Influence of temperature variability on recruitment of cod in the Barents Sea

Transcription

1 ICES mar. Sei. Symp., 198: Influence of temperature variability on recruitment of cod in the Barents Sea Geir Ottersen, Harald Loeng, and Askjell Raknes Ottersen, G., Loeng, H., and Raknes, A Influence of temperature variability on recruitment of cod in the Barents Sea. - ICES mar. Sei. Symp., 198: During the past fifteen years several studies have examined the influence of temperature variability on the recruitment of Arcto-Norwegian cod. The overall conclusion has been that year classes of high abundance occurred mainly during years with high temperature, while poor year classes occurred mainly during cold years. The present article quantifies the difference between year classes produced in warm and cold years. Separate calculations based on two different abundance indices from the international 0-Group Surveys in the Barents Sea for the years 1965 (1966) to 1992 show that the average production of Arcto-Norwegian cod larvae in warm years was about three times higher than in cold years. Similar calculations were done for haddock and for both species based on the virtual population analysis at age 3. The difference in both 0- group and virtual population analysis recruitment strength between warmer and colder years is statistically significant for cod and haddock for the 1965 to 1992 period. Our results show that the influence of temperature on cod recruitment has been stronger in the last 25 years than in the previous decades. In our opinion this added sensibility to environmental fluctuations is connected with the decline in size of the spawning stock and the change in its age composition. Geir Ottersen, Harald Loeng, and Askjell Raknes: Institute o f Marine Research, Department o f Marine Resources, PO Box 1870 Nordnes, N-5024 Bergen, Norway. Introduction The Barents Sea is the feeding and nursery area for such large commercially important fish stocks as Arcto- Norwegian cod (Gadus morhua), haddock (Melanogrammus aeglefinus), herring (Clupea harengus), and capelin (Mallotus villosus) and management of the resources presupposes a broad and thorough knowledge of the biological system and environmental conditions. Heiland-Hansen and Nansen (1909) stressed the importance of studying how fish are influenced by variations in the physical conditions of the sea. An important factor in the management of fish stocks is an understanding of the recruitment mechanisms. Many investigations have dealt with cod recruitment. The year-class strength of cod is mainly determined during the first six months of life (Sundby et al., 1989), and there are several factors that may be responsible for the large variations observed, e.g., fecundity and egg quality (Kjesbu etal., 1991,1992), starvation at the start of exogenous feeding (Hjort, 1914, Wiborg, 1957; Kislyakov, 1961; Ellertsen et al., 1984), predation on eggs and larvae (Murphy, 1961; Meile and Ellertsen, 1984), and physical factors acting directly on egg and larval populations (Garrod and Colebrook, 1978; Koslow, 1984; Sinclair et al., 1985). At the active feeding stages of larvae, the variable contact rate between larvae and prey, induced by wind-mixing in the surface layer, may also be an important regulatory mechanism in the formation of year-class strength (Rothschild and Osborn, 1988; Sundby and Fossum, 1990). The relation between temperature conditions and the year-class variations of Arcto-Norwegian cod was examined by Sætersdal and Loeng (1987), who concluded that most of the year classes of high and medium abundance are associated with positive temperature anomalies in the early part of a warm period in the Barents Sea. The effect of temperature at the spawning ground on year-class strength has been studied by Ellertsen et al. (1987), who concluded that high temperature on the spawning ground is a necessary but not sufficient condition for the production of year classes of high abundance. The present paper quantifies the effect of temperature conditions on the year-class strength - primarily of cod, secondarily of haddock and herring. In doing so, ratios between average values of recruitment indices within warmer and colder years are calculated. This is done for the 0-group back to 1965 and for the three-year-old fish

2 472 G. Ottersen, H. Loeng, and A. Raknes ICES mar. Sei. Symp., 198 (1994) back to the 1943 year class for cod and 1947 year class for haddock. In order to differentiate between large-scale and more local variations, correlations are calculated between several time series of temperature as well as between the temperature series and recruitment series. The connection between 0-group abundance and modelled inflow of Atlantic water to the Barents Sea is also examined. Material and methods Indexes of 0-group abundance have been worked out from material gathered during the International Barents Sea 0-Group Surveys, which have taken place in late August to early September since We use the indexes for Arcto-Norwegian cod, haddock, and herring as published in the latest survey report (ICES, 1992a). There are two different indices for estimating the 0- group abundance. The area index is equal to the area of sparse occurrences plus 10 times the area of dense occurrences, while the logarithmic index is found by working out a logarithmic mean of the catches per nautical mile in a predefined stratification of the whole survey area. The methods of calculation of the indices are given in Haug and Nakken (1977) and Randa (1982, 1984). A series on year-class abundance at age 3 is based on results of virtual population analysis (VPA). This series covers the 1943 to 1991 year classes for cod and the 1947 to 1991 year classes for haddock. The data are taken from the reports of the Arctic Fisheries Working Group (ICES, 1982, 1992b), except for the last three years which have kindly been provided by T. Jakobsen (pers. comm.). Calculations of the number of fish in the spawning stock and the total biomass of the spawning stock for 1966 to 1992 are also based on the same reports. A description of VPA can be found in ICES (1965). Sætersdal and Loeng (1987) studied the temperature variations in the Barents Sea and classified the 1900 to 1983 years as periods of cold, medium, or warm climatic regime. From 1902 they also characterized the year-class abundance of cod as low, medium, or high. Both series have been prolonged to The average recruitment to the 0-group within each climatic period from 1965 has been estimated for cod, haddock, and herring. Within the same climatic periods, average survival indexes for cod have been calculated. These are defined as and _ 1.0 x 107 x I,og SSN _ 1.0 x 107 x Ilog0 ssb CCD where I Og0 is the logarithmic 0-group index, SSN is the spawning-stock number, and SSB the spawning-stock biomass. To study the variation over a longer time span, the average recruitment at age 3 within climatic periods from 1943 (1947 for haddock) to 1991 is also calculated. This is done by using the VPA estimated recruitment and the climatic regime in the year of spawning. We use time series of temperature from standard sections for the 0-group period (ICES, 1992a). The Kola and Cape Kanin (inner and outer) sections are shown in Figure 1. For the Kola section, in addition to the values from the period of the 0-group surveys (0-50 m interval), we have used annual averages for m from 1921 to 1992 (Bochkov, 1982). The data from recent years have been provided by PINRO, Murmansk. New temperature series for the period 1965 to 1992 are constructed by interpolating station data from the uppermost 60 m to yearly fields covering the Barents Sea. To secure a reasonable spatial coverage, stations for the period 15 August to 15 October are used. The grid resolution is 20 by 20 km horizontally and 5 m in the vertical. The interpolation scheme uses a combination of Laplace and spline fitting at each depth level and fills in linearly in the vertical (Ottersen, 1991). For further analysis we used time series of the arithmetic temperature mean at the 10 m and 60 m levels within the central 0-group area. In Figure 1 this area is seen together with an example of the hydrographic data coverage by Norwegian vessels for 16 August to 11 October A proper statistical analysis of time series is complicated. Methods such as correlation analysis, f-tests, ANOVA, and linear regression all put assumptions on the data which are seldom met by time series. For nonparametric methods too, the true significance level of the test may deviate from the intended nominal level if the series in question are autocorrelated or nonstationary. For parts of our analysis we have chosen the first difference of the natural log transform of the recruitment series. The first difference of the temperature series is also used. This was done to get normally distributed, stationary series without autocorrelation. The appropriateness of such transformations is discussed later. The transformations do not, however, automatically yield series with the desirable properties. This necessitates an analysis of each series in question. For detecting statistically significant autocorrelations we have studied plots of the estimated autocorrelation function and calculated the Durbin-Watson test statistic. For testing normality, the Shapiro-Wilk statistic, W. was used. The null hypothesis here is that the data are a random sample from a normal distribution. Stationarity was examined by means of the unit-root hypothesis test, which has the null hypothesis that the series is nonstationary. To determine the significance in difference of recruitment between warm and cold years we applied

3 ICES mar. Sei. Symp., 198 (1994) Influence o f temperature variability 473 SPITSBERGEN RUSSIA Figure 1. Locations of sections and stations. series from Kola (1), Cape Kanin, inner (2) and Cape Kanin, outer (3), flux through the Fugløya-Bjørnøya section (4) and surface air pressure at Bjørnøya (B) are used. The dots exemplify hydrographic data coverage from 16 August to 11 October Stations within the enclosed area, used for the integrated series, are observed mainly during the 0-group period. the non-parametric Wilcoxon and median tests. These tests are likewise employed to find the difference in temperature between years with high and low 0-group abundance. For the analyses above and other computations we have used the SAS statistical package (SAS Institute, 1988a, 1988b, 1992). Results Statistical properties for six recruitment series are summarized in Table 1. The non-transformed series are positively autocorrelated and non-normal. Four of the series are also non-stationary. Taking the natural log transformation reduces the problem of non-stationarity and normalizes some of the series, but they are still significantly autocorrelated. The first order differenced series, however, have the properties desired. Average recruitment within climatic periods is tabulated in Table 2 for cod, haddock, and herring. Figure 2 shows histograms for the 0-group cod and haddock and Figure 3 for the year-class strength of cod at age 3 from the VPA. The variability between the different periods is shown to be large for all the series. Since 1965 a distinct feature has been a higher recruitment in warmer than in colder periods. The fluctuations in the cod and haddock recruitment seem to be similar throughout the years studied. This impression is supported by the correlations calculated. The correlation between the log transformed logarithmic 0-group indices for cod and haddock for the period was found to be 0.83 and that between their first-order differences For the log-transformed VPA recruitment series the correlation between cod and haddock for was 0.73 and for the differenced series All correlations are significantly different from 0 at the 1% level. The average recruitment to the 0-group and 3-group within all warm years and all cold years has been worked out excluding medium temperature years. These results are given in Table 3 together with the significance levels from the Wilcoxon and median tests used to determine whether the difference in average year-class strength is statistically significant. The null hypothesis is no difference, the alternative is a higher value in warmer years. The recruitment in warmer years for both 0-group series of cod and haddock, as well as the VPA series of cod from 1965, is times as high as in colder years. As the herring stock was nearly depleted during the late 1960s and throughout the 1970s only data from the last 10 years have been used for this calculation. The picture is the same as for cod and haddock, only clearer. The recruitment is substantially higher in warmer years; for this series six times as high. For all the series, with the possible exception of the logarithmic 0-group index for haddock, the differences are statistically significant. For

4 474 G. Ottersen, H. Loeng, and A. Raknes ICES mar. Sei. Symp., 198 (1994) Table 1. Statistical properties of the recruitment series, X(t), the natural log transform, In (X(t)), and the first order difference of the log-transformed series, V In (X(t)). The autocorrelation of a series may be positive (POS.), negative (NEG.), not significant (NO) or the test may be indecisive (IND.). A series is regarded stationary if the test performed is significant at the 5% level and normal if the test is not significant at the 5% level. X(t) ln (X(t)) V In (X(t)) c o 3 ~ ra - a S 3 c e c 2P 3 a 0 M ) o p S 8 ' 5 = c - ^ S eries cö C /5 ca s c/5 C Z c/i C /5 n t/5 Z w a 0-Group cod POS. NO NO POS. NO YES NO YES YES Logarithmic index p = p = Group cod POS. NO NO POS. YES NO NEG. YES YES Area index p = p = Group haddock IND. NO NO IND. NO NO NO YES YES Logarithmic index p = p = Group haddock POS. NO NO POS. YES NO NEG. YES NO Area index p = p = 0.01 VPA 3-year index POS. YES NO POS. YES YES NO YES NO cod p = 0.00 p = 0.10 p = 0.03 VPA 3-year index POS. YES NO POS. YES YES NO YES YES haddock p = 0.00 p = 0.14 p = 0.81 Table 2. Average recruitment for cod, haddock, and herring within periods of warm (W), medium (M), or cold (C) climatic regime as categorized by Sætersdal and Loeng (1987). The survival indexes, Issn and Issb, are defined in the text. Periods where the herring stock was nearly depleted are indicated by (*). The latest period for the VPA series is indicated by (x). Period category 0-Group cod logarithmic index 0-Group cod area index 0-Group haddock logarithmic index 0-Group haddock area index 0-Group herring logarithmic index VPA cod recruitment at age 3 VPA haddock recruitment at age 3 C X o _c *c3 > 3 00 > X at -a ~cz > > 3 cn W M W C w c M C (*) w (*) c (*) w c w (x) 209(x)

5 ICES mar. Sei. Symp (1994) Influence o f temperature variability CLIMATIC PERIOD Figure 2. Average recruitment within climatic periods for 0-group cod, area index (a), logarithmic index (b) and haddock, logarithmic index (c). The double-hatched bars show warm periods, the open bars cold periods : ^ 2! > S o v i s i n m > «g j \ o p - f s > «o o o \ «TTTT V)*/} Ifilfl <T)VO VO VO VO«t'-t-' t^oo o o a v 0 \ 0 \ OvOv 0 \ 0 \ 0 \0 \ C \ C \ Os OV 2 2 OvOv OvOV OvCTv OVOV o so v CLIMATIC PERIOD Figure 3. Average recruitment of cod at age 3 in millions from VPA within climatic periods from 1943 to The doublehatched bars show warm periods, the single-hatched bars periods of medium temperature, and the open bars cold periods.

6 476 G. Ottersen, H. Loeng, and A. Raknes ICESmar. Sei. Symp., 198(1994) Table 3. Average abundance within warm and cold years for different recruitment series, ratios between the averages, and probability levels of tests for difference in recruitment between the climatic regimes. The tests are applied to the natural logtransformed series. VPA in millions. The survival indices, lssn and Issb, arc defined in the text. The asterisks indicate that the test was not performed as the herring series used covered only 10 years. Species Index Period Average index warm years (n) Average index cold years (n) Ratio warm/ cold p-value Wilcoxon test p-value median test Cod 0-Group log (13) 0.47 (14) Cod 0-Group area (13) 145(15) Haddock 0-Group log (13) 0.18(14) Haddock 0-Group area (13) 69(15) Herring 0-Group log (6) 0.14(4) 6.0 * * Cod lssn (12) 80(14) Cod Issb (12) 22(14) Cod VPA 3-year (24) 430(19) Cod VPA 3-year (12) 280(15) Haddock VPA 3-year (22) 140(19) Haddock VPA 3-year (12) 116(15) Table 4. Correlation among five different temperature series during early autumn and with the yearly mean temperature from the m interval of the Kola section ( period, above the diagonal), among the first-order differences of the temperature series (below the diagonal), and between the temperature series and the log-transformed logarithmic 0-group indices of cod and haddock ( period, to the right of the thick vertical line). Correlations given in parentheses are not significantly different from zero at the 10% level. Cape Kanin inner part Cape Kanin outer part area average 10 m level area average 60 m level Kola section 0-50 m Yearly mean temperature Kola sec m Cod logarithmic ind. Haddock logarithmic ind. C. Kanin inner C. Kanin outer area av. 10 m (0.17) (0.21) (0.31) (0.20) (0.09) area av. 60 m (0.15) Kola sec m Yearly mean temperature Kola sec m (0.02) (0.30) (0.22)

7 ICES mar. Sei. Symp (1994) Influence o f temperature variability A ll O 40 O w i Figure 4. during early autumn in the Barents Sea for the period : (a) Kola section (0-50 m), (b) integrated at the 60 m level in the central area of 0-group cod, (c) Cape Kanin section, outer part (0-bottom), and (d) Cape Kanin section, inner part (0-bottom). The stippled lines represent average values of the series. the full VPA series the ratios are reduced to about 1.5. The ratios of the survival indices, Issn and Issb, between warmer and colder periods are shown in Table 3 to be lower than the corresponding 0-group ratios for the same period. This indicates that some of the recruitment variation can be explained by fluctuations in the spawning stock. Figure 4 shows early autumn temperature during The series all have a warm period during the first half of the 1970s, an overall minimum in 1979 and especially warm years in 1983 and The correlations among these series as well as with the temperature integrated from the early autumn stations at the 10 m level and the yearly mean temperature from the m interval of the Kola section are given in Table 4. While there are no problems with significant autocorrelation for the first five temperature series in the table, the Kola m series is autocorrelated. For this series, and for the integrated series at the 10 m and 60 m level, we cannot reject non-stationarity at the 10% level. The results given in Table 4 seem to support the visual impression of distinct similarities in the temperature fluctuations of the Kola and Cape Kanin sections, as well as in the integrated series from the 60 m level. The 10 m level integrated series stands out not just by having no statistically significant correlation with three of the other temperature series, but also by the lack of significant correlation with the recruitment series. To determine whether the temperature on average is higher in years classified as having high or medium 0- group abundance than in years with low 0-group abundance, the Wilcoxon and median tests were applied to the six series given in Table 4. Four of the series showed a statistically significant difference: temperature interpolated at the 60 m level (p Wilcoxon = 0.00, p median = 0.02), yearly mean temperature from the Kola section m level (p Wilcoxon = 0.00, p median = 0.02), temperature from the outer-most part of the Cape Kanin section (p Wilcoxon = 0.01, p median = 0.02), and temperature from the upper 50 m of the Kola section (p Wilcoxon = 0.04, p median = 0.04). The temperature interpolated at the 10 m level had no statistically significant difference (p Wilcoxon = 0.40, p median = 0.44). Using the abundance categorization as above and the

8 478 G. Ottersen, H. Loeng, and A. Raknes ICES mar. Sei. Symp., 198 (1994) Table 5. Modelled wind-driven flux through the Fugløya-Bjørnøya section in June, the average air pressure at Bjørnøya in June and the year-class strength of 0-group cod for the period. The flux is categorized as strong into the Barents Sea (IN), in, weak (w), out or strong out (OUT). The flow is strong if it is larger than 0.5 Sverdrup, weak if it is less than 0.1 Sverdrup. The air pressure is classified as being lower than average (L), average (A), or higher than average (H). The pressure is average if it differs by less than 1.5 standard errors from the mean for this period. Year Flux June Air pressure June 0-Group index Year Flux June Air pressure June 0-Group index 1970 IN L H 1980 out A L 1971 OUT H L 1981 IN L L 1972 OUT H L 1982 w H L 1973 in L M 1983 in L H 1974 out H L 1984 in L M 1975 in L M 1985 out H H 1976 out H L 1986 IN L M 1977 in L L 1987 out H L 1978 OUT A L 1988 w A L 1979 w L L 1989 w L L yearly mean temperature from the m interval of the Kola section back to 1921, the same tests were applied again. For the period from 1921 to 1992 both the Wilcoxon test and the median test gave a statistically significant difference in temperature between years with high, years with medium, and years with low year-class abundance of cod (p = 0.02 for both tests). However, there was no significant difference for the period. For these years the p-values were 0.67 and 0.74 respectively. This indicates a lesser influence of temperature before In Table 5 the relationship between two other time series and the year-class strength of 0-group cod is summarized. Wind-driven flux through the Fugløya- Bjørnøya section has been modelled for 1970 to 1989 (Ådlandsvik and Loeng, 1991; Loeng etal., 1992), while monthly averages of atmospheric surface pressure at Bjørnøya were provided by the Norwegian Meteorological Institute. The results indicate that the fluctuations in 0-group abundance are related to meteorological conditions and inflow of Atlantic water during early summer. For the years 1970 to 1989, below average pressure over the western Barents Sea and increased inflow during June occurred in six of the seven years with an 0- group of high or medium abundance, while of 13 years with low abundance in the same period only 1977 and 1981 had an increased inflow in June. The correlation between the June flux and air-pressure series is high (r = 0.79, p = 0.00), but this is partly explained by the atmospheric data being one of the driving forces of the model. Discussion It is important not to ignore autocorrelation and nonstationarity when dealing with statistical analysis of environmental and recruitment time series. Thompson and Page (1989) point out two major ways to proceed. One method is to allow for autocorrelation when assessing the statistical significance of the correlation between series. The alternative method is to transform the series such that the autocorrelation is removed. One can then proceed using critical levels based on the assumption of serial independence. Classical time-series analysis as described by Box and Jenkins (1970) dealing in terms of auto-regressive (AR) and moving average (MA) processes are much used in other fields. However, Shepherd et al. (1984) point to the difficulties in getting meaningful results with few data, and Hoenig and Prager (1989) say these methods are unsatisfactory when the data reflect complex dynamics and are of short duration. This is usually the case with recruitment series. In order to transform autocorrelated, non-normal, non-stationary recruitment series to a form where ordinary Mests and regression methods could be applied, Cohen et al. ( 1986) took the first difference of the natural log transformed series. This method was also adopted by Thompson and Page (1989) for detecting possible synchrony of recruitment between several cod and haddock stocks. In agreement with Hennemuth et al. (1980) they argue for log transforming recruitment series by pointing to the frequency distribution of such series being approximately lognormal. Shepherd etal. (1984) recommended such an approach as a means of reducing the effect of extreme values in the series. Chatfield (1989) and SAS Institute (1992) suggest that in general this transformation is useful for making the variance constant. In the latter, differencing a series is put forth as a means of removing a trend in addition to reducing autocorrelation. Several of our series are not normally distributed even after log-transformation. Since f-tests also are

9 ICES mar. Sei. Symp.. 1% (1994) Influence o f temperature variability 479 prone to bias because of autocorrelation, the nonparametric Wilcoxon and median tests were chosen instead. These tests are not restricted to normally distributed data sets. They are, however, also influenced by autocorrelation and non-stationarity. Applying the test to the natural log transform of the series reduces the problem of non-stationarity, as given in Table 1, but not that of autocorrelation. Even if the calculation of correlations between positively autocorrelated and non-stationary series may lead to the significance levels found being lower than the true value, these figures are still of interest and are therefore included. Caution is shown when one of the two tests gives a significant p-value and not the other. For most of our series, significance or the lack of it is so clear that even a large adjustment of the significance level will not affect the conclusion. We are well aware that correlation is a measurement of linear similarity in variation and that the influence of an environmental parameter on recruitment is seldom linear. Shepherd et a/.y(1984) state that the response to environmental factors is unlikely to be monotonie (let alone linear), except for species at the extremes of their range. When considering cod and haddock in the Barents Sea we are dealing with species at the lower end of their temperature range, so we can justify using linear methods. Classifying years as warm or cold and calculating averages may seem unnatural. However, such an approach is supported by the results of Ådlandsvik and Loeng (1991), who concluded that the climate of the Barents Sea oscillates between two states - one warm and one cold. Our results seem to show beyond reasonable doubt that variation of ocean temperature does influence the level of recruitment of Barents Sea cod. Positive temperature anomalies affect several factors, which again act favourably on the cod recruits, and it is the sum of these effects which is detected through studying temperature-recruitment relations. Nakken and Raknes ( 1987) examined the link between temperature variation and the distribution and growth of adult cod, concluding that the connection between environmental changes and properties of the cod population was far more complex than a simple length-temperature relation. Loeng and Gjøsæter (1990) in studying the relationship between temperature conditions and growth in 0-group fish in the Barents Sea, stated that temperature effects linked to the availability of food might be just as important as direct temperature effects. Rey et al. (1987) concluded that climatic variations have a pronounced effect on the development in time of the Barents Sea spring bloom, the primary production available for zooplankton being larger in warmer years. Ellertsen etal. (1989) studied the influence of temperature on the feeding conditions for fish larvae, concluding that the temperature-dependent spawning of the copepod Calanus finmarchicus may be the most likely process causing variability in cod larval survival. These results also give clear indications of temperature variations having a greater impact on fluctuations in the Arcto-Norwegian cod recruitment during the last years than in the preceding decades. While the warmer periods from 1965 and onwards all have higher average VPA year-class strength than the corresponding colder periods, no such pattern can be seen for Rather the contrary, Table 2 indicates that the average values are stable until The ratio of the number of recruits hatched in a warm year to those hatched in a cold year is 2.5 for the 1965 to 1991 period (Table 3), but only 1.6 for the 1943 to 1991 period. The test performed on the Kola section temperature series for 1921 to 1964 showed no statistically significant difference between years with low recruitment of cod and years with high or medium year-class size. One explanation for this development is the large change in the structure of the Arcto-Norwegian cod stock over the past 40 years. The fished stock has declined by approximately 75% from the early 1950s to the late 1980s (Jørgensen, 1990). The influence of temperature on recruitment seems to be more pronounced when the spawning stock is small. The variability in recruitment from a small herring stock (Table 3) supports this hypothesis. The composition of the spawning stock has also changed. From the VPA estimates for the last years (ICES, 1982,1992b), it can be seen that the spawning stock depends to a large degree on one or only a few dominating year classes. Borisov (1978) expressed concern about the rejuvenation of the spawning population. The abundance of cod older than 10 years is at present less than 1% of that in the late 1940s (Nilssen etal., 1994). Younger fish tend to have a shorter spawning period than older fish (O. Kjesbu, pers. comm.). Recruit spawners also produce eggs which have a narrower range of vertical distribution than repeat spawners, thus causing less horizontal spreading (Kjesbu et al., 1992). We believe that these changed properties of the stock cause an additional sensibility to environmental fluctuations resulting in the greater difference in recruitment between warmer and colder periods described above. The correlations between the 0-group indices of Arcto-Norwegian cod and haddock and the hydrographic series given in Table 4 show similar patterns. Also, the relatively high correlations both among the recruitment series and their first-order differences seem to reflect a close relationship between year-class strength of the two stocks, especially at the 0-group stage. Shepherd et al. (1984) found the correlation

10 480 G. Ottersen, H. Loeng, and A. Raknes ICES mar. Sei. Symp., 198 (1994) between log-transformed recruitment series of Arcto- Norwegian cod and haddock from 1962 to 1976 to be statistically significant at the 1% level. We regard this as an indication of large-scale temporal environmental variation having an influence common to the early stages of both species. Also, the similarity in ratios between average 0-group abundance within warm and cold years suggests such a common source of variation. Dragesund (1971) and Sætersdal and Loeng (1987) found several cases of favourable recruitment coinciding for cod, haddock, and herring stocks in the Barents Sea. Loeng etal. (in press) indicate that the same close relationship exists for larval growth. The favourable physical conditions in the Barents Sea seem to be related to increased heat transport by the warm Atlantic Current (Mukhina et al., 1987; Sætersdal and Loeng, 1987). The correlation structure shown in Table 4 and test results support this view. The series we expect to be the most representative of Atlantic water masses are those which explain most of the variation in the recruitment series. The 10 m area integrated temperature series is not respresentative of what the fish larvae have experienced. This temperature depends mainly on atmospheric heating during summer, while the other series better reflect the large-scale variability in the oceanographic conditions. The most representative temperature series seem to be the yearly averages from m in the Kola section and the 60 m autumn area average. The one year with high or medium 0-group cod abundance that does not fit into the pattern of increased early summer inflow is 1985, a year which was special in many ways. While the temperature was high for the first two-three months, it decreased to below normal during the following months. Even so, both 0-group indices for cod are unusually high that year, the logarithmic index is only higher for The 1985 value is five times as high as the average over the 14 cold years (including 1985) during the period. Actually, if we had considered 1985 as a warm year due to the positive anomaly of the early months, the average logarithmic index within cold years would be reduced to 0.32 and the ratio between warm and cold years would rise to 4.2 (see Table 3). Loeng (1989) pointed out that 1963 and 1985 were the only year classes since 1930 not fitting the pattern of high recruitment with a regime of increasing temperature or positive temperature anomalies. Loeng et al. (in press) found that 1985 was an outstanding year also when considering growth from the early juvenile to the 0-group stage. The cold year of 1985 had one of the highest growth rates for cod as well as haddock and herring. 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