongatus from TSB of sprat shows a negative pattern in Pseudocalanus elongatus. In this case the amplitudes and the trend are predicted well (Figure 4) and the CCA correlation is significant with respect to the 99% confidence level. The prediction of Cladocera anomaly in summer from the TSB of sprat shows a similar behaviour. A negative pattern in Cladocera anomaly, a high significance (Table 2), and a relatively high correlation of the cross validation and a high skill. Except for the extreme negative anomaly in 1977, the trends and the amplitudes are in good agreement between model and observation (Figure 5). In a further analysis, several statistically significant correlations were found between biomass and 0-group abundance of herring and sprat as predictands and vertical distribution of adults of Acartia spp. and T. longicornis in summer and proportion of adults in the stocks of Acartia spp. and T. longicornis in summer and autumn (Table 4). Discussion The two positive correlations in spring between biomass of sprat stock and biomass of Acartia spp. (Figure 2) and between biomass of herring stock and biomass of Pseudocalanus elongatus (Figure 3) are an indication of similarities in the dynamics of these parameters. The

8 586 G. Kornilovs et al. Percentage (%) of adult Acartia under m Relative values of sprat recruitment and total biomass Figure 9. Percentage of adult Acartia spp. at depth < m in daytime in summer and relative values of sprat 0-group abundance and total stock biomass., Acartia;, sprat recruitment;, sprat total biomass. share of Acartia spp. is low in sprat diet in spring (Kostrichkina et al., 1980), while herring feeding activity is low due to spawning. The negative correlation between sprat biomass and P. elongatus biomass in spring seems to be reasonable because this zooplankton species is an important food item for sprat in the spring period (Figure 4) (Kostrichkina et al., 1980). The negative correlation between sprat biomass and biomass of cladocerans in summer, with a lag of one year, indicates that sprat biomass is controlling the biomass of cladocerans in the next year (Figure 5). It is known that during winter, species of cladocerans disappear from the zooplankton. Development in spring starts from the resting eggs produced in the late summer and autumn of the previous year (Viitasalo and Katajisto, 1994). Whether increased predation on specimens producing these resting eggs can significantly influence the rebuilding of the stock in the next year has not been investigated. It is worth mentioning, however, that in the 1990s the abundance in the Central Baltic Sea of the dominant cladocera species Bosmina longispina was low in the majority of the years although decreased salinity and high water temperature in summer are considered to be favourable for this species (Kostrichkina, 1984; Viitasalo et al., 1990). The positive correlations between biomass and 0-group abundance of sprat and biomass of Centropages hamatus with lags of 1 and 2 years seem to have no biological explanation. Although the skills are very high and the CCA correlations are highly significant, we believe these correlations to be statistical artefacts to which no scientific importance should be attached. Besides, C. hamatus have never been an important food item of sprat in the Central Baltic Sea. The correlation analysis between 0-group abundance and total stock biomass of sprat and the biomass of cladocerans species showed that the majority of correlations were negative (Table 3). Due to the low values of cross validation and skill these correlations were rejected from multivariate regression analysis as not significant. However, this could be considered as a sign of predation pressure of pelagic fish on cladocerans species and predation could be a cause for rapid decline of cladocerans abundance in autumn (Hansson et al., 1990). The analysis of the stock structure of Acartia spp. and T. longicornis and their vertical migration has shown significant relationships of these parameters with the biomass and 0-group abundance of sprat (Table 4). The percentage of the adult T. longicornis in the stock in summer has negative correlations, both with sprat 0-group abundance and stock biomass (Table 4, row 1). Similar relationships were found for Acartia spp. (Table 4, row 2). For Acartia spp. these relationships are also meaningful in autumn although smaller than in summer (Table 4, row 3), while for T. longicornis in autumn, no significant correlations were found. This is evidently connected with the decline of feeding activity in autumn (October). Sprat 0-group abundance and stock biomass also influence the vertical distribution of these species. Thus, the respective correlation coefficients for T. longicornis and for Acartia spp. were given in Table 4. The

9 Fish and zooplankton interaction in the Central Baltic Sea 587 increase of sprat stock biomass and rich year classes result in decrease of adults in the stocks of Acartia spp. and T. longicornis. To avoid increased predation the adult specimen of both species strengthen the descent to deeper layers (> m) during the daytime. It is interesting to note that a high correlation between proportions of nauplii in stocks of both species in summer was obtained (r=0.84). This indicates that both species have similar reproductive timing and requirements in summer. The results correspond to previous studies on vertical migrations of these species. It is known that T. longicornis performs more distinct vertical migrations in comparison to Acartia spp. (Sidrevics, 1979; Hansson et al., 1990; Viitasalo et al., 1990). Also in our studies the correlation coefficients for vertical migrations were higher for T. longicornis. Consequently, the adult specimens of T. longicornis are less affected by the predation than those of Acartia spp. (Table 4). The fact that no significant correlations between stock biomass of pelagic fishes and biomass of zooplankton in summer and autumn were found, might indicate that these relationships are masked by the influence of other factors determining the biomass of zooplankton species. It is clear, however, that in the 1990s the increased stock of sprat and a sequence of strong year classes has influenced the stock structure and behaviour of Acartia spp. and Temora longicornis (Figures 6 9). Whether it has a substantial effect on the total biomass of these copepods is not clear. Acknowledgements The authors are indebted to Christoph Zülicke for graphic support. We want to thank John Hislop for his engaging review and helpful comments. This paper is a contribution to the BASYS project (EU funding through the MAST program, contract MAS ). References Arrhenius, F Diet composition and food selectivity of 0-group herring (Clupea harengus L.) and sprat (Sprattus sprattus L.) in the northern Baltic Sea. ICES Journal of Marine Science, 53: Arrhenius, F., and Hansson, S Food consumption of larval, young and adult herring and sprat in the Baltic Sea. Marine Ecology Progress Series, 96: Davidyuka, A Herring and sprat feeding in 1994 and 1995 in the eastern Baltic. ICES CM 1996/J: 24. Dippner, J. W., Kornilovs, G., and Sidrevics, L Longterm variability of zooplankton in the Central Baltic Sea. Journal of Marine Systems, 25: Dippner, J. W., Hänninen, J., Kuosa, H., and Vuorinen, I The influence of climate variability on zooplankton abundance in the northern Baltic Archipelago Sea (SW Finland). ICES Journal of Marine Science, 58: Flinkman, J., Vuorinen, I., and Aro, E Planktivorous Baltic herring (Clupea harengus) prey selectively on reproducing copepods and cladocerans. Canadian Journal of Fisheries and Aquatic Science, 49: Flinkman, J., Aro, E., Vuorinen, I., and Viitasalo, M Changes in northern Baltic zooplankton and herring nutrition from 1980s to 1990s. Top- down and bottom-up processes at work. Marine Ecology Progress Series, 165: Hansson, S., Larsson, U., and Johannson, S Selective predation by herring and mysids, and zooplankton community structure in a Baltic Sea coastal area. Journal of Plankton Research, 12(5): Hernroth, L. (ed.) Recommendations on methods for marine biological studies in the Baltic Sea. Mesozooplankton biomass assessment. Baltic Marine Biologists, 10: ICES Report of the Baltic Fisheries Assessment Working Group. ICES CM 1998/Assess: 13. ICES Report of the Baltic Fisheries Assessment Working Group. ICES CM 1999/ACFM: 16. Kornilovs, G., Berzinsh, V., and Sidrevics, L The analysis of mean weight-at-age changes of Baltic herring in the Gulf of Riga. ICES CM 1992/J: 24. Kostrichkina, E The dynamics of zooplankton abundance and biomass in the south-eastern, eastern and northeastern Baltic. Articles on biological productivity of the Baltic Sea. Moscow, 2: (in Russian). Kostrichkina, E., Starodub, M., and Davidjuk, A To the question on herring and sprat growing period in the eastern and south-eastern Baltic. In Fisheries investigation of Balt- NIIRH. 15, pp (in Russian). Livezey, R. E The evaluation of forecast. In Analysis of climate variability, pp Ed. by H. Von Storch, and A. Navarra. Springer Verlag, Berlin. 331 pp. Lumberg, A., and Ojaveer, E On the environment and zooplankton dynamics in the Gulf of Finland in Proceedings of the Estonian Academy of Science, Ecology, 1: Matthäus, W., and Schinke, H Mean Atmospheric Circulation Patterns Associated with Major Baltic Inflows. Deutsche Hydrographische Zeitschrift, 46: Michaelsen, J Cross-validation in statistical climate forecast models. Journal of Climate and Applied Meteorology, 26: Naglis, A., and Sidrevics, L The analysis of mean weight-at-age changes of Baltic herring in the eastern Baltic Proper, SD 28. ICES CM 1993/J: 24. Plikshs, M., Kalejs, M., and Grauman, G The influence of environmental conditions and spawning stock size on the year-class strength of the eastern Baltic cod. ICES CM 1993/J: 22. Rudstam, L. G., Aneer, G., and Hilden, M Top-down control in the pelagic Baltic ecosystem. Dana, 10: Sidrevics, L Some peculiarities of vertical distribution of zooplankton in the Central Baltic. Fisheries investigations in the basin of the Baltic Sea. Riga, Zvaigzne, 14: (in Russian). Sparholt, H. (ed.) Growth changes of herring in the Baltic. TemaNord, 532, 120 pp. UNESCO Zooplankton sampling. Monographs on oceanographic methodology Vol. 2. UNESCO Press, New York. 174 pp. Viitasalo, M., and Katajisto, T Mesozooplankton resting eggs in the Baltic Sea: identification and vertical distribution in laminated and mixed sediments. Marine Biology, 120: Viitasalo, M., Vuorinen, I., and Ranta, S Changes in crustacean mesozooplankton and some environmental parameters in the Archipelago Sea (northern Baltic) in Ophelia, 31:

10 588 G. Kornilovs et al. von Storch, H Spatial patterns: EOF and CCAs. In Analysis of climate variability, pp Ed. by H. Von Storch, and A. Navarra. Springer Verlag, Berlin. 331 pp. von Storch, H., Zorita, E., and Cubasch, U Downscaling of global climate change estimates to regional scales: An application to Iberian rainfall in wintertime. Journal of Climate, 6: Vuorinen, I., Hänninen, M., Viitasalo, M., Helminen, U., and Kuosa, H Proportion of copepod biomass declines with decreasing salinity in the Baltic Sea. ICES Journal of Marine Science, 55: